Investment in understanding the Earth pays off twice. It enables pursuit of scientific questions that rank among the most interesting and profound of our time. It also serves society's practical need for increased prosperity and security. Over the last half-century, we have built a sophisticated network of satellites, aircraft, and ground-based remote sensing systems to provide the raw information from which we derive Earth knowledge. This network has served us well in the development of science and the provision of operational services. In the next decade, the demand for such information will grow dramatically. New remote sensing capabilities will emerge. Rapid evolution of Internet geospatial and location-based services will make communication and sharing of Earth knowledge much easier. Governments, businesses, and consumers will all benefit. But this exciting future is threatened from many directions. Risks range from technology and market uncertainties in the private sector to budget cuts and project setbacks in the public sector. The coming decade will see a dramatic confrontation between the vision of what needs to be accomplished in Earth remote sensing and the reality of our resources and commitment. The outcome will have long-term implications for both the remote sensing community and society as a whole.

We describe a new approach to unsupervised classification that automatically finds dense parts of the hyperspectral data cloud. These dense regions are identified as the cluster centers required for unsupervised classification. The approach is tested using AVIRIS hyperspectral imagery from central Texas that has spectrally well separated land covers. The algorithm is then applied to the more stressing case of separating coniferous and deciduous forests in eastern Virginia. We find that the major spectral difference is brighter reflectance in the NIR plateau for deciduous forests compared to adjacent coniferous stands. This difference is sufficient to distinguish the forest types, and is confirmed by comparison to ground truth information.

Data from small and large footprint lidar systems were used to derive basic forest attributes from old-growth Douglas fir/western hemlock dominated stands at the Gifford Pinchot National Forest in the Pacific Northwest of United Sates. The derived forest attributes include canopy height and canopy closure. The crown depth estimates were made from the large footprint dataset. The study provides the unique opportunity to compare basic forest attributes derived from small and large footprint lidar systems, and also demonstrates the significance of complimentary analysis of data from different lidar systems in providing expanded information on forest structure. Results of the analysis showed a high degree of agreement between the canopy height estimates from both lidar systems

In recent years, the impact of aquatic invasive species on biodiversity has become a major global concern. In the Sacramento-San Joaquin Delta region in the Central Valley of California, USA, dense infestations of the invasive aquatic emergent weed, water hyacinth (Eichhornia crassipes) interfere with ecosystem functioning. This silent invader constantly encroaches into waterways, eventually making them unusable by people and uninhabitable to aquatic fauna. Quantifying and mapping invasive plant species in aquatic ecosystems is important for efficient management and implementation of mitigation measures. This paper evaluates the ability of hyperspectral imagery, acquired using the HyMap sensor, for mapping water hyacinth in the Sacramento-San Joaquin Delta region. Classification was performed on sixty-four flightlines acquired over the study site using a decision tree which incorporated Spectral Angle Mapper (SAM) algorithm, absorption feature parameters in the spectral region between 0.4 and 2.5μm, and spectral endmembers. The total image dataset was 130GB. Spectral signatures of other emergent aquatic species like pennywort (Hydrocotyle ranunculoides) and water primrose (Ludwigia peploides) showed close similarity with the water hyacinth spectrum, however, the decision tree successfully discriminated water hyacinth from other emergent aquatic vegetation species. The classification algorithm showed high accuracy (κ value = 0.8) in discriminating water hyacinth.

Remote sensing of grass/herb quantity is essential for rangeland management of livestock and wildlife. Spectral indices such as NDVI, determined from red and near infrared bands are affected by variable soil and atmospheric conditions and saturate in dense vegetation. Alternatively, the wavelength of maximum slope in the red-NIR transition, termed the red edge position (REP) has potential to mitigate these effects. But the utility of the REP using air- and space-borne imagery is determined by the availability of narrow bands in the region of the red edge and the simplicity of the extraction method. Very recently, we proposed a simple technique for extracting the REP called the linear extrapolation method [Cho and Skidmore, Remote Sens. Environ., 101(2006)118.]. The purpose of this study was to evaluate the potential of the linear extrapolation method for estimating fresh grass/herb biomass and compare its performance with the four-point linear interpolation and three-point Lagrangian interpolation methods. The REPs were derived from atmospherically corrected HYMAP images collected over Majella National Park, Italy in July 2004. The predictive capabilities of various REP linear regression models were evaluated using leave-one-out cross validation and test set validation methods. For both validation methods, the linear extrapolation REP models produced higher correlations with grass/herb biomass and lower prediction errors compared with the linear interpolation and Lagrangian REP models. This study demonstrates the potential of REPs extracted by the linear extrapolation method using HYMAP data for estimating fresh grass/herb biomass.

The potential of digital image processing for weed mapping in arable crops has widely been investigated in the last decades. In grassland farming these techniques are rarely applied so far. The project presented here focuses on the automatic identification of one of the most invasive and persistent grassland weed species, the broad-leaved dock (Rumex obtusifolius L.) in complex mixtures of grass and herbs. A total of 108 RGB-images were acquired in near range from a field experiment under constant illumination conditions using a commercial digital camera. The objects of interest were separated from the background by transforming the 24 bit RGB-images into 8 bit intensities and then calculating the local homogeneity images. These images were binarised by applying a dynamic grey value threshold. Finally, morphological opening was applied to the binary images. The remaining contiguous regions were considered to be objects. In order to classify these objects into 3 different weed species, a soil and a residue class, a total of 17 object-features related to shape, color and texture of the weeds were extracted. Using MANOVA, 12 of them were identified which contribute to classification. Maximum-likelihood classification was conducted to discriminate the weed species. The total classification rate across all classes ranged from 76 % to 83 %. The classification of Rumex obtusifolius achieved detection rates between 85 % and 93 % by misclassifications below 10 %. Further, Rumex obtusifolius distribution and the density maps were generated based on classification results and transformation of image coordinates into Gauss-Krueger system. These promising results show the high potential of image analysis for weed mapping in grassland and the implementation of site-specific herbicide spraying.

Field experiments with man-made fires in a forest were conducted to verify fire warning products from satellite remote sensing techniques and to select more effective channels for producing these products. Pine branches and trunks as well as other woods were burned at a designated place in a pine-dominated forest to simulate wild forest fires when a satellite was passing over the sky. Infrared spectral irradiances, visible spectrum, brightness, and temperature were measured concurrently with satellite data at the ground using a medium and near-infrared MOMEM MR154 FT-Spectroradiometer, an infrared thermal imager, and a visible and near-infrared spectroradiometer (ASD FR). The measurements showed two emission peaks in middle infrared band that corresponded exceptionally to the combustion strength. One of the spikes at 4.17 μm reflected the CO emission peak. The other peak spanned through the wavelengths of 4.34-4.76 μm, which exhibited a much stronger response to the fire than the commonly used channel 3.5-4.0 μm for fire monitoring in remote sensing. The results suggest that the wave band 4.34-4.76 μm is probably more sensitive and more effective than the common-used channel for wild fire monitoring using satellite remote sensing techniques. However, the peak of this wavelength band drifted during the burning process, which should be taken into account in channel selection. This band is suitable to determine forest fires. Further studies are needed to use it for retrieving fire strength quantitatively.

We assessed the capability of AVIRIS and MODIS to estimate canopy water content. Hyperspectral water retrievals with AVIRIS data, EWT, were compared to in situ leaf water content and LAI measurements at a semi-arid site in southeastern Arizona. Retrievals of EWT showed good correlation with field canopy water content measurements. Statistical analysis also suggested that EWT was significant among seven different vegetation communities. Four MODIS indexes derived from band ratios using the reflectance product and were compared to retrievals of EWT with AVIRIS at both the semi-arid site and a temperate conifer forest. Good statistical agreements were found between AVIRIS EWT and all four MODIS indexes at the semi-arid site in savanna shrub communities. Slightly poorer correlations were found at the forest site where water indexes had better correlation to AVIRIS EWT than vegetation indexes. Temporal patterns of the four indexes in all semi-arid vegetation communities except creosote bush and agriculture show distinct seasonal variation and responded to precipitation at the savanna site. Three years of net ecosystem exchange (NEE) data from eddy covariance measurements at the forest site were compared to the time series of MODIS indexes. MODIS water indexes showed similar seasonal patterns to NEE that were strongest during the period of net carbon sequestration. In contrast, the time series of MODIS vegetation indexes did not yield a good relationship to NEE.

Precision agriculture requires high spectral and spatial resolution imagery for advanced analyses of crop and soil conditions to increase environmental protection and producers' sustainability. GIS models that anticipate crop responses to nutrients, water, and pesticides require high spatial detail to generate application prescription maps. While the added precision of geo-spatial interpolation to field scouting generates improved zone maps and are an improvement over field-wide applications, it is limited in detail due to expense, and lacks the high precision required for pixel level applications. Multi-spectral imagery gives the spatial detail required, but broad band indexes are not sensitive to many variables in the crop and soil environment. Hyperspectral imagery provides both the spatial detail of airborne imagery and spectral resolution for spectroscopic and narrow band analysis techniques developed over recent decades in the laboratory that will advance precise determination of water and bio-physical properties of crops and soils. For several years, we have conducted remote sensing investigations to improve cotton production through field spectrometer measurements, and plant and soil samples in commercial fields and crop trials. We have developed spectral analyses techniques for plant and soil conditions through determination of crop water status, effectiveness of pre-harvest defoliant applications, and soil characterizations. We present the most promising of these spectroscopic absorption and narrow band index techniques, and their application to airborne hyperspectral imagery in mapping the variability in crops and soils.

