KEYWORDS: Signal to noise ratio, Sensors, Signal processing, Satellites, Digital signal processing, Design and modelling, Image processing, Process control, Spectroscopy, Quantum efficiency
Regarding crop production, which is the basis for food security, improving yields, using fewer materials through the appropriate use of nitrogen fertilizers and pesticides, and protecting the environment have become important global issues. This is part of the Green Food System Strategy announced by Japan's Ministry of Agriculture, Forestry, and Fisheries (MAFF) in May 2021. In this context, we plan to launch a miniature satellite with a hyperspectral sensor to observe Canopy Nitrogen Content (CNC) and Solar-Induced Fluorescence (SIF) in the mid-to the late 2020s. A miniature hyper spectrometer with a wide spectral range of 400 nm to 1700 nm and a narrow spectral resolution of 2nm to 10 nm, with a relatively medium Ground Sampling Distance (GSD) of 70m and Signal-to-Noise Ratio (SNR) of approximately 130 is currently under consideration. System optimization, such as the trade-off between the GSD and SNR under mass and envelope constraints, and the introduction of cutting-edge technologies, such as visible-enhanced InGaAs detectors, are both critical to the realization of specific mission objectives. A feasibility study and preliminary payload design are presented in this thesis.
The objective of this study was to investigate the potential of the synergy between the biophysical/ecophysiological models and remote sensing signatures for dynamic estimation of key biophysical variables at the ecosystems-atmosphere interface. We obtained a long-term and comprehensive data set of micrometeorological, plant, and remote sensing (optical and thermal domains) measurements over well-managed uniform agricultural fields. The net ecosystem CO2 flux (NEECO2) was measured by the eddy covariance method (ECM). A soil-vegetation-atmosphere transfer (SVAT) model was used to describe the energy balance, water budget, and physiological processes in the soil-vegetation-atmosphere system that allowed simulating the seasonal change of CO2 and water fluxes as well as biomass, photosynthesis, soil water, and surface temperatures. Both remotely sensed surface temperature and spectral reflectance were useful to effectively tune the process-based model, so that biomass, evapotranspiration, and CO2 flux were accurately simulated. Simulated NEECO2 agreed nicely with those measured by ECM, while simulated biomass agreed well with independent measurements. The synergy of remote sensing and process-based modeling was quite effective in utilizing infrequent and multi-source remote sensing data. This approach would have great potential for quantitative and dynamic assessment of multiple variables in terrestrial ecosystems.
Ability to estimate crop information from remotely sensed imagery is fundamental in precision agriculture. Traditional approach using optical remote sensing is often limited by cloud-free quality imagery while microwave radar has not been fully explored to infer crop conditions. There is a need to develop an alternative to infer crop information that overcomes these limitations. In this study, an optical/radar synergy was developed and used to examine its potential for extracting soil and plant information. The synergy uses a microwave scattering model developed by Karam and his colleagues but modified to (1) take into account underneath soil backscattering properties and (2) use optical remote sensing as direct input variables to the model. The synergistic method was applied to two data sets from Maricopa Agricultural Center, Maricopa, Arizona, and the experimental fields of the National Institute for Agro-Environmental Sciences, Tsukuba, Japan. The data sets included images from Landsat and ERS satellites as well as some ground based soil and plant measurements. The preliminary results indicate that radar imagery can be effectively integrated with optical imagery for extracting both soil and plant information. There exist potentials to use such synergy for site-specific agricultural management practices.
Information based on satellite data is used for evaluation of crop growth conditions what is essential for proper management of agricultural fields. The database of satellite data used for this application consists of optical and radar data from ERS. Soil moisture has been assessed using two different approaches. First one concerned the application of soil moisture index based on sensible and latent heat calculated from surface temperature (ATSR) and meteorological data (H/LE) and backscattering coefficient calculated from SAR data. Second one concerned the application of modified semiemperical water-cloud model to simulate backscattering coefficients of C-VV of ERS and L-HH of JERS as a function of LAI, Leaf Water Area Index and Vegetation Water Content. The final results gave the possibilities of comparison of the modeled soil moisture values with field measurements. The two-way attenuation of vegetation in three models for C-VV band and L-HH band has been examined.
An AOTF-based spectral imager was developed for hyperspectral measurement of plant reflectance in the field. A hyperspectral image cube for the spectral region between 450 nm-900 nm can be obtained at 3 to 5 nm resolution intervals within a few seconds. The system is light and compact, and both the spectral wavelengths and intervals are programmable with PC control. Wavelengths can be rapidly tuned, either sequentially or randomly. Hyperspectral measurements were taken over plant leaves and canopies using the AOTF system and a high-resolution radiometer. Both the leaf nitrogen and chlorophyll contents of the rice canopies were well estimated by multiple regression of high- resolution data in the visible and near-IR regions. A weak signal at 970nm and its normalized indices were found to be useful for estimation of leaf water content. An approach of model inversion was enabled by the use of hyperspectral data. A close and linear relation was found between measured and retrieved water contents. Further, an analysis based on concurrent measurements of hyperspectral reflectance and canopy gas exchange by eddy-covariance method suggested the potential of normalized weak signal for the spectral assessment of canopy CO2 uptake. The hyperspectral reflectance measurement has great potential for estimating the ecological and physio-chemical variables of plant leaves and canopies.
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