Potential for synergetic use of Sentinel- 1 and Sentinel-2 for mapping biomass of Mediterranean shrublands is investigated. As preliminary research, backscatter and its ratio from Sentinel-1 (C-band dual polarization SAR), and NDVI from Sentinel-2 (13 bands multi-spectral data) are assessed by using the NDVIR biomass model. Then the fusion biomass model is proposed based on shrub volume formations. The fusion model is verified by filed survey data which measured shrub height and diameter applyied into the allometric model. The proposed fusion model shows around 14 % improvement of accuracy compared to the single sensor model (r-square: from 0.72 to 0.86, RMSE: from 0.158 to 0.109).

Climate and land-use change profoundly affect plant species distribution (SD) and composition, and the impact of these processes is expected to increase in the coming years. As a proxy of global changes, knowledge of SD and diversity along climatic gradients is essential to determine the efforts needed for species conservation. Plant spectral diversity is an emerging approach used as a proxy for species diversity based on remote sensing. Thus, the research aim was to develop a comprehensive methodology based on spectral diversity for SD and richness mapping and to study their relations with environmental and human-derived factors, demonstrated along Mediterranean to semi-arid climatic gradient. The study addresses two main knowledge gaps regarding spectral diversity: (1) improving the accuracy of woody species classification by features extraction and selection, and by using texture analysis in an ecosystem characterized by high spatial variability and relatively small-sized and sparse woody vegetation; and (2) developing a better estimate of the local species ‎richness and their response to environmental and human-derived factors (i.e. climate, topography, substrate, and land cover factors) across a transition zone between Mediterranean woodlands and semi-arid dwarf shrublands. A hyperspectral image was acquired for a 43-km strip along the study area using an airborne flight of AISA-FENIX (380–2500 nm, 420 bands) at the end of the 2017 rainy season. The dominant species were surveyed, with a total number of 247 trees and shrubs, to train a machine learning support vector machine (SVM) classification for species distribution mapping, which yielded an overall accuracy of 86.1%. A feature extraction and selection methodology was developed, combining principal component analysis and neighborhood component analysis techniques, facilitating the identification of 33 spectral diagnostic bands out of 330 spectral bands. The classification accuracy was decreased by about 2% to 84.2% using only 33 spectral bands. The classification accuracy improved by about 7.1% for the seven large crown species (93.3%) by adding texture information. Later, the local species richness was calculated by utilizing the alpha diversity index (i.e. the Shannon Index) for 30-m grid cells and was tested in response to environmental (i.e. climate, substrate, and topography) and human-derived factors (i.e. land cover). The highest sensitivity to alpha diversity factors was mean annual precipitation, slope, and land surface temperature. The alpha diversity showed higher richness in the natural Mediterranean shrubland and the guarrigue located in the northern part of the climate gradient. We suggest that the approach presented here significantly improves the estimation of woody species distribution and diversity in areas characterized by high spatial heterogeneity along steep climatic gradients.

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.

Grazing lands are crucial for agricultural productivity, ecological stability, and carbon sequestration, underscoring the importance of monitoring vegetation biomass for the effective management of these ecosystems. Remote sensing data, including optical vegetation indices (VIs) like the Normalized Difference Vegetation Index (NDVI), are widely used to monitor vegetation dynamics due to their simplicity and high sensitivity. In contrast, radar-based VIs, such as the Polarimetric Radar Vegetation Index (PRVI), offer additional advantages, including all-weather imaging capabilities, a wider saturation range, and sensitivity to the vegetation structure information. This study introduces an enhanced form of the PRVI, termed the Normalized PRVI (NPRVI), which is calibrated to a 0 to 1 range, constraining the minimum value to reduce the background effects. The calibration and range factor were derived from statistical analysis of PRVI components across vegetated regions in the Contiguous United States (CONUS), using dual-polarization C-band Sentinel-1 and L-band ALOS-PALSAR data on the Google Earth Engine (GEE) platform. Machine learning models using NPRVI and NDVI demonstrated their complementarity with annual herbaceous biomass data from the Rangeland Analysis Platform. The results showed that the Random Forest Model outperformed the other machine learning models tested, achieving R² ≈ 0.51 and MAE ≈ 498 kg/ha (relative MAE ≈ 32.1%). Integrating NPRVI with NDVI improved biomass estimation accuracy by approximately 10% compared to using NDVI alone, highlighting the added value of incorporating radar-based vegetation indices. NPRVI may enhance the monitoring of grazing lands with relatively low biomass compared to other vegetation types, while also demonstrating applicability across a broad range of biomass levels and in diverse vegetation covers.

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.