The exact knowledge of the spatiotemporal dynamics of crop diseases for an implementation of a site-specific fungicide application is fundamental. Remote sensing is an appropriate tool to monitor the heterogeneity of fungal diseases within agricultural sites. However, the identification of an infection at an early growth stage is essential. This study assesses the potential of multispectral remote sensing for multitemporal analyses of crop diseases. Within an experimental test site near Bonn (Germany) a 6-ha sized plot with winter wheat was created, containing crops with each possible infection stage of three different pathogens. Two multispectral QuickBird images (04/22/2005 and 06/20/2005) and a spectrally resampled HyMap image (05/28/2005) were used to analyse the spatiotemporal dynamic of infection. The data preprocessing comprised a radiometric and a precise geometric correction by using DGPS-measurements that is an important requirement for Precision Agriculture applications. Ground truth data, in particular infection severity, growth stage/height, and spectroradiometer measurements were collected. A decision tree, using mixture tuned matched filtering results and a vegetation index was applied to classify the data (infected and non-infected areas). Classification results were compared to ground truth data. The classification accuracy of the first scene was only 56.8% whereas the scene of 28 May (65.9%) and the scene of 20 June (88.6%) achieved considerably higher accuracies. The results showed that high-resolution multispectral data are generally suitable to detect in-field heterogeneities of vegetation vitality though they are only moderately suitable for early detection of stress factors.

The productivity of the vegetation layer and its consumption by cattle, goats and sheep are important topics in characterizing the ecologic conditions in the North-western Namibian rangeland. Using a mesoscale biosphere model the calculation of above ground phytomass (= biomass) and their seasonal productivity based on satellite data is of specific interest. The investigation area, Kaokoveld (north western Namibia), is characterized by a strong hydro climatic gradient with an annual precipitation range from 380mm/a in the north eastern part of the research area to 50 mm/a at the border of the Namib Desert. Small scale vegetation patterns with fractions of savannahs, woody savannahs, open and closed shrub lands and grasslands are the manifestation of this climatic gradient and the heterogeneous relief. The study area is partly used by local herders of the Himba as pasture ground for their livestock. This usage causes problems such as overgrazing and degradation of the vegetation. Together with the impact of climate change the known ecological gradient has strengthened during the last decade. With the remote sensing based regional biosphere model (RBM Kaokoveld) quantitative information about biomass changes and pasture ecology can be determined. Growth and reduction of biomass can be observed by using the theory of Monteith and Running et al. Biomass production can be derived from the combination of incoming solar radiation, NDVI, resulting from MODIS data and a biophysical conversion factor. This factor describes the ability of plants to produce net primary production (NPP). The regional biosphere model allows extracting detailed information from an area-wide biomass balance by using remote sensing. This balance describes the production as well as the consumption of biomass by cattle, game and natural decomposition. The modelling approach runs on medium temporal and spatial scale with a decadal time step and spatial resolution of 1 km. These temporal and spatial resolutions may allow in future the integration of retrospective NOAA AVHRR time series. At the moment the model uses a four years time series of MODIS data from 2000 to 2005, with biomass changes and degradation areas as results. First results of the modelling outcome shown that the influence of overgrazing and the process of temporal vegetation degradation within a yearly cycle are mostly driven by sufficient production of biomass during the raining season.

The assessment of food provisioning services of ecosystems in Three-gorge areas is helpful for better understanding the function of ecosystems in local human well-beings. In this paper, process-based models are used to assess the potential food provisioning services derived from agriculture ecosystems and grassland ecosystems, a modifying model along with normal woodlands and a set of modifying coefficients is built to assess the potential food from woodland ecosystems. A set of power regression models based on environment factors are built to estimate the potential fish production from water ecosystems. Land cover data stemmed from Landsat TM images, as well as other natural and social-economic data in 1km resolution such as temperature, precipitation, and DEM, are used to support these assessment models. It shows that the four ecosystems in Three-gorge areas can provide 85.98×10¹² calories heat, 2.49 billion kilograms protein and 823.4 million kilograms fat. Human carrying capacity model under the balance nutrition pattern is built in this paper, which results in two key findings: ecosystems in Three-gorge areas can feed 45.92 million people under wealthy living standard which is 1.53 times of the current population, and the sustainable population is from 9.69 to 36.23 million under that living standard. Multi-scale population pressure model is built to calculate the population pressure index in Three-gorge areas. The grain for green pressure index, a multivariate linear weighed model, is used to determine the spatial distribution of farmland fit for grain for green and fit for protecting.

Remote sensing is used to show the actual distribution of distinctive invasive weeds such as leafy spurge (Euphorbia esula L.), whereas landscape modeling can show the potential distribution over an area. Geographic information system data and hyperspectral imagery [NASA JPL's Airborne Visible Infrared Imaging Spectrometer (AVIRIS)] were collected for Devils Tower National Monument in northeastern Wyoming, USA. Leafy spurge was detected in the AVIRIS imagery using the Spectral Angle Mapper with a 74% overall accuracy. The areas of leafy spurge presence and absence were compared to the predictions of the Weed Invasion Susceptibility Prediction (WISP) model. Over the area of the AVIRIS imagery, about 8% of the landscape was covered by leafy spurge, whereas 23% of the landscape has the potential to be invaded. Using kappa analysis, the agreement between remote sensing and landscape modeling was 30%, which was significantly less than expected by chance, indicating model errors. Detailed analysis of individual data layers showed that only a few of the predictor variables were required. Elimination of non-significant predictor variables reduced the area predicted to be susceptible to 13%, and increased the accuracy of the predictions to 81%. Remote sensing was a powerful addition to landscape modeling because the entire landscape was used for the analysis increasing its statistical power, whereas field data collection would be limited in scope and would be more costly.

A series of locks and dams were constructed and put into operation on the Upper Mississippi River in the 1930s to facilitate commercial navigation. As a result, historical floodplain landscapes were altered. For example, islands characterized by floodplain forests experienced prolonged unfavourable hydrologic conditions and were eliminated from many areas of the river. The distribution and extent of other large river habitat types (e.g., wetlands, secondary channels) were also impacted. In addition, large areas of open water habitat were created through the impoundment of the river. Proposed management plans for the Upper Mississippi River include (1) modernization of the locks and dams to improve navigation efficiency, and (2) ecological restoration to conditions more characteristic of pre-impoundment. The purpose of the work reported here is to describe and apply a spatially explicit comprehensive aquatic systems model (SECASM). The SECASM is offered as one approach for evaluating the anticipated outcomes of alternative management and restoration actions (e.g., island creation, floodplain forest restoration, water level management). The model simulates spatial-temporal changes in the distribution and extent of five land-use types representative of the Upper Mississippi River floodplain: prairie, marsh, upland woody vegetation, surface water, and combined urban/agricultural areas. The SECASM has a spatial resolution defined by 100 x 100-meter grid elements (i.e., 1 ha) and operates using a daily time step for simulated durations up to 100 years. Transitions of habitat types within each grid element are determined by a combination of rule-based algorithms and ecological process equations. The model outputs are amenable to the production of landscape maps and the calculation of landscape metrics (e.g., lacunarity index) that usefully summarize landscape patterns. The ability of the SECASM to realistically describe alterations in Upper Mississippi River floodplain landscapes was evaluated by using Pool 5 land-use patterns reported for 1890 as an initial condition, simulating 100-y of landscape change (including impoundment), and comparing model results with reported conditions for 1989. The SECASM was subsequently used to examine several hypotheses concerning landscape impacts of impoundment, outcomes of alternative restoration actions, and the potential effects of nutrient enrichment.

Multifilter rotating shadowband radiometers are deployed in the United States, Canada, and New Zealand by the USDA (United States Department of Agriculture) UV-B (ultraviolet-B) Monitoring and Research Program to measure UV-B irradiances at seven discrete wavelengths. A synthetic model is used to construct the continuous spectral distribution, from which irradiance integrals can be performed for various purposes. The derived spectral data are posted for public use through a web accessible database. Although the synthetic model has been validated with a certain data set, few works have been seen to compare the results of the synthetic model with simulations of other widely accepted models such as TUV. Through this comparison the validation of the synthetic model can be further confirmed and alternative techniques for constructing spectral irradiances from discrete narrowband measurements can also be explored. In this study the data from the USDA UV-B Monitoring and Research Program are used to evaluate the synthetic model and to explore the capability of the TUV model for constructing continuous spectra from discrete measurements. Simulations of the TUV model are compared with discrete measurements, erythema-weighted broadband measurements, and the results of the synthetic model. Good agreements between derived results by using TUV model and the synthetic model with measurements in general further confirm the validation of the synthetic model. Generally, the spectral irradiances constructed by using synthetic model are lower than those by using the TUV model at very shorter wavelengths (<301 nm) and at the wavelengths of 315-342 nm, but are higher at other wavelengths. The ratio of erythemal doses derived by using the TUV simulation to broadband measurements varies between 0.87-1.02. Constructed erythemal doses by using the TUV simulation are closer to broadband measurements than those obtained by using the synthetic model. These results suggest that the TUV model may be a good alternative to accurately estimate continuous spectral distributions from discrete measurements.

Ultraviolet (UV) radiation is the source energy for tropospheric photolysis processes, while harmful for living organism of the earth. It is thus necessary to incorporate UV radiation for an integrated earth modeling system to predict interactions between climate, chemistry and ecosystem processed. The widely-used NCAR TUV (Tropospheric Ultraviolet and Visible) radiation model has been coupled with the state-of-the-art mesoscale CWRF (Climate extension of the Weather Research and Forecasting model) to predict the UV dependence of local climate conditions and its impacts on air quality and crop growth. The original TUV v4.2 has been significantly improved by (1) replacing the core radiation transfer solver, DISORT v1.1 with the latest v2.0beta; (2) adding a new aerosol scheme based on the Shettle (1989); (3) recoding the entire model to follow the CWRF F90 standard with dynamic memory allocation and modular design; and (4) developing a flexible interface for coupling with CWRF. Given the lack of detailed cloud information in observations, this study focuses on validation of the TUV module in a standalone mode against the USDA UV-B data under clear-sky conditions. To facilitate this, a cloud detection scheme based on Long and Ackerman (2000) is incorporated to distinguish clear versus cloudy sky conditions from the UV-B observations. The model input includes in situ measurements of the column ozone and total aerosol optical depth; TOMS retrievals of the column ozone (in case missing in situ) and climatologically surface reflectivity; and the NARR (North American Regional Analysis) meteorological conditions. The TUV results agree well with the UV-B measurements at 7 narrow spectral bands (300, 305, 311, 317, 325, 332, 368 nm).

