Prosopis spp. are a fast growing invasive tree originating from the American dry zones, introduced to Kenya in the 1970s for the restoration of degraded pastoral lands after prolonged droughts and overgrazing. Its deep rooting system is capable of tapping into the ground water table reducing its dependency on rain water and increasing its drought tolerance. It is believed that the Prosopis invasion was eased by a hybridization process, described as the Prosopis Juliflora – Prosopis Pallida complex, suggesting that introduced Prosopis spp. evolved into a hybrid, specifically adapted to the environmental conditions, rendering it a superior and aggressive competitor to endemic species. In many dry lands in Kenya Prosopis has expanded rapidly and has become challenging to control. On the other hand, in some cases, an economic use seems possible. In both cases, detailed and accurate maps are necessary to support stakeholders and design management strategies. The aim of this study is to map the distribution of Prosopis spp. in a selected area in north-west Turkana (Kenya), covering a section of the Tarach water basin. The study is funded by the European Union through the National Drought Management Authority (NDMA) in Kenya, and the main purpose is to assess the potential production of Prosopis pods, which can be used to manufacture emergency livestock feeds to support animals during drought events. The classification was performed using novel Sentinel-2 data through a non-parametric Random Forest classifier. A selection of reference sites was visited in the field and used to train the classifier. Very high classification accuracies were obtained.
This work aims at investigating the capability of COSMO-SkyMed® (CSK®) constellation of Synthetic Aperture Radar
(SAR) system to monitor the Leaf Area Index (LAI) of different crops. The experiment was conducted in the Marchfeld
Region, an agricultural Austrian area, and focused on five crop species: sugar beet, soybean, potato, pea and corn. A
linear regression analysis was carried out to assess the sensitivity of CSK® backscattering coefficients to crops changes
base on LAI values. CSK® backscattering coefficients were averaged at a field scale (<σ°dB>) and were compared to the
DEIMOS-1 derived values of estimated LAI. LAI were as well averaged over the corresponding fields (<LAIest>). CSK®
data acquired at three polarizations (HH, VV and VH), four incidence angles (23°, 33°, 40° and 57°) and at different
pixel spacings (2.5 m and 10 m) were tested to assess whether spatial resolution may influence results at a field scale and
to find the best combination of polarizations and CSK® acquisition beams which indicate the highest sensitivity to crop
LAI values. The preliminary results show that sugar beet can be well monitored (r = 0.72 - 0.80) by CSK® by using any
of the polarization acquisition modes, at moderate to shallow incidence angles (33° - 57°). Slightly weaker correlations
were found, at VH polarization only, between CSK® < σ°dB> and <LAIest> for potato (r = 0.65), pea (r = 0.65) and
soybean (r = -0.83). Shallower view incidence angles seem to be preferable to steep ones in most cases. CSK®
backscattering coefficients were no sensitive at all to LAI changes for already developed corn fields.
Remote sensing (RS) has long been a useful tool in global and regional applications. The Water Footprint (WF) of a crop is defined as the volume of water consumed for its production, where green and blue WF stand for rain and irrigation water usage. RS provides new tools for global WF assessment and represents an innovative approach to regional and global irrigation mapping, enabling the estimation of green and blue water use. This paper presents an overview of the EU COST Action ES 1106 "Assessment of European agriculture water use and trade under climate change (EURO-AGRIWAT)", regarding the evaluation of the potential of remote sensing to improve the WF and Virtual Water Trade (VWT) assessment. The main objective is the analysis of the role of satellite data in the suitable models and indices concerned with the analysis of WF and VWT. The main tasks include: an inventory of the existing and near future satellite data records for several European regions that could be used for the WF and VWT assessment; the study of satellite data resolution requirements, in time and space; the analysis of the assimilation of satellite data into models for the determination of green and blue water use; conclusions and recommendations concerning the possibility to integrate remote sensing into WF and VWT accounting. The combination of RS data to assess the volume of irrigation applied, and the green and blue WF faces several limitations with respect to discrepancies in spatial and temporal resolution and data availability, which will be studied.
