Synthetic Aperture Radar (SAR), as an active sensor transmitting long wavelengths, has the advantages of working day and night and without rain or cloud disturbance. It is further able to sense the geometric structure of forests more than passive optical sensors, making it a valuable tool for mapping forest Above Ground Biomass (AGB). This paper studies the ability of the single- and multi-temporal C-band Sentinel-1 and polarimetric L-band PALSAR-2 data to estimate live AGB based on ground truth data collected in New England, USA in 2017. Comparisons of results using the Simple Water Cloud Model (SWCM) on both VH and VV polarizations show that C-band reaches saturation much faster than the L-band due to its limited forest canopy penetration. The exhaustive search multiple linear regression model over the many polarimetric parameters from PALSAR-2 data shows that the combination of polarimetric parameters could slightly improve the AGB estimation, with an adjusted R² as high as 0.43 and RMSE of around 70 Mg/ha when decomposed Pv component and Alpha angle are used. Additionally, the single- and multi-temporal C-band Sentinel-1 data are compared, which demonstrates that the multi-temporal Sentinel-1 significantly improves the AGB estimation, but still has a much lower adjusted R² due to the limitations of the short wavelength. Finally, a site-level comparison between paired control and treatment sites shows that the L-band aligns better with the ground truth than the C-band, showing the high potential of the models to be applied to relative biomass change detection.

Crop type inventory and within season estimates at moderate (85%) accuracies and the ability to provide insight on crop location and extent within the crop season. HLS fused with S1 had, at times, a higher accuracy (5–10% relative overall accuracy and kappa increases) within season although the combination of fused data was minimal at times, crop dependent, and the accuracies tended to converge by harvest. In cloud prone regions and certain temporal periods, S1 performed well overall. The growth in the availability of time dense moderate resolution data streams and different sensitivities of optical and radar data provide a mechanism for within season crop mapping and area estimates that can help improve food security.

Identifying and quantifying conservation-practice adoption in U.S. cropland is key to accurately monitoring trends in soil health regionally and nationally and informing climate change mitigation efforts. We present the results of an automated system used across 645 counties in the United States Corn Belt from 2005 to 2018, mapped at field-scale and summarized for distribution at aggregated scales. Large-scale mapping by OpTIS (Operational Tillage Information System), a software tool that analyzes remotely sensed data of agricultural land, provides trends of conservation tillage (defined as >30% residue cover), cover cropping, and crop rotations, while modeling by DNDC (Denitrification–Decomposition), a process-based model of carbon and biogeochemistry in soil, provides estimates of the ecosystem outcomes associated with the changes in management practices mapped by OpTIS. Ground-truthing data acquired via OpTIS mobile, a roadside field-surveying app, were used for verification in 30 counties. OpTIS results for the Corn Belt show adoption of cover crops after planting corn and soy increased from 1% to 3% of the mapped area when comparing 2006 to 2018. Comparison of trends for conservation tillage use from 2006 to 2018 shows a slight decrease in conservation tillage adoption, from 46% to 44%. Results from DNDC show these soils sequestered soil organic carbon (SOC) at an area-weighted mean change in SOC (dSOC) rate of 161 kgC/ha/year. Comparatively, in a scenario modeled without the adoption of soil health management practices, the same soils would have lost SOC at an area-weighted rate of −65 kgC/ha/year. As many factors affect changes to SOC, including climate and initial SOC in soils, modeling counterfactual scenarios at the field scale demonstrates outcomes of current soil health management in comparison to regional management practices and best management practices, with respect to SOC sequestration. Regional trends in adoption rates of conservation agriculture and resulting soil health implications are of great use for a wide range of stakeholders. We demonstrate the capability of OpTIS remote sensing to deliver robust, large-scale, multi-sensor, ground-verified monitoring data of current and historical adoption of conservation practices, and of DNDC process-based modeling to provide assessments of the associated environmental outcomes across regions in U.S. cropland.

Soil moisture is a key indicator to assess cropland drought and irrigation status as well as forecast production. Compared with the optical data which are obscured by the crop canopy cover, the Synthetic Aperture Radar (SAR) is an efficient tool to detect the surface soil moisture under the vegetation cover due to its strong penetration capability. This paper studies the soil moisture retrieval using the L-band polarimetric Phased Array-type L-band SAR 2 (PALSAR-2) data acquired over the study region in Arkansas in the United States. Both two-component model-based decomposition (SAR data alone) and machine learning (SAR + optical indices) methods are tested and compared in this paper. Validation using independent ground measurement shows that the both methods achieved a Root Mean Square Error (RMSE) of less than 10 (vol.%), while the machine learning methods outperform the model-based decomposition, achieving an RMSE of 7.70 (vol.%) and R² of 0.60.

Recent conflict along the border of Bangladesh and Myanmar has amplified a food security crisis and access to the region remains challenging. Moderate-resolution satellite remote sensing offers an approach to complement more traditional food insecurity hot spot assessment across Rakhine, Myanmar; however, conflict creates unique signals that are not agroclimatologically driven and need to be considered. Time series radar and optical data cubes were built and used to assess for deviations across space and time for rice paddy production areas based on established techniques. Ultimately, the Sentinel-1 radar was more helpful compared to fused Landsat-7 and -8 and Sentinel-2 data cubes that were substantially impacted by cloud cover during key growth stages. Anecdotal reporting, very high resolution (VHR) imagery, and expert knowledge were used to support operational analyses routines in an attempt to characterize rice into failed, abandoned, and cultivated classes across 2016 to 2018 seasons. Accuracy assessment using co-timed VHR showed overall accuracy (%) of 86.5, 87.5, and 91.0 for 2016, 2017, and 2018, respectively. Nearly one-third of rice production was characterized as failed or abandoned in any given year. Qualitative analyses showed paddy failure was often adjacent to conflict events. The moderate-resolution imagery and automated routines offer complementing metrics that can be used to help guide food security assessments. In regions where climate change, migration, and conflict coincide, decision support tools will need to evolve and continue to integrate human perspectives.