Field experiments and laboratory tests have shown multiple effects of enhanced ultraviolet-B (UV-B) radiation on cotton growth, development, and yield. Adverse effects include development of chlorotic and necrotic patches on leaves, reductions in total leaf area, plant height, photosynthesis, and yield. However, little work has been carried out to incorporate these experimental results into a simulation model and to estimate the effects of UV-B radiation under field conditions with varied environments and management practices. This study incorporates experimental results of UV-B effects on cotton crop into a cotton simulation model, GOSSYM, which is being used widely in various applications. In this work, first modules were modified to incorporate the effects of UV-B radiation on canopy photosynthesis, leaf area expansion, and stem and branch elongation. Then, the modified model was used to test the validity of model assumptions and algorithms on independent experimental data sets. Finally, preliminary studies were performed to simulate the effects of UV-B radiation in the field conditions at Stoneville, Mississippi using 30-year (1964-1993) climate data. Simulation results agreed well with experimental measurements, proving the validation of the model. Our results suggest that cotton lint yield declined with increased UV-B radiation. The reductions were 20% when UV-B irradiance was 12 kJ m^-2 under irrigated conditions. Similar reductions in yield were predicted at lower UV-B radiation (11 kJ m^-2) under rain-fed conditions. The modified model will be useful to simulate the impacts of UV-B radiation on cotton growth and yield under current and future climatic conditions and to suggest management options to mitigate the adverse effects.

Accurate crop growth monitoring and yield forecasting are significant to food security and sustainable development of agriculture. However, regional crop growth simulation faces the difficulties in determining the spatial distribution of some model parameters and initial conditions. In this study, regional biomasses at turn-green stage of winter wheat were re-estimated by linking WOFOST model and Soil Adjusted Vegetation Index (SAVI) synthesized from remote sensing data. Moreover, we proposed a way of combining evapotranspiration derived from satellite remote sensing data to crop grow simulation model. Thus, the regional initial available soil water and irrigation at earring stage were re-initialized and re-estimated by using remote sensing data. Those methods were well applied to simulate the growth and development for winter wheat at local site. After regionalizing of weather data, crop model parameters and initial conditions, those methods were used to estimate winter wheat yields in North China during the growing season from 2001 to 2002 at the scale of 0.25 degrees. The results showed that both soil water and final winter wheat yields estimation were improved and the relative root mean square error (RRMSE) decreased from 0.63 without remote sensing data to 0.20 with remote sensing data for 32 sites. The relative errors of the aggregated yields for three provinces were -4.9%, 4.3% and 8.6%, respectively. These results illustrated that remote sensing data can be used to improve winter wheat yields simulation at regional scale.

A vegetation-soil-integrated-model (VSIM) to simulate net primary production at watershed scale was developed to explore the effect of soil water dynamic on the primary production processes in arid and semi-arid in northwest China. The model coupled a soil water dynamic module and a vegetation growth module. The former is a daily time step, multi-horizon and distributed spatial model. The later included a mechanism model of stomatal conductance based on the mechanical character of guard cell, which used to reflect both the influence of soil water potential to stomatal conductance and the stomatal control to net photosynthesis and transpiration processes at leaf scale. Scaling up to canopy and watershed scale through considered the effect of canopy structure and heterogeneity of topography. The main inputs of the model includes photosynthetic characteristics of main vegetation type, metrological data, soil texture and physical properties, and DEM. The outputs are soil water of 4 soil layers, evaporation, transpiration, runoff, net primary production and biomass of leaf, stem and root. The model was used in Zhifanggou watershed, which located in forest steppe zone and belonged to hilly area of Loess Plateau, and the model validation was tested by field observation data sets and RS data sets. In the modeling experiment, simulations show to provide good approximation with field observation data. The simulated biomass of grass and sub-shrub are better than that of arbor and shrub, and the dynamic of LAI have well coherence with the results calculated by Landsat TM data. The model could reflect the processes of precipitation-runoff at the watershed, and indicate the spatio-temporal changes of soil water content.

Japan Aerospace Exploration Agency (former NASDA) has successfully launched a new Advanced Earth Orbiting Satellite (ADEOS-II) aboard an H-2A booster on December 14, 2002. ADEOS-II is designed to monitor global climactic change through researches of the Earth's environment. GLI, which is one of five sensors, has high potential for vegetation monitoring, and it will contribute to the future satellite sensor. GLI has 23 channels in VNIR which include 380nm channel, 6 channels in SWIR, and 7 channels in MTIR. And this sensor has two kinds of spatial resolution, which are 1km and 250m. GLI 380nm channel is very unique channel, which can be sensitive for aerosol over land. GLI land higher level processing includes precise geometric correction, 16-day composite, atmospheric correction, and vegetation index (NDVI and EVI). However, GLI atmospheric correction for land is conducted for only Rayleigh scattering and Ozone absorption. Therefore, this atmospherically corrected NDVI is affected by aerosol over land. On the other hand, 380nm data has the capability of removal of aerosol over the land. The difference between TOA NDVI and the new NDVI subtracted 380nm can be a function of optical thickness of aerosol. This paper shows that the evaluation of aerosol correction over the land by using GLI 380nm reflectance.

With the rapid socio-conomic development and urbanization acceleration, obvious changes of land use have happened every year in Chongqing. The concerns on the land use dynamic monitoring and assessment by policy makers are rising. The overall objectives of this study are to monitor and examine the major determinants of land use changes of core urban districts in Chongqing. Based on the land use survey information derived from airscapes in 2005, as well as that in 1996 and the land use update data from 1997 to 2004, the land use dynamic database of core urban districts in Chongqing is built, then the land use dynamic analysis is carried out. It shows that the built up area has risen 27.6 percent and cultivated land has decreased 27.5 percent from 1996 to 2005. In this study, a system of econometric model on the changes of built up area and cultivated land has been developed, it was empirically estimated based on remote sensing data of land use and a unique set of data collected by authors on economic development and policies that have driven land use pattern changes of core urban districts in Chongqing. The results of this study indicate that economy growth, urbanization acceleration, the regional development and functional zone policy are key factors that have been driving land use changes of core urban districts in Chongqing. The paper concludes with a set of policy recommendations on harmonizing Chongqing's urban development and cultivated land protection.

Multitemporal remotely sensed data provide an accurate, economical means to analyze the changes in land cover over time. Land cover change in the region of Baiyang Lake that is the biggest freshwater lake in North China effects local eco-environment intensely. Based on the Landsat (TM) data for 1987, 1991, 1996, and 2002, and employing the maximum-likelihood method, the land cover was classified into seven types, farmland, forest land, urban land, village, water body, wetland and bare land. The overall classification accuracies averaged 86% and the Kappa coefficient is 0.75. Then the transition matrix of The LCC was obtained by overlaying land post-classification map. Between 1987 and 2002 the amount of farmland decreased from 63.9% to 58% of the total land area, wetland decreased from 4.5% to 3.3%, while forest land increased from 2.6% to 3.3%, urban land increased from 1.2% to 2.6%, village increased from 26.1% to 29.1%, water body increased from 1.3% to 3.3%, the amount of bare land was unchanged. Land cover change can not take place independently but has certain linkages with the socioeconomic factors and mutations in natural conditions. Precipitation controlled the area of water and wetland, and human practice process restricted conversions of farmland, urban land, village and forest land.

MODIS and MISR are two Earth Observing System instruments flown onboard Terra satellite. Their synergistic use could greatly benefit the broad user community by ensuring the global view of the Earth with high-quality products. A necessary condition for data fusion is radiometric calibration agreement between the two instruments. Earlier studies showed about 3% absolute radiometric difference between MISR and respective MODIS land bands in the visible and near-IR spectrum, which are also used in aerosol and cloud research. This study found a systematic bias of +(0.01-0.03) between two surface albedo products derived from MODIS and MISR L1B data using the AERONET-based Surface Reflectance Validation Network (ASRVN). The primary cause of the bias is inconsistencies in the cross-sensor calibration. To characterize MODIS-MISR calibration difference, top-of-atmosphere MODIS and MISR reflectances were regressed against each other over liquid water clouds. The empirical regression results have been adjusted for the differences in the respective MISR and MODIS spectral responses using radiative transfer simulations. The MISR-MODIS band gain differences estimated with this technique are +6.0% in the blue, +3.3% in the green, +2.7% in the red, and +0.8% in the NIR band. About 2.1%-3.6% of the difference in the blue band is due to the difference in the MODIS-MISR solar irradiance models.

The Visible and Infrared Scanner (VIRS) aboard the Tropical Rainfall Measuring Mission (TRMM), launched on 28 November 1997, has two reflected solar bands and three thermal infrared bands. The solar bands are calibrated using an onboard solar diffuser (SD) and the thermal bands are calibrated using an onboard blackbody (BB). Since launch, VIRS has provided more than eight years of on-orbit observations. The five VIRS bands have a close spectral match with corresponding Moderate Resolution Imaging Spectroradiometer (MODIS) bands. MODIS was launched on 18 December 1999 and 4 May 2002 aboard the NASA EOS Terra and Aqua spacecrafts, respectively. In this study, six years of VIRS and MODIS overlapping data are used to examine VIRS long-term calibration stability and consistency. This is particularly useful for the VIRS solar band calibration due to a lack of capability to track the on-orbit SD degradation. To reduce impacts due to scene variations, measurements from simultaneous nadir overpasses (SNOs) for VIRS and MODIS are co-located and aggregated to 30-by-30km areas for inter-comparison. Results show that the VIRS thermal bands maintain a stable calibration. For the two VIRS solar bands at 0.62μm and 1.62μm, the calibrated reflectance values gradually drift higher over the six-year period. The 0.62μm band increases at a rate of 1.1%/yr over the period, compared to an increase of 0.4%/yr for the 1.62μm band.

