Observations from spaceborne radar contain considerable information about vegetation dynamics. The ability to extract this information could lead to improved soil moisture retrievals and the increased capacity to monitor vegetation phenology and water stress using radar data. The purpose of this review paper is to provide an overview of the current state of knowledge with respect to backscatter from vegetated (agricultural) landscapes and to identify opportunities and challenges in this domain. Much of our understanding of vegetation backscatter from agricultural canopies stems from SAR studies to perform field-scale classification and monitoring. Hence, SAR applications, theory, and applications are considered here too. An overview will be provided of the knowledge generated from ground-based and airborne experimental campaigns that contributed to the development of crop classification, crop monitoring, and soil moisture monitoring applications. A description of the current vegetation modeling approaches will be given. A review of current applications of spaceborne radar will be used to illustrate the current state of the art in terms of data utilization. Finally, emerging applications, opportunities and challenges will be identified and discussed. Improved representation of vegetation phenology and water dynamics will be identified as essential to improve soil moisture retrievals, crop monitoring, and for the development of emerging drought/water stress applications.

The latest developments in radar mission concepts suggest that subdaily synthetic aperture radar will become available in the next decades. The goal of this study was to demonstrate the potential value of subdaily spaceborne radar for monitoring vegetation water dynamics, which is essential to understand the role of vegetation in the climate system. In particular, we aimed to quantify fluctuations of internal and surface canopy water (SCW) and understand their effect on subdaily patterns of L-band backscatter. An intensive field campaign was conducted in north-central Florida, USA, in 2018. A truck-mounted polarimetric L-band scatterometer was used to scan a sweet corn field multiple times per day, from sowing to harvest. SCW (dew, interception), soil moisture, and plant and soil hydraulics were monitored every 15 min. In addition, regular destructive sampling was conducted to measure seasonal and diurnal variations of internal vegetation water content. The results showed that backscatter was sensitive to both transient rainfall interception events, and slower daily cycles of internal canopy water and dew. On late-season days without rainfall, maximum diurnal backscatter variations of >2 dB due to internal and SCW were observed in all polarizations. These results demonstrate a potentially valuable application for the next generation of spaceborne radar missions.

Floods are one of the most wide-spread, frequent, and devastating natural disasters that continue to increase in frequency and intensity. Remote sensing, specifically synthetic aperture radar (SAR), has been widely used to detect surface water inundation to provide retrospective and near-real time (NRT) information due to its high-spatial resolution, self-illumination, and low atmospheric attenuation. However, the efficacy of flood inundation mapping with SAR is susceptible to reflections and scattering from a variety of factors including dense vegetation and urban areas. In this study, the topographic dataset Height Above Nearest Drainage (HAND) was investigated as a potential supplement to Sentinel-1A C-Band SAR along with supervised machine learning to improve the detection of inundation in heterogeneous areas. Three machine learning classifiers were trained on two sets of features dual-polarized SAR only and dual-polarized SAR along with HAND to map inundated areas. Three study sites along the Neuse River in North Carolina, USA during the record flood of Hurricane Matthew in October 2016 were selected. The binary classification analysis (inundated as positive vs. non-inundated as negative) revealed significant improvements when incorporating HAND in several metrics including classification accuracy (ACC) (+36.0%), critical success index (CSI) (+39.95%), true positive rate (TPR) (+42.02%), and negative predictive value (NPV) (+17.26%). A marginal change of +0.15% was seen for positive predictive value (PPV), but true negative rate (TNR) fell −14.4%. By incorporating HAND, a significant number of areas with high SAR backscatter but low HAND values were detected as inundated which increased true positives. This in turn also increased the false positives detected but to a lesser extent as evident in the metrics. This study demonstrates that HAND could be considered a valuable feature to enhance SAR flood inundation mapping especially in areas with heterogeneous land covers with dense vegetation that interfere with SAR.