Speaker
Description
Monitoring crop conditions is essential for forecasting agricultural production and supporting food security. Two key variables for this purpose are Surface Soil Moisture (SSM) and Vegetation Water Content (VWC). VWC quantifies the amount of water stored in the above-ground biomass per unit area and is commonly measured through destructive field sampling. While accurate, this approach is labor-intensive and unsuitable for operational monitoring over large agricultural regions.
Earth Observation (EO) technologies offer an effective alternative for systematic, large-scale VWC monitoring. Optical remote sensing has been extensively used for this purpose but is often hindered by cloud cover and atmospheric effects. Microwave observations, in contrast, provide all-weather and day-and-night monitoring capabilities. VWC can be inferred from microwave measurements through the retrieval of Vegetation Optical Depth (VOD), a variable that describes microwave attenuation within the vegetation canopy. However, VOD products derived from radiometers and scatterometers are currently available only at coarse spatial resolutions (typically 10-50 km), limiting their applicability at the field scale. Synthetic Aperture Radar (SAR) observations are therefore a key resource for downscaling VOD- and VWC-related information to agricultural plot resolutions [1].
Despite this potential, the retrieval of VWC from SAR data remains challenging because of the complex interactions between soil, vegetation, and surface roughness contributions to the radar backscatter signal. The increasing availability of C-band observations from Sentinel-1 and L-band observations from missions such as NISAR, SAOCOM, and ALOS-4, together with the future ROSE-L mission, creates unprecedented opportunities for synergistic multi-frequency retrieval approaches.
Within this context, the ESA SARWATER project aims to retrieve SSM and VWC at approximately 250 m spatial resolution across a wide range of crop and soil conditions through the combined use of C- and L-band SAR observations [2]. The proposed framework relies on two pillars: first, the distinct temporal dynamics of soil moisture, VWC, and surface roughness, which allow their individual contributions to SAR measurements to be decoupled; and second, the complementary sensitivities of C- and L-band responses to soil and vegetation properties, which guide the overall retrieval strategy. The methodology was initially developed and tested using airborne C- and L-band SAR data acquired during the AGRISAR 2006 campaign over the Görmin test site in northern Germany, together with concurrent in situ measurements [3].
This work also presents the retrieval methodology and its initial assessment over two contrasting agricultural regions: the Tavoliere plain in southern Italy and the Rur Catchment in Germany. The analysis is based on multi-year Sentinel-1, ALOS, and SAOCOM observations combined with extensive in situ VWC measurements collected during dedicated field campaigns, including ESA SARSENSE [4] and SARSimNG-HT [5], as well as SSM observations from long-term hydrological monitoring networks. The retrieved VWC estimates are further evaluated through comparison with independent optical-based [6] and model-derived [7] VWC products.
References:
[1] El Hajj, Mohammad, et al. "First vegetation optical depth mapping from Sentinel-1 C-band SAR data over crop fields."Remote Sensing, 11.23 (2019): 2769, DOI: 10.3390/rs11232769.
[2] Mattia, Francesco, et al. “Advancing High-Resolution Retrieval of Soil and Vegetation Water Content from C- and L-band SAR Observations: the SARWATER Project.” 4th vol. Trends in Earth Observation: Smart Earth Observation for a Sustainable Future, (2026), DOI: 10.978.88944687/31.
[3] Hajnsek, I., et al. "AgriSAR 2006—Airborne SAR and optics campaigns for an improved monitoring of agricultural processes and practices." Geophys. Res. Abstr. Vol. 9. 2007.
[4] Mengen, David, et al. "The SARSense campaign: Air-and space-borne C-and L-band SAR for the analysis of soil and plant parameters in agriculture."Remote Sensing 13.4 (2021): 825. DOI: 10.3390/rs15092282
[5] Balenzano, Anna, et al. "SARSimHT-NG campaign over southern Italy for investigating sub-daily water processes." IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. DOI: 10.1109/IGARSS53475.2024.10641490
[6] Salinero-Delgado, Matías, et al. "Monitoring cropland phenology on Google Earth Engine using gaussian process regression." Remote Sensing 14.1 (2021): 146. DOI: 10.3390/rs14010146
[7] Garofalo, Pasquale, et al. "EaSiCroM: A Modular, Low-Parameterisation Decision Support System for Crop Growth Simulation and Irrigation Scheduling in Water-Scarce Agricultural Systems." Sustainability 18.8 (2026): 3956. DOI: 10.3390/su18083956
Acknowledgment: This research was supported by the project SAR retrieval of soil and vegetation WATER content (SARWATER), funded by the European Space Agency (ESA) under Contract No. 4000148119/25/I-EB-bgh. (Website: https://sarwater.irea.cnr.it/)