Abstract
The objective of this study was to analyze the sensitivity of radar signals in the X-band in irrigated grassland conditions. The backscattered radar signals were analyzed according to soil moisture and vegetation parameters using linear regression models. A time series of radar (TerraSAR-X and COSMO-SkyMed) and optical (SPOT and LANDSAT) images was acquired at a high temporal frequency in 2013 over a small agricultural region in southeastern France. Ground measurements were conducted simultaneously with the satellite data acquisitions during several grassland growing cycles to monitor the evolution of the soil and vegetation characteristics. The comparison between the Normalized Difference Vegetation Index (NDVI) computed from optical images and the in situ Leaf Area Index (LAI) showed a logarithmic relationship with a greater scattering for the dates corresponding to vegetation well developed before the harvest. The correlation between the NDVI and the vegetation parameters (LAI, vegetation height, biomass, and vegetation water content) was high at the beginning of the growth cycle. This correlation became insensitive at a certain threshold corresponding to high vegetation (LAI ~2.5 m2/m2). Results showed that the radar signal depends on variations in soil moisture, with a higher sensitivity to soil moisture for biomass lower than 1 kg/m². HH and HV polarizations had approximately similar sensitivities to soil moisture. The penetration depth of the radar wave in the X-band was high, even for dense and high vegetation; flooded areas were visible in the images with higher detection potential in HH polarization than in HV polarization, even for vegetation heights reaching 1 m. Lower sensitivity was observed at the X-band between the radar signal and the vegetation parameters with very limited potential of the X-band to monitor grassland growth. These results showed that it is possible to track gravity irrigation and soil moisture variations from SAR X-band images acquired at high spatial resolution (an incidence angle near 30°).
Highlights
In agriculture areas, information on soil and vegetation conditions is key for water and crop management
Courault et al [15] found a kLAI of 0.71 from Formosat-2 images acquired on a larger area in the same region, including wheat, rice and irrigated grassland
The objective of this work was to investigate the sensitivity of radar signals to soil moisture and vegetation parameters (LAI, vegetation height, biomass, and vegetation water content)
Summary
Information on soil and vegetation conditions is key for water and crop management. The use of in situ sensors to measure soil and vegetation parameters is not effective, especially over large areas, due to the punctual information provided by these measurements. Space-borne remote sensing is a useful tool for mapping vegetation and soil parameters due to its capacity to provide continuous coverage over large areas at various spatial and temporal resolutions. The information extracted from optical data is sometimes incomplete due to clouds. Sensors with spectral bands in the microwave range allow for the acquisition of images in all types of weather. SAR (Synthetic Aperture Radar) sensors are useful additional remote sensing data sources for applications such as crop and water management
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