The aim of this paper is to present a freely available data service platform (http://ivfl-info.boku.ac.at/) for executing preprocessing operations (such as data smoothing, spatial and temporal sub-setting, mosaicking and reprojection) of time series of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices (NDVI and EVI) on request. The web-application is based on the integration of various software and hardware components: a web-interface and a MySQL database are used to collect and store user’s requests. A server-side application schedules the user’s requests and delivers the results. The core of the processing system is based on the “MODIS” package developed in R, which provides MODIS data collection and pre-processing capabilities. Smoothed and gap-filled data sets are derived using the state-ofthe- art Whittaker filter implemented in Matlab. After the processing, data are delivered directly via ftp access. An analysis of the performance of the web-application, along with processing capacity is presented. Results are discussed, in particular in view of an operative platform for real time filtering, phenology and land cover mapping.
Vegetation indices (VI) combine mathematically a few selected spectral bands to minimize undesired effects of soil background, illumination conditions and atmospheric perturbations. In this way, the relation to vegetation biophysical variables is enhanced. Albeit numerous experiments found close relationships between vegetation indices and several important vegetation biophysical variables, well known shortcomings and drawbacks remain. Important limitations of VIs are illustrated and discussed in this paper. As most of the limitations can be overcome using physically-based radiative transfer models (RTM), advantages and limits of RTM are also presented.
In the context of defining a procedure for near real time land use/land cover (LULC) mapping with seasonal updated
products, this research examines the use of time-series and phenological indicators from MODIS NDVI. 16-day NDVI
composites from MODIS (MOD13Q1) covering the period from 2001 to the present were acquired for three test sites
located in different parts of Europe. The newly proposed Whittaker smoother was used for filtering purposes. Metrics of
vegetation dynamics (such as minimum, maximum and amplitude, etc.) were extracted from the filtered time-series.
Subsequently, the capability of three data sets (raw, filtered data and phenological indicators) was evaluated to separate
between different LULC classes by calculating the overall classification accuracy for the years 2002 and 2009. Ground
truth data for model calibration and testing set was derived combining existing land cover products (GLC2000 and
GlobCover 2009). Based on these results, the benefits of using phenological indicators and cleaned data for land cover
classification are discussed.
In the context of a sustainable agriculture, a controlled and efficient irrigation management is required to avoid negative
effects of the increasing water scarcity, especially in arid and semi-arid regions.
Within this background, the project 'Participatory multi-Level EO-assisted tools for Irrigation water management and
Agricultural Decision-Support' (PLEIADeS: http://www.pleiades.es) addressed the efficient and sustainable use of water
for food production in water-scarce environments. Economical, environmental, technical, social and political dimensions
are considered by means of a synergy of leading-edge technologies and participatory approaches. Project partners,
represented by a set of nine pilot case studies, include a broad range of conditions characteristic for the European,
Southern Mediterranean and American regions.
PLEIADeS aimed at improving the performance of irrigation schemes by means of a range of measures, made possible
through wide space-time coverage of Earth observation (E.O.) data and interactive networking capabilities of
Information and Communication Technologies (ICT).
Algorithms for a number of basic products to estimate Irrigation Water Requirements (IWR) in an operational context
are defined. In this study, the pilot zone at the Nurra site in Sardinia, Italy, is chosen to test, validate and apply these
methodologies.
Spatial and temporal information of soil water content is of essential importance for modelling of land surface processes
in hydrological studies and applications for operative systems of irrigation management. In the last decades, several
remote sensing domains have been considered in the context of soil water content monitoring, ranging from active and
passive microwave to optical and thermal spectral bands.
In the framework of an experimental campaign in Southern Italy in 2007, two innovative methodologies to retrieve soil
water content information from airborne earth observation (E.O.) data were exploited: a) analyses of the dependence of
surface temperature of vegetation with soil water content using thermal infrared radiometer (TIR), and b) estimation of
superficial soil moisture content using reflectance in the visible and near infrared regions acquired from optical sensors.
The first method (a) is applicable especially at surfaces completely covered with vegetation, whereas the second method
is preferably applicable at surfaces without or with sparse vegetation. The synergy of both methods allows the
establishment of maps of spatially distributed soil water content.