Remote sensing-based vegetation indices are widely used for vegetation monitoring applications. The NDVI is the most commonly used indicator for spatial and temporal vegetation dynamics. For long term or multitemporal observations, the combined use of multisensoral NDVI data is necessary. However, due to different sensor characteristics NDVIvariations occur. The sensor geometry, like viewing- and solar angle, atmospherical conditions, topography and spatial or radiometric resolution influence the data. This study contributes to another important factor, the spectral characteristics of different sensors, in particular the relative spectral response (RSR) functions. In order to analyze the NDVI variations caused by different RSR functions, the multispectral bands of Landsat 5 TM, QuickBird, Aster and SPOT 5 were simulated by the use of hyperspectral data of the airborne HyMap sensor. The observed NDVI differences showed a non-linear but systematic NDVI offset between the sensors. Results indicate that the NDVI differences decrease significantly after cross-calibration. A gradual cross-sensor calibration of NDVI taking first spectral characteristics into account is essential. Residual factors could be calibrated in a second step. Such an inter-calibration is desirable for multisensoral NDVI- analyses to ensure the comparability of achieved results.

Lone-term satellite observations, such as Advanced Very High Resolution Radiometer (AVHRR), provide an irreplaceable means in monitoring Earth system through a series of satellites. However, to be able to detect the signal related to climate change, one of the critical requirements is the consistency and stability of calibration among the satellites. Applying Simultaneous Nadir Overpass (SNOs) method (Cao et al., 2002)., we fully accessed instrument-related consistency of AVHRR measurements covering all channels (from visible to IR) and time period from 1978 to 2003. It is seen that the inter-satellite biases in visible channels (channel 1 and 2) show larger inconsistency among satellites especially between NOAA-14 and NOAA-12. The inconsistency is shown as both the large bias and trend in the biases, mostly due to the lack of onboard calibration. Comparatively, the biases in IR channels, i.e., channel 4 and 5 are generally smaller, there are within ± 1 k. However, the difference in the magnitude of the biases among satellites and the dependence of biases on the scene temperature may affect the quality of long term trend derived from such dataset. Analyses of bias root causes indicate that the effect from the difference in Spectral Response Function may not be large enough to account for the observed biases.

This work demonstrates how lunar data are used by three different imaging radiometers: the Visible and InfraRed Scanner (VIRS) onboard the Tropical Rainfall Measuring Mission (TRMM) and the MODerate resolution Imaging Spectroradiometers (MODIS) onboard the EOS Terra and Aqua satellites. Using the measured lunar data, radiometric models have been developed for the on-orbit calibration of the remote sensing systems' reflected solar bands. For the VIRS with spectral bands nearly identical to several of the MODIS bands, the integrated lunar reflectance data were measured, from Jan 1998 to Jun 2006, at phase angles ranging from 1.2° to 120.2°. For the two nearly identical MODIS instruments, the lunar irradiance was measured at phase angles from 54° to 56°. We present stability trending of the lunar data at selected phase angles over periods of four to eight years and use these observations to examine instrument radiometric stability. Moreover, we discuss implications of these results.

The NOAA-KLM (15 to 18) AVHRR are current operational sensors with observations used for many remote sensing applications. This paper examines the on-orbit calibration consistency and stability of the NOAA-15 to 18 AVHRR using MODIS aboard the NASA EOS Terra and Aqua spacecraft as transfer radiometers. Coincident and co-located observations collected from the middle of 2002 to the beginning of 2006 at orbital crossovers between AVHRR and MODIS are used for trend analysis. Uncertainties in the AVHRR and MODIS comparison data sets due to mismatch in pixel footprint, band spectral differences and BRDF effects are discussed. Possible biases among these AVHRR sensors are determined for the visible channel at 0.6 μm, the near-IR channel at 0.8 μm and the two longwave infrared channels at 11.0 and 12.0 μm. Trending results show that there is an agreement of within 2% in reflectance for the AVHRR visible channels onboard NOAA-15 to 17, and the most recently launched NOAA-18 (2005). Large differences of up to 15% in trending comparison are found for the near-IR channel, illustrating problems for this channel due to a lack of on-board calibration capabilities for AVHRR. For the longwave infrared channels, NOAA-17 has the most stable performance. NOAA-16 to 18 AVHRR agree with within 0.30 K in scene temperature estimation, while NOAA-15 AVHRR is 0.4 to 0.6 K lower than the other AVHRR.

Cross calibration of data products across generations of satellite program is indispensable to facilitate continuous data products by satellite observations. To investigate long term environmental change through vegetation monitoring, it is required to use data from different platforms, e.g., normalized difference vegetation index (NDVI) from NOAA-AVHRR series with the ones from TERRA- and AQUA-MODIS. In this context, cross calibration of spectral vegetation index (VI) is an important factor which determine the accuracy of such changes. In our previous work, we introduced a way of deriving analytical relationships between two vegetation indices based on an equation of vegetation isoline. The functional form of the relationships was found to be a ratio of polynomials. On the other hand, most of the studies that investigate relationships of NDVI products between two sensors simply assumed first- or second-order polynomial to describe the relationships of the two data products. In this paper, we discuss the relevancy of using higher-order polynomials by relating those coefficients implicitly to biophysical parameters, atmospheric properties, and soil optical properties. The order of polynomials sufficient to approximate the relationships is clarified from both analytical and numerical point of view by conducting numerical experiments in addition to analytical derivations.

Long term data records require the effective integration of new sensor technologies and improved algorithms to better characterize global and climate change impacts on ecosystems, while preserving the fundamental attributes of the existing data record. In this study, we investigated key determinants in the spectral translation and extension of MODIS Vegetation Index products across current sensor systems and to the NPOESS (VIIRS) era. We used simulated sensor-specific data sets derived from hyperspectral data using field spectroroadiometers and Hyperion sensors to investigate inter-sensor translation and continuity issues of the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). We also investigated the use of data fusion of satellite VI time series with in-situ flux tower time series measurements of photosynthesis, and the use of data fusion with tower-based continuous measures of broadband/hemispherical VI's as possible reference data sets for the inter-calibration of satellite VI time series from different sensor systems. Preliminary comparisons are presented with actual satellite VI measurements from SPOT-VEGETATION, Terra- and Aqua-MODIS, and AVHRR sensors. We found that with a consistent atmosphere correction scheme and a generalized compositing procedure, translation of multi-sensor datasets can be achieved with certain limitations.

The emissivity variation of the land surface is the most difficult effect to correct for when retrieving land surface temperature (LST) from satellite measurements. This is not only because of the emissivity inter-pixel variability, but also because each individual pixel is a combination of different surface types with different emissivies. For different illumination-observation geometries, this heterogeneity leads to different ensemble (scene) emissivities. The modified geometric project (MGP) model has been demonstrated to be able to simulate such effect when the surface structural characteristics are available. In this study, we built a lookup table to correct the surface emissivity variation effect in LST retrievals. The lookup table is calculated using the MGP model and the MODTRAN radiative transfer model. The MGP model, assumes that the land surface visible to the satellite sensor is a composite of homogeneous vegetation and soil background surface types. The homogeneous or "pure" surface types and their emissivity values are adopted from Snyder's surface type classification. Our simulation procedure was designed to calculate the emissivity directional variation for multiple scenarios with different surface types, solar-view angles, tree cover fractions, and leaf area index. Analysis of the MODTRAN simulation results indicates that an error of over 1.4 K can be observed in the retrieved LST if surface emissivity directional variability is not accounted for. Several MODIS granule data were selected to evaluate the correction method. The results are compared with the current MODIS LST products.

Adopted with Weight Centre Model(WCM) and Land Use Degree Model (LUDM), Climate data of China in recent 20 years and a 2-period Land Use/Land Cover (LUCC) data covering China are used to analyze impacts and direction of changes caused by climatic changes and human activities to China vegetation covers and land use. In the last 20 years, the dual impacts by climatic changes and economic development have led to Land Use Degree Weight Centre shift to Northeast 54km. In East-West direction, Land Use Degree Excursion Intensity is caused 81% by climatic changes and 19% by anthropogenic impacts; while in South-North direction, is caused 85% by climatic changes and 15% by anthropogenic impacts.

Snow cover is one of the sensitive indicators of global climate change. Numerous studies have shown the importance of accurate measurements of snow cover. The Moderate Resolution Imaging Spectroradiometer (MODIS) is well suited to the measurement of snow cover because snow characteristically has high reflectance in the MODIS Visible (VIS) and low reflectance in the MODIS Shortwave Infrared (SWIR) wavelengths, a characteristic that allows for snow detection by a normalized ratio of VIS and SWIR bands. The automated MODIS snow-mapping algorithm uses at-satellite reflectance in MODIS VIS band 4 (0.545-0.565 μm) and SWIR band 6 (1.628-1.652 μm) to calculate the Normalized Difference Snow Index (NDSI). Aqua MODIS band 7 (2.105-2.155 μm) instead of band 6 has been used to calculate NDSI, in response to band 6 striping problem caused by non-functional or noisy detectors. In our early study, a feasible algorithm to map Aqua MODIS band 6 based on the relationship between Terra MODIS bands 6 and 7 has been developed and validated. This algorithm has been used to retrieve Aqua MODIS band 6. Aqua MODIS NDSI values computed from Aqua MODIS observed band 6, simulated band 6, and observed band 7 are used to map snow based on current MODIS snow algorithm, respectively. Snow coverage mapped using NDSI computed from observed band 6 is regarded as a standard snow product, comparison and analysis are performed between snow mapping using NDSI computed from simulated band 6 and observed band 7. This paper will investigate the measurement continuity between Terra and Aqua MODIS snow coverage products, and propose another alternative for Aqua MODIS NDSI retrieval. Our approach for monitoring snow coverage is valuable to keep the continuity and consistency for MODIS snow products.

