The sensitivity of synthetic aperture radar (SAR) backscatter to soil moisture has been adequately established. However, monitoring of soil moisture over large agricultural areas is still difficult because SAR backscatter is also sensitive to other target properties like surface roughness, crop cover, and soil texture (soil type), along with its strong sensitivity to soil moisture. Hence, to develop a methodology for large-area soil moisture estimation using SAR, it is necessary to incorporate the effects of surface roughness, crop cover, and soil texture in the soil moisture retrieval model. In this paper, a methodology for soil moisture estimation over a large area is developed using a pair of low- and high-incidence-angle RADARSAT-1 SAR data over parts of Agra, Mathura, and Bharatpur districts, India, during March 1999. The methodology requires acquisition of synthetic aperture radar data at low and high incidence angles, such that the soil moisture changes are negligible between the two acquisitions. In order to demonstrate the applicability of the developed methodology, the same was validated over a different area (parts of Saharanpur and Haridwar districts, India) during March 2005. Both test sites provided the variety of agricultural heterogeneity required for development and validation of the methodology for large-area soil moisture estimation. The proposed methodology offers an approach to incorporate the effects of surface roughness, crop cover, and soil texture in the soil moisture retrieval model from the space platform, without making any assumptions on the distributions of these parameters or without knowing the actual values of these parameters on ground.

Interaction of synthetic aperture radar (SAR) with vegetation is volumetric in nature, hence SAR is sensitive to the variation in vegetation density. At the same time SAR is also sensitive to other target properties such as canopy structure, canopy moisture, soil moisture and surface roughness of the underlying soil. However, the sensitivity of SAR backscatter to the vegetation density depends upon the frequency, polarization and angle of incidence at which the SAR is operated. This paper provides comparative evaluation of the sensitivity of multi‐frequency and multi‐polarized SAR backscatter to the plant density of Prosopis juliflora, a thorny plant. Monitoring of P. juliflora is of importance as the state forest department introduced it to arrest the spread of desert. In carrying out this study, data from the SIR‐C/X‐SAR mission over parts of Gujarat, India, have been used. In the present study, the variation of multi‐frequency (L and C) and multi‐polarized (HH, VV and VH) SAR backscatter with plant density has been studied. The results clearly indicate that cross‐polarized SAR backscatter at longer wavelength is the appropriate choice for the quantitative retrieval of plant density.

The proposed study offers an approach to incorporate the effect of surface roughness in the estimation of soil moisture from space without actually measuring surface roughness conditions on ground. It is required to acquire synthetic aperture radar data at low and high incidence angles, such that the soil moisture changes are negligible between the two acquisitions.

This paper aims to demonstrate the potential of space-borne datasets for forest height estimation over Indian tropical forests. Fully polarimetric space-borne SAR interferometry (PolInSAR) data is acquired at the L-, C-, and X-band frequencies. The X-band data are acquired in two modes—single pass and repeat pass. The datasets are compensated for decorrelation due to SNR and varying spatial baselines. The remaining major decorrelation is the volumetric decorrelation, which is modeled using random volume over ground model to estimate the PolInSAR height for all the three frequencies. The modified Three-stage inversion algorithm is utilized for forest stand height estimation. The effect of forest biomass on height inversion accuracy is assessed. Furthermore, a first-order estimate of the absolute temporal decorrelation is demonstrated for the repeat-pass space-borne PolInSAR datasets. Extensive field validation campaigns are carried out in the tropical forest ranges. The forest height is inverted using data at all the three frequencies and validated with field measured values. The zero-temporal baseline bistatic TerraSAR-X/TanDEM-X and the L-band ALOS-2/PALSAR-2 result in a good height inversion with ${\bm{r}^2}$ and RMSE of 0.77 and 1.86 m (X-band) and 0.75 and 1.94 m (L-band), respectively. Furthermore, a comparison of estimated height of ALOS-2/PALSAR-2, TerraSAR-X, and RadarSAT-2 has been done with respect to the estimated height of TerraSAR-X/TanDEM-X. It is observed that RMSE of height inversion with respect to TerraSAR-X/TanDEM-X height is found to be 5.4 m, 7.6 m, and 12.8 m for ALOS-2/PALSAR-2, RadarSAT-2, and TerraSAR-X, respectively.

Spatial distribution of surface roughness is very critical information for many application areas. Surface roughness is often characterized using statistical distribution. However, due to the huge complexity associated with spatial soil surfaces it is difficult to accurately characterize surface roughness over large areas using statistical distribution. Surface roughness influences SAR backscatter significantly and therefore for bare soil surfaces, surface roughness plays a critical role in determining the degree of depolarization of the SAR signal. In this paper surface roughness is retrieved using multi-polarized Envisat-1 ASAR data. The depolarization ratio [σ°_VH − σ°_VV] has been found to be very sensitive to surface roughness. This study demonstrates an approach that can be used to retrieve quantitative surface roughness values from a space platform without making any assumptions regarding distribution of surface roughness on the ground.