Results of the analyses are presented and discussed, in particular in view of an operative context in irrigation studies.
KEYWORDS: Soil science, Reflectivity, Data modeling, Vegetation, Agriculture, Solar radiation models, Sensors, Near infrared, Short wave infrared radiation, Sun
In the context of agricultural applications, the knowledge of soil moisture availability is an essential aspect for irrigation
management. The microwave waveband region (SAR) has been primarily used to estimate soil moisture from Earth
Observation (E.O.) data. However, the optical domain (0.4 - 2.5 μm) may as well offer the possibility to get information
about soil moisture since an overall decrease of soil reflectance corresponds to increasing surface soil water content. Data
from two different experiments (ESA SPARC and AgriSAR) have been exploited aiming at estimating soil moisture
from optical E.O. data by using the radiative transfer model PROSAILH. A soil scale factor (α) was introduced into the
model and estimated using a LUT inversion technique. Relatively high negative relationships between the α-factor and
the measured soil water content (up to R2 = 0.73) could be found for several crop types with low vegetation cover. The
results of this study indicate the potential to retrieve surface soil moisture information from optical E.O. data for similar
soil types. The method gives the advantage of retrieving simultaneously soil and canopy characteristics from the same
E.O. data sources by using a physical method of parameter estimation.
Earth Observation (E.O.) technologies provide a valuable data base for the monitoring of crop and soil characteristics on
a large scale, in a rapid, accurate and cost-effective way. The present work aims at evaluating different methods and
models for the estimation of the Leaf Area Index (LAI) by means of hyperspectral data acquired by the optical airborne
instrument CASI during the ESA AgriSAR 2006 campaign. Inversion of a physical model using an iterative optimization
technique (SQP) and a fast look-up-table (LUT) approach is performed and results are compared with an empirical
model based on the relationship between LAI and WDVI. Furthermore, the analyses carried out on the inversion of the
physical models provide the opportunity to test the spectral bands proposed for the upcoming E.O. satellite Sentinel-2
developed by ESA in the framework of GMES (Global Monitoring for Environment and Security). The Sentinel-2
spectral sampling is compared with the one proposed by an independent study determining the wavebands best
characterizing vegetation and crops. Accuracy of LAI estimation, evaluated with the AgriSAR 2006 field measurements,
is discussed in the context of operational agricultural monitoring.
In the context of vegetation studies Earth Observation (E.O.) data have been extensively used to retrieve biophysical
parameters of land surface. In some cases, thanks to the availability of near-real-time data, tools and applications have
been developed and implemented in the fields of precision agriculture, water resources monitoring and management. So
far, empirical approaches based on vegetation indices (VIs) have been successfully applied. They may provide a
satisfactory level of accuracy in the estimation of important vegetation biophysical parameters (e.g. LAI, fractional
ground cover, biomass, etc). Such methods, however, require a reliable reference data-set to calibrate empirical formulas
on different vegetation types; furthermore, they are generally based on a few spectral bands, with a consistent under-exploitation
of the full spectral range available in new generation sensors. Alternative approaches based on inversion of
radiative transfer models of vegetation represent a challenging opportunity for the estimation of vegetation parameters
from data with high dimensionality.
The Leaf Area Index is a key parameter that is indispensable for many biophysical and climatic models. LAI is required for modeling crop water requirements for precision farming and agricultural resource management. The objective of this study was to investigate different approaches for estimating LAI from EO data. To this aim multiangular CHRIS/PROBA data, from SPARC 2003 and 2004, were used in the inversion of PROSPECT-SAILH models using a numerical optimization technique based on Marquardt-Levenberg algorithm. The optimal spectral sampling to estimate LAI was investigated using a sensitivity analysis. From the same data set, the reflectance in the red and near-infrared bands, from the closer to nadir image, was considered in order to estimate the LAI using an empirical approach based on the CLAIR model. The LAI obtained from the empirical approach was finally employed as prior information in the physical based model. LAI values retrieved with the combined approaches were realistically estimated with a good accuracy (RMSE is 0.51 m2m-2).
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