Research has shown that remote sensing in both the optical and microwave domain has the capability of estimating vegetation water content (VWC). Though lower in spatial resolution than MODIS optical bands, AMSR-E microwave measurements are typically less affected by clouds, water vapor, aerosol or solar illumination, making them complementary to MODIS real time measurements over regions of clouds and haze. In this study we explored a wavelet based approach for combining vegetation water content observations derived from higher spatial resolution MODIS and lower spatial resolution AMSR-E microwave measurements. Regression analysis between AMSR-E VWC and spatially aggregated MODIS NDII (Normalized Difference Infrared Index) was first used to scale MODIS NDII to MODIS VWC products. Our approach for combining information from the two sensors resorts to multiresolution wavelet decomposition of MODIS VWC into a set of detail images and a single approximation image at AMSR-E resolution. The substitution method of image fusion is then undertaken, in which the approximation image is replaced by AMSR-E VWC image, prior to using inverse wavelet transform to construct a merged VWC product. The merged VWC product thus has information from both MODIS and AMSR-E measurements. The technique is applied over low vegetation regions in Texas grasslands to obtain merged VWC products at intermediate resolutions of ~1.5km. Apart from offering a way to calibrate MODIS VWC content products to AMSR-E observations, the technique has the potential for downscaling AMSR-E VWC to higher spatial resolution over moderately cloudy or hazy regions where MODIS reflective bands become contaminated by the atmosphere. During such situations when contaminated MODIS signals cannot be used to obtain the wavelet detail images, MODIS detail images from a preceding time step is used to downscale the current AMSR-E VWC to higher resolutions. This approach of using detail images from the recent past would be justified if the detail images containing the high frequency components of the image change slowly. Correlation analysis of detail images from consecutive time steps shows that this is approximately true, at-least for the low spatial resolution detail images. Our approach yields accuracy of around 77% on the average over the selected study region and temporal period. This technique thus has the potential for ensuring the data continuity of high spatial resolution VWC products, a requirement essential for fire risk monitoring.

Detecting and quantifying crop residue cover on agricultural fields is essential in identifying conservation tillage practices and estimating carbon sequestration, both of which are important goals within the Agricultural Policy Framework of Agriculture and Agri-Food Canada. Crop residue is traditionally measured using ground survey techniques such as the line-transect method or visual (drive-by) assessment but these techniques are tedious, time-consuming and subjective. With the increased number of advanced earth observation satellites, remote sensing has now become a viable option for mapping agricultural land management practices and percent crop residue cover. A wide variety of indices such as the Normalized Difference Index (NDI) and the Modified Soil Adjusted Crop Residue Index (MSACRI) were developed using multispectral data for this purpose but results have been mixed. Advanced classification techniques including linear spectral mixture analysis (SMA) and spectral angle mapper (SAM) provide an alternative to derive percent crop residue cover. Landsat-7 SLC-Off data were acquired over an agricultural study site in Eastern Ontario on May 25 2005. Simultaneous ground data were collected to characterize residue type, position, direction and percent cover. NDI, MSACRI, SMA and SAM were all computed and used to derive percent crop residue cover information. Preliminary results indicate that the SMA model predicts percent crop residue cover over agricultural fields with the most success, especially over fields of corn residue with an R² value of 0.85 (RMSE of 12.46 and D of 0.99). However, further investigation is needed where residue models are validated against a larger dataset with greater variability in percent crop residue cover.

There are many means to monitor the Land Use and Land Cover Change (LUCC) in time. Phase difference Image, is one of these means that is often used. By using gaps images to analyze the changes, errors in result images happen easily by accident. Another disadvantage of Phase difference Image is not adequately using the time-serial images. The pixel at the same position in time-serial images will structure a sequence change of points, this sequence include information of LUCC, and it is important to describe the change process of those points for understanding the change of Land Cover. In this paper, we use Fractional Vegetation Cover (FVC) images that were derived from NOAA/AVHRR time series data from 1982 to 2000 and time (year) is the independent variable. Every pixel is an attributive variable, using the regress method to analyze the change of vegetaion cover in Westen China. Meanwhile, we discuss the results and significance of the F-test and t-test for this regression. Through the above work: 1) the result of the regression method for time series data is more stabilizing than Phase Difference Image; 2) Regression method can be used to forecast the change of vegetation and Phase difference image can not; 3) By the regression image, we can find that the increase of vegetation cover is close relationship with old oasis, and that human activities is obviously one of the most important factors contributing to the change of vegetation cover in the arid lands in Westen China. In Qaidam High Basin, there are few signs of human habitation, the vegetation cover decreased, indicating the environment has degraded in this area.

Much attention has been paid to terrain effects on planetary scale. Few works have addressed these impacts at finer scales. In this study, the terrain effect on a backflow weather occurred over North China during December 22 to 23, 2002 was investigated using the Pennsylvania State University-NCAR Fifth-Generation Mesoscale Model (MM5), version 3. Numerical simulation results show that the mountains in the western North China play a very important role in the backflow precipitation. The cold air from North East Plain was obstructed by the Taihang Mountain. The flat topography of the plain reduced the thickness of the cold air, its vertical velocity, and the intenseness of the precipitation. The results indicate that the cold air was accumulated in the windward of the mountain. This accumulation resulted in increased thickness of the cold air. Humid air could climb up to the top and amplify the precipitation.

Soil wind erosion is the primary process and the main driving force for land desertification and sand-dust storms in arid and semi-arid areas of Northern China. Many researchers have paid more attention to this issue. This paper select Inner Mongolia autonomous region as the research area, quantify the various indicators affecting the soil wind erosion, using the GIS technology to extract the spatial data, and construct the RBFN (Radial Basis Function Network) model for assessment of wind erosion hazard. After training the sample data of the different levels of wind erosion hazard, we get the parameters of the model, and then assess the wind erosion hazard. The result shows that in the Southern parts of Inner Mongolia wind erosion hazard are very severe, counties in the middle regions of Inner Mongolia vary from moderate to severe, and in eastern are slight. The comparison of the result with other researches shows that the result is in conformity with actual conditions, proving the reasonability and applicability of the RBFN model.

Land surface albedo is a key parameter in modeling radiative transfer in the atmosphere. Simulated climates are sensitive to specified albedo in models. The MODIS BRDF/Albedo Science Data Product represents the latest attempt at providing a dataset suitable for climate model comparisons. It is necessary to analyze the feature of white-sky and black-sky albedo before its use in land surface models. White-sky (diffuse) and black-sky albedo (direct at local solar noon) in China from MODIS based on Lucht algorithm are calculated and analyzed. The differences of white-sky and black-sky albedo for different land use/land cover are compared. The derived albedo exhibits clear interannual variation with large variation in Northern China. Black-sky albedo and white-sky albedo are characterized with different features for different land covers.

This paper examines the performance of an off-line version of the Community Land Model (CLM3.0) by simulating the soil properties: soil temperature, and soil wetness, in Tibetan Plateau, and the modeled results are validated with direct measurements at three filed sites. The soil properties in the model are initialized with field measurements and are driven by half-hourly observed atmospheric variables (temperature, humidity, wind speed, surface pressure and downward radiation (solar and infrared). The observation (or direct measurements) of the soil properties and atmospheric fields are collected through the Global Energy and Water Cycle Experiment (GEWEX) Asian Monsoon Experiment (GAME)-Tibet project. Results indicate the CLM is able to capture general characteristics of soil in Tibetan Plateau. The model shows sensitivity to initial soil properties, particularly soil moisture. The initial error in the soil moisture contributes largely the simulated bias in soil moisture.

Better understanding the dynamics of the East Asian monsoon system is essential to address its climate variability and predictability. Regional climate models are useful tools for this endeavor, but require a rigorous evaluation to first establish a suite of physical parameterizations that best simulate observations. To this end, the present study focuses on the CWRF (Climate extension of WRF) simulation of the 1998 summer flood over east China and its sensitivity to cumulus parameterizations on CWRF performance. The CWRF using the Kain-Fritsch and Grell-Devenyi cumulus schemes both capture the observed major characteristics of geographic distributions and daily variations of precipitation, indicating a high credibility in downscaling the monsoon. Important regional differences, however, are simulated by the two schemes. The Kain-Fritsch scheme produces the better precipitation patterns with smaller root-mean-square errors and higher temporal correlation coefficients, while overestimating the magnitude and coverage. In contrast, the Grell-Devenyi ensemble scheme, using equal weights on all closure members, overall underestimates rainfall amount, suggesting for future improvement with varying weights depending on climate regimes.

The capability of the Climate extension of the Weather Research and Forecasting (CWRF) model in simulating the 1991 and 1998 summer floods in China is evaluated with 4-month continuous integrations as driven by the NCEP/NCAR observational reanalysis. It is shown that CWRF has a pronounced downscaling skill, capturing the key characteristics in the spatial patterns and temporal evolutions of precipitation in both severe anomalous monsoon cases. The result gives a high perspective for future CWRF applications in understanding and predicting China monsoon variability.

