Droughts in a warming climate have become more common and more extreme, making understanding forest responses to water stress increasingly pressing. Analysis of water stress in trees has long focused on water potential in xylem and leaves, which influences stomatal closure and water flow through the soil‐plant‐atmosphere continuum. At the same time, changes of vegetation water content (VWC) are linked to a range of tree responses, including fluxes of water and carbon, mortality, flammability, and more. Unlike water potential, which requires demandingin situmeasurements, VWC can be retrieved from remote sensing measurements, particularly at microwave frequencies using radar and radiometry. Here, we highlight key frontiers through which VWC has the potential to significantly increase our understanding of forest responses to water stress. To validate remote sensing observations of VWC at landscape scale and to better relate them to data assimilation model parameters, we introduce an ecosystem‐scale analog of the pressure–volume curve, the non‐linear relationship between average leaf or branch water potential and water content commonly used in plant hydraulics. The sources of variability in these ecosystem‐scale pressure‐volume curves and their relationship to forest response to water stress are discussed. We further show to what extent diel, seasonal, and decadal dynamics of VWC reflect variations in different processes relating the tree response to water stress. VWC can also be used for inferring belowground conditions—which are difficult to impossible to observe directly. Lastly, we discuss how a dedicated geostationary spaceborne observational system for VWC, when combined with existing datasets, can capture diel and seasonal water dynamics to advance the science and applications of global forest vulnerability to future droughts.

Accurate spatio-temporal information about rice growth is an important factor for agronomic management and regional grain yield estimation. In this letter, a unified framework for monitoring and mapping of rice using dense time-series of Sentinel-1 synthetic aperture radar (SAR) images is proposed. A processing chain for such dense time-series Sentinel-1 images is developed with the Google Earth Engine's cloud computing platform. A dense time-series analysis of backscatter response of rice with different management practices is analyzed. Subsequently, the early and late transplanted rice is classified using a clustering algorithm within this platform. The proposed approach is used to monitor different cultivars of rice in three districts in the state of West Bengal, which is one of the major rice growing regions in India. The classification accuracy is assessed across 150 validation points spanning multiple blocks for the 2017 monsoon season. The Sentinel-1 SAR images acquired up to the early vegetative stage for rice have provided satisfactory classification accuracy with an overall accuracy >85% with κ ~ 0.86 across different management practices throughout the region.

Soil moisture dynamics in the presence of dense vegetation canopies are determinants of ecosystem function and biogeochemical cycles, but the capability of existing spaceborne sensors to support reliable and useful estimates is not known. New results from a recently initiated field experiment in the northeast United States show that the National Aeronautics and Space Administration (NASA) SMAP (Soil Moisture Active Passive) satellite is capable of retrieving soil moisture under temperate forest canopies. We present an analysis demonstrating that a parameterized emission model with the SMAP morning overpass brightness temperature resulted in a RMSD (root‐mean‐square difference) range of 0.047–0.057 m³/m³ and a Pearson correlation range of 0.75–0.85 depending on the experiment location and the SMAP polarization. The inversion approach included a minimal amount of ancillary data. This result demonstrates unequivocally that spaceborne L‐band radiometry is sensitive to soil moisture under temperate forest canopies, which has been uncertain because of lack of representative reference data.

Accurate measurements of agricultural land cover are important for monitoring global food security, economic stability, and environmental conditions. Since significant portions of global agricultural land are frequently cloud covered, synthetic aperture radar (SAR) has been shown to be a reliable form of gathering crop measurements, even in regions where acquiring clear optical imagery is challenging. In this work, repeat coverage from the C-band Sentinel-1 satellite over a portion of North Dakota is used to classify individual agricultural land-cover types. In this approach the times series forms the basis of a classification algorithm, where individual pixels are compared against a model of average crop backscatter response and classified as the crop with the least difference from the model. Multiple variations on the analysis are run to test the influence of polarization, iterations in model building, number of training fields, and validation input on the classification accuracy. It is shown that both VV and VH polarizations individually and combined are routinely able to produce overall accuracies above 90% when using multiple iterations in model building. These results show the potential for SAR-based agricultural land cover classifications built from comprehensive time series data.

The National Aeronautics and Space Administration (NASA) in the United States and the Indian Space Research Organisation (ISRO) have developed the NASA-ISRO Synthetic Aperture Radar (NISAR) mission, planned for launch in 2025. The mission will use SAR to map Earth’s solid surfaces every 12 days, persistently on ascending and descending portions of the orbit, over all land and ice. The mission’s primary objectives will be to study Earth’s land and ice deformation and ecosystems in areas of common interest to the U.S. and Indian science communities. This single observatory solution with L-band (24-cm wavelength) and S-band (9.4-cm wavelength) imaging radars has a swath of more than 240 km at 5–10-m resolution, using full polarimetry where needed. The data will be processed into a suite of products in radar-specific and geographic coordinates tailored to the needs of each science discipline. The product suite is designed to be analysis ready and will be freely and openly available. To achieve these unprecedented capabilities, both radars use a reflector-feed system whereby the feed aperture elements are individually sampled to allow a scan-on-receive capability at both the L band and S band. The project is preparing for launch at the integration and test facilities in India. The launch will take place at the Satish Dhawan Space Center in India on ISRO’s Geosynchronous Launch Vehicle (GSLV) Mark II. NISAR will be launched into a sun-synchronous polar orbit at a 748-km altitude with an exact 12-day repeat cycle. This article summarizes the mission, the science, the measurements, and plans for commissioning and early operations.

Crop area mapping is important for tracking agricultural production and supporting food security. Spaceborne approaches using synthetic aperture radar (SAR) now allow for mapping crop area at moderate spatial and temporal resolutions. Multi-frequency SAR data is highly useful for crop monitoring because backscatter response from vegetation canopies is wavelength dependent. This study evaluates the utility of C-band Sentinel-1B (Sentinel-1) and L-band ALOS-2 (PALSAR) data, collected during the 2019 growing season, for generating accurate active crop extent (crop vs. non-crop) classifications over an agricultural region in western Canada. Evaluations were performed against the Agriculture and Agri-Food Canada satellite-based Annual Cropland Inventory (ACI), an open data product that maps land cover across the extent of Canada’s agricultural land. Classifications were performed using the temporal coefficient of variation (CV) approach, where an optimal crop/non-crop delineating CV threshold (CV_thr) is selected according to Youden’s J-statistic. Results show that crop area mapping agreed better with the ACI when using Sentinel-1 data (83.5%) compared to PALSAR (73.2%). Analysis of performance by crop reveals that PALSAR’s poorer performance can be attributed to soybean, urban, grassland, and pasture ACI classes. This study also compared CV values to in situ wet biomass data for canola and soybeans, showing that crops with lower biomass (soybean) had correspondingly lower CV values.