François Charron

Irrigated Grassland Monitoring Using a Time Series of TerraSAR-X and COSMO-SkyMed X-Band SAR Data

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°).

Soil moisture retrieval over grassland using X-band SAR data

The objective of this study was to analyze the sensitivity of radar signal in X-band to irrigated grassland soil conditions. Time series of radar (TerraSAR-X and Cosmo-SkyMed) images were acquired at a high temporal frequency in 2013 over a small agricultural region in South Eastern France. Simultaneously to satellite data acquisitions, ground measurements were conducted during several growing cycles of the grassland in order to monitor evolution in soil and vegetation characteristics. Results show that radar signal is clearly dependent on the soil moisture with a higher sensitivity to soil moisture for biomass lower than 1 kg/m2. HH and HV polarizations showed almost similar sensitivity to soil moisture. The penetration depth of the radar wave in X-band was high even for dense and high vegetation: flooded areas were clearly visible on the images with higher detection potential in HH polarization than in HV polarization even for vegetation heights reaching 1 m. These results showed that it is possible to track gravity irrigation and soil moisture variation from SAR X-band images acquired at high spatial resolution and medium incidence angle.

Soil moisture retrieval over irrigated grasslands using X-band SAR data combined with optical data acquired at high resolution

The aim of this study was to develop an inversion approach to estimate surface soil moisture from X band SAR data over irrigated grassland areas. This approach is based on the coupling between Synthetic Aperture Radar and optical images through the Water Cloud Model. An inversion technique based on multi-layer perceptron neural networks was used to invert the WCM for soil moisture estimation. Three inversion configurations were defined: (1) HH polarization, (2) HV polarization, and (3) both HH and HV polarizations, all including the Leaf Area Index derived from optical images. For the three inversion configurations, the NNs were trained and validated using a noisy synthetic dataset generated by the WCM for a wide range of soil moisture and LAI values. The trained NNs were then validated from a real dataset. The use of X band SAR measurements in HH polarization yields more precise results on soil moisture estimates.