NPP is not only the original driver of carbon cycle, but also has significance in global change research. In this study, NPP data from GLO-PEM model and Chinese plantation data were used to explore the spatial and temporal changes of NPP in Chinese plantation area from 1981 to 2000. As the results, mean annual NPP in Chinese plantation area was about 663.37gCm^-2yr^-1 in the past 20 years, with higher NPP in several provinces in South China, and lower NPP in some arid and semi-arid regions in Northeast China, North China and Northwest China. NPP increased more in the eastern part of North China and in Central China and South China, but decreased in most regions of West China, North Liaoning, East Jilin and North Heilongjiang. Monthly variation of plantation NPP was mainly in phase from June to September, especially in July and August during the 4^th times from 1996 to 2000, monthly NPP increased most. Mixed plantation had the highest mean annual NPP and coniferous plantation had the least. Plantation in East China had higher mean annual NPP, annual NPP increase rate and monthly NPP variation than that in West China. The increment of total annual NPP in Chinese plantation from 1980's to 1990's was 84.51×10⁴tCyr^-1. Plantation in Hainan province had the highest mean annual NPP and NPP increase, and plantation in Guangdong province had the largest total annual NPP increase in the past 20 years, but in Xinjiang province, mean annual NPP in plantation area was lowest and decreasing.

Desertification is a worldwide concern and the assessment of aeolian desertification has become one hotspot in global ecosystem research. In this paper, hyperspectral data acquired from modular OMIS-I imaging spectrometer, combined with ETM data and field survey data, was used to assess the aeolian desertification in Korqin Sand, Inner Mongolia, China by pixel-level. The results indicated that hyperspectral image, combined with ETM image and little field works, is capable to monitor and assess desertification through quantitative retrieval of assessing parameters directly from hyperspectral data or indirectly from the encoding map by visual interpretation of hyperspectral image and ETM image. For the retrieval of vegetation biomass and coverage, polynomial fit curve is suitable to regions where shrubs and grasses coexist, while linear fit curve is suitable to single vegetation type and was highly restricted by region. The retrieval of surface soil water content based on soil thermal inertia is suitable in flat terrain and sparse vegetation, and it can resist vegetation disturbance. The algorithms for numerical evaluation and quantitative retrieval for hyperspectral image are also practicable for aeolian desertification in Korqin Sand, China.

In the first part of this paper, a 3DVar Land Data Assimilation Scheme (LDAS) is presented. With virtue of this land data assimilation system, this part of the paper demonstrates the results and error analysis of assimilating air temperature data observed at various meteorological stations in China into the output of ECMWF ERA-40. The air temperature distribution of sparse observation zones is obtained, which shows the validity of the assimilation procedure. The 3DVar LDAS can greatly improve the ECMWF background estimates with the high quality observations of air temperature from the Chinese meteorological stations. By comparing the assimilated air temperature field and the ECMWF background field to the observations, the assimilation outputs have better agreement with the air temperature variation trend than the ECMWF background. Another advantage of the assimilated result is that it can describe the extreme air temperature more accurately.

Populus euphratica (P. euphratica), at Tarim Nature Reserve, growing in their natural habitat represents a valuable resource for elucidating mechanism of acclimation to environmental constraints. P. euphratica is a salt-tolerant tree species growing in saline semi-arid areas. It is one of the stress-tolerance and desert-grown species. Therefore, the P. euphratica has been treated as main protecting object and the Tarim Nature Reserve is one of two P. euphratica Reserves in China. The nature reserve is located in the middle reach of the Tarim River, Xinjiang. It is not only the world's largest intact and unfrequented area of Populus euphratica forests, but also plays great significant role in maintaining the ecological balance of the Tarim Basin. However, the Populus euphratica Nature Reserve's eco-environment is getting more and more degenerated due to the human activities in recent years. This paper analyzed the ecological frangible factors and their influence mechanisms on the nature reserve eco-system stability using 3S technologies. The results showed the eco-environmental condition of P. euphratica is fragile and the ability of insisting on the artificial influence is weak because of the harsh climate, topographical conditions, and human irrational water use and land resources. The shortage and exhaustion of surface water, as well as descending of groundwater depth, make Populus euphratica forests deteriorating. The protecting strategies are suggested in this paper according to the research analyses.

The surface heterogeneity of densely vegetated region is often ignored as its spatial variation doesn't shows so obvious as sparse region. This paper is to examine to which degree the estimation difference with scale change would be. The surface net radiation and related variables between six consecutive scales from 30 to 960 m over a dense grass covered region in Northern China are calculated with a simplified scheme based on Landsat ETM data. The estimation agreements between neighbouring scales are evaluated with the mean absolute percent difference and the index of agreement. The two indices indicated variation is not so obvious and can't determine whether the study area is homogeneous or not. Further analyses of the fraction variation of land covers with scales and the change of related mean variables for individual land cover with scales, reach a consistent result that the major covers with larger patches are more insensitive to scale change than the minor ones with smaller patches. The introduction of land cover information improves detecting the effect of patches with different covers when the surface net radiation is considered.

Climate yield potential is known to be under great effect of climate factors. Based on multi-yearly climate records and agrometeorological observations and by means of the scheme for calculating yield potential (YP) proposed by Huang Bingwei the authors investigate the distribution of mean climate YP for several representative stations in Henan and averaged availability of climate resources for staple crops in this province, and with Zhengzhou station as the typical station, calculated is made of annual YP of photosynthesis, light-temperature, and light - temperature- water and also YP of wheat and corn related to the above three factors. Wavelet analysis shows that on an annual basis, the photosynthesis YP has quasi-8 yearly periods, light-temperature YP gives quasi-8 and quasi-4 yearly periods, and light- temperature-water YP exhibits quasi-6 and quasi-10-yearly periods. Study is also undertaken of availability of climate resources by wheat and corn in the year and their growth season. Finally, analysis is done of effects of meteorological factors upon yields of the staple crops as well as their availability of the climate resources.

Using the 1961-2001 rainfall datasets from 25 typical stations in Fujian Province treated by means of Empirical Orthogonal Function (EOF), Fast Fourier Function (FFT), Continuous Wavelet Transform (CWT) and Orthogonal Wavelet Transform (OWT) study is made of sequences of flood/drought indices (Z index) in different seasons. Evidence suggests that 1) the regional flood/drought events have significant 2~3 year periods in 1965-1975 and the 1990s; 2) rainfall amount in the south opposite to that in the north shows pronounced 1- and 3~ 4-yearly periods after the mid 1980s; 3) quantitatively, precipitation occurs in the west to middle in an opposite way to the other parts of the province, with noticeable 1~2 yearly periods in 1985 - 1998, and more appreciable 9~13-yearly periods after the 1980s; 4) within the study period (1961-2001) the drought trend is more apparent in the south (east) than in the north (west), particularly in the 1990s; 5) the regional climate is relatively wetter (drier) in the 1960s and 1980s (1970s and 1990s).

One of the Yangtze River sources, Qumar River valley was selected as the research area. Landsat Thematic Mapper (TM) imagery were very useful in detecting and monitoring the land cover change, especially in the desolate and bad physical environment region like Tibetan Plateau. In our work TM images in 1994 and land cover map in 1980s were selected to detect and monitor the land cover change. The satellite images were calibrated, registered and georeferenced, then classified and visually interpreted. Ancillary data such as were used to assist the satellite image classification and interpretation. Seven land cover types were obtained, that is, residential area, meadow steppe, alpine meadow, alpine steppe, water surface, exposed rock and desert land. The statistical results of every class land cover type indicated that the desert expanded significantly, about 194km², a very big number. The climatic change maybe is the main reason, but the human activity, for example, digging gold in the northwest of this area, is also maybe an important affecting factor. Secondly, the meadow steppe distributed in the low reaches of Qumar River decreased significantly, its change maybe is relevant to climatic change. The variation trend of other land cover type is not significant. All of this showed the significant degeneration of ecological environment of the source of Yangtze River.

Determination the regional land surface parameters and components of surface radiation balance over heterogeneous landscape is very important and not an easy problem, in such researches, the utilization of satellite remote sensing is indispensable. In this study, a parameterization method based on Landsat-7 ETM+ data and 22 weather stations data is described for deriving the regional distributions of land surface parameters and components of surface radiation balance over the South Ningxia area. The distribution figs and straight-bar charts of the parameters and components are given out. Further more, the South Ningxia area is classified into five surface types, regional distributions are discussed according to each type. The main results indicate: All the regional distributions are characteristic by their terrain nature and the regional distributions are obvious and regular. The figures of the mountains and rivers are very clear, cause there is a great deal vegetation growing over the mountains and rivers edge. It is seen that the derived regional distributions of land surface parameters and components of surface radiation balance for the whole mesoscale area are in good accordance with the land surface status.

Droughts hazard that occurrs frequently in nature and has a great impact on agriculture. Timely monitoring and assessment of drought conditions are critical to mitigate its effects. By using NOAA/AVHRR satellite data, in current study, we derived the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI) and land surface temperature (LST), and analyzed the spatial characteristics of vegetation indexes and land surface temperature. The temperature vegetation dryness index (TVDI) was used to monitor the winter wheat drought conditions from March to May of 2005 in the middle-south part of Hebei Province, China. The results showed that SAVI was better than NDVI for representing the winter wheat growth condition in spring. The correlation of soil moisture with TVDI based on SAVI was greater than that of based on NDVI. The analysis of TVDI and soil moisture data from weather stations' measurement demonstrated that a better correlation existed between TVDI and relative humidity of soil at 10cm and 20cm. TVDI therefore can be used as a good indicator for operational drought monitoring.

