An approach is evaluated for retrieval of land surface parameters (soil moisture, vegetation water content, and surface temperature) using satellite microwave radiometer data in the 6-18 GHz frequency range. The approach is applicable to data that will be acquired by the Advanced Microwave Scanning Radiometer (AMSR), planned for launch on the Japanese Advanced Earth Observing Satellite (ADEOS)-II and Earth Observing System (EOS) PM-1 platforms in 1999 and 2000, respectively. The retrieval method is based on a radiative transfer (RT) model for land-surface and atmospheric emission, with model coefficients that can be tuned over specific calibration regions and applied globally. The method uses an iterative, least-squares algorithm, based on six channels of radiometric data. Simulations using this algorithm indicate that, for an assumed sensor noise of 0.3 K in all channels, soil moisture and vegetation water content retrieval accuracies of 0.06 g cm/sup -3/ and 0.15 kg m/sup -2/, respectively, should be achievable in regions of vegetation water content less than approximately 1.5 kg m/sup -2/. A surface temperature accuracy of 2 C should be achievable, except for bare soils, where discrimination between moisture and temperature variability is difficult using this algorithm. These accuracies are for retrievals averaged over the sensor footprint, and they exclude conditions of precipitation, open water, snow cover, frozen ground, or high topographic relief within the footprint. The algorithm has been tested using data from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) for the years 1982-1985, over the African Sahel, and the retrieval results compared to output from an operational numerical weather prediction model.

Retrieval of land surface parameters using passive microwave measurements at 6-18 GHz

https://hal.archives-ouvertes.fr/hal-03272371/document

Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications

The enormous increase of remote sensing data from airborne and space-borne platforms, as well as ground measurements has directed the attention of scientists towards new and efficient retrieval methodologies. Of particular importance is the consideration of the large extent and the high dimensionality (spectral, temporal and spatial) of remote sensing data. Moreover, the launch of the Sentinel satellite family will increase the availability of data, especially in the temporal domain, at no cost to the users. To analyze these data and to extract relevant features, such as essential climate variables (ECV), specific methodologies need to be exploited. Among these, greater attention is devoted to machine learning methods due to their flexibility and the capability to process large number of inputs and to handle non-linear problems. The main objective of this paper is to provide a review of research that is being carried out to retrieve two critically important terrestrial biophysical quantities (vegetation biomass and soil moisture) from remote sensing data using machine learning methods.

Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data

Advances in soil moisture retrieval from synthetic aperture radar and hydrological applications

Observations from spaceborne radar contain considerable information about vegetation dynamics. The ability to extract this information could lead to improved soil moisture retrievals and the increased capacity to monitor vegetation phenology and water stress using radar data. The purpose of this review paper is to provide an overview of the current state of knowledge with respect to backscatter from vegetated (agricultural) landscapes and to identify opportunities and challenges in this domain. Much of our understanding of vegetation backscatter from agricultural canopies stems from SAR studies to perform field-scale classification and monitoring. Hence, SAR applications, theory, and applications are considered here too. An overview will be provided of the knowledge generated from ground-based and airborne experimental campaigns that contributed to the development of crop classification, crop monitoring, and soil moisture monitoring applications. A description of the current vegetation modeling approaches will be given. A review of current applications of spaceborne radar will be used to illustrate the current state of the art in terms of data utilization. Finally, emerging applications, opportunities and challenges will be identified and discussed. Improved representation of vegetation phenology and water dynamics will be identified as essential to improve soil moisture retrievals, crop monitoring, and for the development of emerging drought/water stress applications.

/pdf/radar-remote-sensing-of-agricultural-canopies-a-review-53mfpiiqux.pdf

Radar Remote Sensing of Agricultural Canopies: A Review

A microwave scattering model was developed for layered vegetation based on an iterative solution of the radiative transfer equation up to the second order to account for multiple scattering within the canopy and between the ground and the canopy. The model is designed to operate over a wide frequency range for both deciduous and coniferous forest and to account for the branch size distribution, leaf orientation distribution, and branch orientation distribution for each size. The canopy is modeled as a two-layered medium above a rough interface. The upper layer is the crown, containing leaves, stems, and branches. The lower layer is the trunk region, modeled as randomly positioned cylinders with a preferred orientation distribution above an irregular soil surface. Comparisons of results obtained using this model with measurements from deciduous and coniferous forests show good agreement at several frequencies for both like and cross polarizations. >

/pdf/a-microwave-scattering-model-for-layered-vegetation-2gt3fx66dc.pdf

A.K. Fung

Papers

A microwave scattering model for layered vegetation