Biomass is a critical biophysical parameter used to monitor agricultural and terrestrial ecosystems. Operational difficulties in measuring biomass on the field scale led to use of remote sensing techniques. Numerous vegetation indices (VIs) have been developed for this purpose, most of which utilize bands in the VIS-NIR spectral region. However, a significant number of them exhibit saturation at leaf area index (LAI) higher than 2. We propose an innovative vegetation index red-edge ratio normalized difference vegetation index (RERNDVI), that combines a VI sensitive to moderate to high LAI with VI sensitive to low to moderate LAI. An empirical assessment of the performance of our new index compared with nine existing spectral indices was conducted using hyperspectral and biomass data collected by Center for Advanced Land Management Information Technologies for maize and soybean fields, and Sentinel-2 data for semi-arid shrubland sites for which biomass was estimated based on the allometric model. The coefficient of determination (r²) of RERNDVI with green biomass for maize and soybean is high, but equally important, its noise equivalence is significantly lower than that obtained for all other indices for the full range of biomass levels. Nevertheless, RERNDVI-biomass relationships vary for different crops and shrubs, suggesting that generalizing these relationships will require information regarding canopy structure parameters as well.

Advanced Synthetic Aperture Radar (SAR) has become an essential modality in remote sensing, offering all-weather capabilities and sensitivity to vegetation biophysical parameters and surface conditions, while effectively complementing optical sensor data. This study evaluates the impact of precipitation on the Normalized Polarimetric Radar Vegetation Index (NPRVI) using dual-polarization Sentinel-1 C-band SAR data from agricultural fields at the Beltsville Agricultural Research Center (BARC). Field-measured precipitation and Global Precipitation Measurement (GPM) precipitation datasets were temporally aligned with Sentinel-1 acquisition times to assess the sensitivity of radar signals to precipitation events. NPRVI exhibited a strong sensitivity to precipitation, particularly within the 1 to 7 h prior to the satellite overpass, even for small amounts of precipitation. A quality assessment (QA) framework was developed to flag and correct precipitation-affected radar observations through interpolation. The adjusted NPRVI values, based on the QA framework using precipitation within a 6 h window, showed strong agreement between field- and GPM-derived data, with an RMSE of 0.09 and a relative RMSE of 19.8%, demonstrating that GPM data can serve as a viable alternative for quality adjustment despite its coarse spatial resolution. The adjusted NPRVI for both soybean and corn fields significantly improved the temporal consistency of the time series and closely followed NDVI trends, while also capturing crop-specific seasonal variations, especially during periods of NDVI saturation or limited variability. These findings underscore the value of the proposed radar-based QA framework in enhancing the interpretability of vegetation dynamics. NPRVI, when adjusted for precipitation effects, can serve as a reliable and complementary tool to optical vegetation indices in agricultural and environmental monitoring.

Soil moisture plays an important role in agriculture, hydrology, plants’ seasonal growth cycle, and their sustainability under climate change. This paper presents a novel method for regional mapping of soil moisture with partial vegetation cover by using dual-polarized Sentinel-1 C-band SAR data. Model-based retrieval of soil moisture from radar backscatter under partial vegetation cover first necessitates the separation of the backscatter of the underlying soil from total radar backscatter, and then the implementation of an inversion technique to estimate its soil moisture content from the backscattering coefficient of the underlying soil. The regional mapping of soil moisture was verified with in-situ measurements along a climatic gradient (650 mm/year to 100 mm/year) in central Israel. A good agreement between the estimated soil moisture and the measured from ground samples was found, with an r-square of 0.793 with an RMSE of 0.047.

This paper presents soil moisture retrieval from measured polarimetric backscattering coefficients of a vegetated surface. Based on the analysis of the quite complicate first-order radiative transfer scattering model for vegetated surfaces, a simplified scattering model is proposed for an inversion algorithm. Extraction of the surface-scatter component from the total scattering of a vegetation canopy is addressed using the simplified model, and also using the three-component decomposition technique. The back-scattering coefficients are measured with a polarimetric L-band scatterometer during two months as well as the biomasses, leaf moisture contents, and soil moisture contents. Then the measurement data are used to estimate the model parameters for vv-, hh-, and vh-polarizations. The scattering model for tall-grass-covered surfaces is inverted to retrieve the soil moisture content from the measurements using a genetic algorithm. The retrieved soil moisture contents agree quite well with the in-situ measured soil moisture data.

A new polarimetric SAR image classification technique based on the degree of polarization (DoP) and the co-polarized phase-difference (CPD) is presented in this paper. Since the DoP and the CPD of a scattered wave provide information on the randomness of the scattering and the type of scattering mechanisms, at first, the statistics of the DoP and CPD are examined with measured polarimetric SAR image data. Then, a DoP-CPD diagram with appropriate boundaries between six different classes is developed based on the SAR image. The classification results are compared with the existing Entropy-alpha diagram as well as the JPL-AirSAR polarimetric data. The technique may have capability to classify an SAR image into six major classes; a bare surface, a village, a crown-layer short vegetation canopy, a trunk-layer short vegetation canopy, a crown-layer forest, and a trunk-dominated forest.