Temperature vegetation dryness index (TVDI) is a simple and effective methods for drought monitoring. In this study, the statistic characteristics of MODIS-EVI and MODI-NDVI at two different times were analyzed and compared. NDVI reaches saturation in well-vegetated areas while EVI has no such a shortcoming. In current study, we used MODIS-EVI as vegetation index for TVDI. The analysis of vegetation index and land surface temperature at different latitudes and different times showed that there was a zonal distribution of land surface parameters. It is therefore necessary to calculate the TVDI with a zonal distribution. Compared with TVDI calculated for the whole region, the zonal calculation of TVDI increases the accuracy of regression equations of wet and dry edge, improves the correlations of TVDI and measured soil moisture, and the effectiveness of the large scale drought monitoring using remote sensing data.

Luxi County of Yunnan Province in China has the biggest areas of tobacco fields that belong to Chinese Red River Tobacco Company. And the areas of tobacco field in 2005 achieved more than 20,000 Chinese acres. So Luxi County is the ideal bed to identify the tobacco field by remotely sensed technology. The paper introduces SPOT5 imagery with the high spatial resolution of 5m and clear texture information and Landsat TM imagery with the medium spatial resolution of 30m and high spectrum resolution in study area. Firstly, we ortho-rectify the TM and SPOT imageries in study area, then uses the pansharp fusion method to fuse the above two Ortho-images with different spatial resolutions. Lastly, based on the spatial distribution patterns of the tobacco field with highly congregated in macro regions of continent & nation, and the small patch dispersible at the levels of the County & Town & Village, considered the tobacco spectrum characteristic and the terrain distribution characteristic, the paper introduces the altitude above sea level, the slope, the vegetation index (NDVI), the texture factor and so on to identify the tobacco fields in the fused imagery. The rate of accuracy of computer classifies by this method achieves 77.75%.

The characteristic of climate in North China is short of precipitation in winter and spring. Insufficient supply of water is a major factor affecting yield of winter wheat. The variation of yield caused by irrigation or drought at different stages is not alike. The relationship between them can be represented with water-yield reaction coefficient. Based on the experiment conducted in 2001 through 2004, yield of winter wheat has a marked positive correlation with precipitation at different stages after winter. The water-yield reaction coefficients increase with crop development, especially in turning green stage. The maximum occurs at head sprouting stage. Then it decreases slightly at milking stage. In order to raise water use efficiency of winter wheat, it is necessary to practice irrigation at elongating and head sprouting stages first and milking stage next.

From the dependence of winter wheat on water for growth a study is conducted of drought intensity and probability as well as their impacts upon the yield, whereupon are developed models for venture evaluation of drought damage to the crop grown in Henan province, with which to make the venture evaluation and regionalization of drought effects to provide scientific basis for modifying crop structure and preparing countermeasures for preventing and alleviating drought loss.

Using Landsat TM and ETM+ images in 1993 and 2004 to interpret the variation of land use and vegetation cover in Zhengzhou area, a study is conducted on the current situation and change of desertification and vegetation cover during 1993-2004, indicating that the desertification has been under control, leading to the decline of the area and most of the waste land converted into arable land, but nevertheless vegetative coverage has dropped by 0.43% in this study period, and especially the woodland has greatly decreased, responsible for lowered vegetation area and vulnerable ecological environment. Some countermeasures are proposed against desertification and for expanding vegetation coverage in an effort to form a good ecosystem in the research area.

Based on 1982-1999 satellite sensings, meteorological data and observations of crop growth/development, a study is conducted on seasonal and interannual variations in vegetation NDVI (normalized difference vegetation index), its response to climate change and its relationship with crop growth/development during the period in Zhengzhou region of Henan province. Evidence suggests that the interannual change is not so pronounced in the regional NDVI, exhibiting, on the whole, an unsteadily increasing trend; stronger seasonality is shown in the annual variation, with a more significant rising trend in spring as opposed to those for summer and autumn; NDVI change is in positive correlation both to temperature and precipitation (stronger response to temperature), with its change subject dominantly to temperature and rainfall in spring and summer, respectively, under varying effect of temperature and precipitation in autumn, and largely under the impacts of heat in winter and during the growth of winter wheat and summer-sown corn the NDVI is positively correlated to the crop height and density (the number of crop individuals per unit area) in their milking stage, the latter acting as a principal factor of NDVI change.

By means of a linear tendency estimation scheme and correlation analysis a study is undertaken of change in Zhengzhou climate and phonological response of woody plants thereto, together with relations between meteorological elements (rainfall, sunshine length and mean temperature) and phenological periods investigated. And later, using a least squares polynomial, a fit expression is constructed for the peak phase of flowering in relation to mean temperature over the previous 3 months. Results show that mean temperature is the critical climate factor to the phonological response of the trees except for leaf-falling phase in autumn; temperature inference of phenology has a clear ecological implication in exploring the relation between climate change and phonological response, with which to plan agricultural undertaking and monitor ecological environment on a scientific basis.

In this article, the factor analysis models were used to make evaluations on ecological security in Xinjiang, China, and establishing the ecological security evaluation indicators system to assess the ecological security during 1988-2003 in Xinjiang, ranking the factors that affect Xinjiang's ecological security. At the same time, according to the size of comprehensive factors value that affect the ecological security, Xinjiang's ecological security during 1988-2003 was sequenced. The main conclusions as follows: during 1988-2003, Xinjiang ecological security situation had been steadily improving as a whole, and the ecological security was in a better-toward trend, but with the existence of local indicators, local area being deteriorated.

Xinjiang is rich in natural resources. The fast economic growth in recent decades imposes pressure on environment. We used the material flow index to investigate the impacts of economic development on environment and explored the strategies for sustainable development. Results show that Xinjiang is still at a stage that economic growth is at the cost of high consumption of natural resources. In terms of natural resources usage, the growth of the economy is not efficient. The increase in primary products raises serious problems in environment. Higher level products should be encouraged in the future for sustainable development.

Moderate Resolution Imaging Spectroradiometer (MODIS) data are widely used to compute regional evapotranspiration (ET) at 1000-m spatial resolution. However, due to the fact that the village densities in most counties in North China Plain are higher than 0.5 per km², the crop ET mapping at 1000-m resolution computed using MODIS data often fails to differentiate the crop field from the residential area, thus resulted in inaccurate ET estimation. In this study, we analyzed relationship between crop ET and MODIS-normalized difference vegetation index (NDVI) and deduced ET equations to calculating winter wheat and summer corn ET from NDVI. The equations were tested using measured data and proved that they are reliable. The equations were applied using MODIS 250 m spatial resolution NDVI and mapped crop ET at 250 m resolution. Compared with ET map from high resolution Landsat, the improved resolution ET map can described the spatial variations of regional crop ET in a similar pattern.

The evapotranspiration (ET) is one of the most important components of the water cycle in semi-arid Taihang Mountain region of North China. The spatial distribution and seasonal variation of ET will directly impact the stream flow volume and the amount of lateral recharges to the aquifers of mountain front plain. Due to significant changes in topography, the ET of this semi-arid region tends to vary dramatically both in time and space, which renders the accurate estimation of yearly or seasonal ET a difficult task. In current study, based on rGIS-ET v1.0, a regional ET model, by adding module of adjusting surface temperature in terrain, solar radiance terrain correction, and shaded relief, we improved the rGIS-ET a remote sensing model on ArcGIS platform for mapping ET distribution in such a semi-arid mountain area. With DEM of 30 meter and climate data, we run the model to estimate daily ET in mountain area using Landsat data and MODIS data, respectively. The results of model application shows that model could correct the errors of ET value caused by elevation and terrain significantly while Landsat data was used. While MODIS data was used, the model could not do terrain correction accurately for MODIS has a low spatial resolution, but MODIS data with a high temporal resolution could be used to estimate the temporal variation of ET in a mountain area.

AHP is a kind of very effective systematic analytical method, widely applied to energy utilizing and resource analyzing, talent predicting, economic management project, urban industries planning, communications and transportation, water resource using and so on, using this method to solve the problem of ecology also have very strong practicability and validity. Using 15 kinds of endured plants in East Alashan-West Erdos as the research objects, this paper adopts 3S technique and outfield investigates to confirm the geographical distributions, extent density in the distribution area, plant community construction, and plant community environment of the plants. Then invite the experts to give marks according to this datum and using the AHP method to deal with the results, thereby get the priority protective order of endangered plants in East Alashan-West Erdos.

China is in the fragile climate area and is one of the most serious countries that suffered serious natural disaster in the world. This text had analyzing the climate conditions of China for the three-year difficult period of 1959-1961 scientifically, and told us about the number of the disaster (20, 29, 26 times) that occurred in the main natural areas by analyzing meteorological factors in 1951-1961, and the result is serious. The disasters of these years are mainly drought and typhoon, other natural disasters such as flood, hail are mainly in some areas in China. On the basis of analyze the three years' meteorological materials from the whole country (670 observation stations at national level and national basis, 4/24 times a day) year by year, and do the comparative analysis according to a common way (considered the average value of climate conditions in 1961-1990 as the standard value), analyze the impact of the natural disasters on national economy objectively with specific and accurate materials at that time, and give us suggestions on how to organize the deference of climate emergency system, etc. According to analysis, during 1959 - 1961, China's climate characteristic was lack of precipitation, especially in 1960, the space and time for the precipitation was not fair. As a whole, the weather and climate conditions are very disadvantageous to China's agricultural production during those years, especially 1960. According to loss caused by the disasters, the year 1960 was much more serious than 1959 and 1961.

Land surface states have significant control to the water and energy exchanges between land surface and the atmosphere. Thus land surface information is crucial to the global and regional weather and climate predictions. China has built abundant meteorological stations that collect land surface data with good quality for many years. But applications of these data in their numerical weather and climate prediction models are quite low efficient. To take the advantages of land surface data in numerical weather and climate models, we have developed a three dimension variational (3DVar) Land Data Assimilation Scheme (LDAS). In Part 1 of this paper, we present the mathematical design of the 3DVar LDAS. By assimilating a single point observational datum into a background setup, the LDAS is tested to demonstrate its capability and usage. In the other part of this paper, we will demonstrate the results and error analysis of assimilating China's air temperature observational data of the meteorological stations into ECMWF's model background using the 3DVar Land Data Assimilation Scheme.

Soil moisture and related hydrological process play an important role in regional and global climates. However, large-scale and long-term observation of soil moisture is sparse. In this study, the latest NCAR Community Land Model is used to simulate regional soil moisture in East Asia for recent 25 years with the atmospheric forcing provided by NCEP/DOE reanalysis. A 50-year simulation has been conducted with the first 25 years as the model spins up for soil moisture to reach steady state. The last 25 years simulation provides a soil moisture dataset with physical consistency and spatio-temporal continuity. Our analysis focuses on spatial and temporal variability of the regional soil moisture based on the last 25-year modeling. Additionally, The trend in the regional soil moisture and its possible link to climate warming is examined. The main conclusions can be summarized as follows: 1. Simulated soil moisture exhibits clear sensitivity to its initial condition. Such sensitivity is a function of soil depth. This study indicates that the equilibrium time of soil moisture increases with the depth of soil layers. It takes about 20 years to reach equilibrium below 1.5m. Therefore either a longer spin-up (20 years or more) or accurate initial soil moisture is necessary for a quality land surface modeling. 2. In comparison with the reanalysis and in-situ measurements, the model reproduces the observed large-scale structure reasonably well. The simulation shows mesoscale spatial variation as well. 3. Linear trend analysis shows that soil has become drier in most areas of East Asia in recent years except southern China and the Tibetan Plateau where soil gets wetter. Further analysis indicates that such dry trend may have a close link to warming surface climate through enhanced evaporation.

Staggered canopy system is an effective planting practice for high yield by maximizing light penetration into the canopy of cotton (Gossypium hirsutum L.) plants. Compared with traditional cropping practices, this system produces higher yields in general. A staggered canopy system is constructed by planting two cultivars of different shoot architectures to form a canopy of two leaf layers. Field experiments of four treatments were carried out to determine the optimal pattern of staggered canopy. Solar radiations at different heights in the canopies were measured at a vertical interval of 20 cm between and within rows, using a digital light intensity meter. The optimal planting pattern for high yields consisted of two rows of tall plants bracketing a row of short plants with a wide spacing of 100 cm around the rows of short plants, which formed a staggered canopy. The available photosynthetic photon flux density in the staggered canopy was higher than in the canopy of conventionally planted field after the canopies were closed. The staggered canopy system allows more light penetration into the canopy than the conventional, where light was deducted sharply by an excessively dense canopy. In addition, wind speeds and CO₂ concentrations inside the staggered canopy were greater than those in the conventional. The staggered canopy has an improved canopy structure compared to a conventional planting practice.

Based on research on the oasis-desert ecosystem, changes in land use of the Akesu-Awati oasis from 1990 to 2000 are analyzed through using the methods of 3S and statistics. A classification system of land use is created and 3S-integration is realized. Using the table of equivalent weight factors of the economic value of China's ecosystem services, the loss of ecological value resulting from land use changes are estimated. This research shows that changes of land use are notable. Areas of land use such as cropland, residential site and saline land amount increase rapidly in contrast to various degrees of reduction in grassland, forest land, water area and wetland. The ecological value has become negative, decreasing by 1.055×109 yuan. Given such great losses in the estimated economic value of ecosystem services, it is clear that the best development of society and economy in the research area will be achieved only through comprehensive, planned sustainable development.

Effective management of water resources in arid and semi-arid areas demands studies that cross over the disciplinaries of natural and social sciences. An integrated Hydrological, Ecological and Economical (HEE) modeling system at regional scale has been developed to assess water resources use and ecosystem production in arid and semi-arid areas. As a physically-based distributed modeling system, the HEE modeling system requires various input parameters including those for soil, vegetation, topography, groundwater, and water and agricultural management at different spatial levels. A successful implementation of the modeling system highly depends on how well it is calibrated. This paper presented an automatic calibration procedure for the HEE modeling system and its test in the upper and middle parts of the Yellow River basin. Previous to calibration, comprehensive literature investigation and sensitivity analysis were performed to identify important parameters for calibration. The automatic calibration procedure was base on conventional Monte Carlo sampling method together with a multi-objective criterion for calibration over multi-site and multi-output. The multi-objective function consisted of optimizing statistics of mean absolute relative error (MARE), Nash-Sutcliffe model efficiency coefficient (E_NS), and coefficient of determination (R²). The modeling system was calibrated against streamflow and harvest yield data from multiple sites/provinces within the basin over 2001 by using the proposed automatic procedure, and validated over 1993-1995. Over the calibration period, the mean absolute relative error of simulated daily streamflow was within 7% while the statistics R² and E_NS of daily streamflow were 0.61 and 0.49 respectively. Average simulated harvest yield over the calibration period was about 9.2% less than that of observations. Overall calibration results have indicated that the calibration procedures developed in this study can efficiently calibrate the modeling system in the study area. Annual validation results for average streamflow and harvest yield showed relative large errors which were associated with irrigation water use and reservoir impact. The validation results of streamflow for sites in upper reaches have shown close relationship with observations which indicated the liability of calibrated parameter values in predicting watershed responses. The information and results provided by the study will be helpful to watershed modelers and model users in calibrating complex watershed models and contribute knowledge to interdisciplinary modeling for water resources management in the study area.

Ranging from 83°41' to 95° 11' east longitude and from 30° 27' to 35°39' northern latitude, Northern Tibet in the highest of Tibet is known as the ridge of world with the mean elevation of 4500 meter. Northern Tibet is the headstream of Yangtze River, Nu River and Lancang River. The environmental condition of this area has significant effect on the mainly rivers, climate and eco-environment of Tibet and the whole country, so much as the global for the rigorous natural condition and fragile ecosystem. Based on the Normalized Difference Vegetation Index (NDVI) data (from 1981 to 2004), this paper has revealed the dynamics of grassland vegetation coverage of Northern Tibet by the method which the remote sensing technique integrated with ground measurement. The preliminary results are as follows: The grassland vegetation coverage of Northern Tibet is very low that the average value is only 29.7% for many years. In terms of spatial distribution of grassland vegetation coverage, the eastern region of Northern Tibet has a relatively higher coverage of grassland vegetation; the western has the lowest vegetation coverage. During the period of 1981-2004, the average grassland vegetation coverage had a slight decreasing trend, but fluctuation is relative greatly; the grassland vegetation coverage in the western region had a marked rising trend while the coverage in the middle, eastern and northern regions had a decreasing tendency.

Minimizing soil evaporation is a key element in improving water use efficiency in dry areas. Experiments were conducted in the winter wheat field during 2004-2005 to compare effects of different row spacing on soil evaporation. A model to estimate soil evaporation was developed based on the experimental data. The reference crop evapotranspiration (ET₀) was estimated by the Penman-Montieth equation, and the factors affecting evaporation under crop canopy were divided into both the radiation item (ET_s1) and the aerodynamics item (ET_s2) according to the degree of crop canopy coverage (f_c). The simulation equation for actual evaporation, combining with soil moisture parameter, was established in this paper. In this study, a light meter was utilized to measure f_c, which replaced leaf are index (LAI) in evaporation estimation. In comparison with the measured evaporation by micro-lysimeters (ML) in four row spacing: 7.5 cm, 15 cm, 22.5 cm and 30 cm, tested in a randomized block design, the simulated daily evaporation had root mean square errors (RMSE) of 0.22 mm, 0.24 mm, 0.25 mm 0.26 mm and a bias of 0.01 mm, 0.02 mm, -0.08 mm, -0.03 mm respectively. Results showed that using canopy coverage factor to replace leaf area index could effectively estimate soil evaporation.

In this paper, the laws and forest programs related to the enhancement of carbon sequestration were briefly introduced. The implementation and status of Regulations and decrees, such as National Compulsory Tree Planting Campaign, and forest programs, such as Natural Forest Protection Program, The Key Shelterbelt Development Program, Program to Return Farmland to Forests were summarized. The estimation of carbon sequestration by forest, forest and land-use change and some of the forest programs were reviewed. Only a few estimates for carbon sequestration by forest programs were carried out in China.

The information of biomass and productivity of an ecosystem is an essential to evaluate the ecosystem and its environment. This sort of data is usually retrieved from satellite data. However, the accuracy of the retrieval and the algorithms for the retrieval vary with the environment and the type of the ecosystem. In this study, the relationship between the biomass of oasis ecosystems at Fukang, Xinjiang, China and the normalized vegetation index (NDVI) was established in order to derive biomass data of the ecosystem from satellite data. The NDVI data were from the MODIS data with a resolution of 250 meters. Biomass measurements were taken in August, 2003 at 53 sampling sites. Linear and nonlinear regression analyses were performed on this data set. In general, the nonlinear models perform better than the linear models although all of them can successfully generate biomass data with the input of NDVI. Among those nonlinear models, the model Y=5593.3NDVI³+7509.7NDVI²-1268.9NDVI+191 performs the best in terms of the retrieval accuracy, where Y represents the biomass.

Climate changes in the past decades in Jianghuai Valley are detected systematically by using statistical techniques in this study. The results show that the feature of the climate change is warming. Both temperature and precipitation are now in the phase of a relative high climatic base state and the phase of high climatic variability. Therefore, both frequency and strength of extreme climate events such, as hot weather, droughts, and floods, have increased remarkably since 1990s. Finally, the RCM PRECIS developed by Hadley Center for Climate Prediction and Research is used to provide the predictions of future climate in the valley. The results give an average surface warming of 2.9°C under the SRES B2 emissions scenario by the end of this century (2071-2100). And the precipitation may have a lager increase.