Vegetation has been effectively monitored using remote sensing time-series vegetation index (VI) data for several decades. Drought monitoring has been a common application with algorithms tuned to capturing anomalous temporal and spatial vegetation patterns. Drought stress models, such as the Vegetation Drought Response Index (VegDRI), often use VIs like the Normalized Difference Vegetation Index (NDVI). The EROS expedited Moderate Resolution Imaging Spectroradiometer (eMODIS)-based, 7-day NDVI composites are integral to the VegDRI. As MODIS satellite platforms (Terra and Aqua) approach mission end, the Visible Infrared Imaging Radiometer Suite (VIIRS) presents an alternate NDVI source, with daily collection, similar band passes, and moderate spatial resolution. This study provides a statistical comparison between EROS expedited VIIRS (eVIIRS) 375-m and eMODIS 250-m and tests the suitability of replacing MODIS NDVI with VIIRS NDVI for drought monitoring and vegetation anomaly detection. For continuity with MODIS NDVI, we calculated a geometric mean regression adjustment algorithm using 375-m resolution for an eMODIS-like NDVI (eVIIRS’) eVIIRS’ = 0.9887 × eVIIRS − 0.0398. The resulting statistical comparisons (eVIIRS’ vs. eMODIS NDVI) showed correlations consistently greater than 0.84 throughout the three years studied. The eVIIRS’ VegDRI results characterized similar drought patterns and hotspots to the eMODIS-based VegDRI, with near zero bias.

Monitoring drought impacts in forest ecosystems is a complex process because forest ecosystems are composed of different species with heterogeneous structural compositions. Even though forest drought status is a key control on the carbon cycle, very few indices exist to monitor and predict forest drought stress. The Forest Drought Indicator (ForDRI) is a new monitoring tool developed by the National Drought Mitigation Center (NDMC) to identify forest drought stress. ForDRI integrates 12 types of data, including satellite, climate, evaporative demand, ground water, and soil moisture, into a single hybrid index to estimate tree stress. The model uses Principal Component Analysis (PCA) to determine the contribution of each input variable based on its covariance in the historical records (2003–2017). A 15-year time series of 780 ForDRI maps at a weekly interval were produced. The ForDRI values at a 12.5km spatial resolution were compared with normalized weekly Bowen ratio data, a biophysically based indicator of stress, from nine AmeriFlux sites. There were strong and significant correlations between Bowen ratio data and ForDRI at sites that had experienced intense drought. In addition, tree ring annual increment data at eight sites in four eastern U.S. national parks were compared with ForDRI values at the corresponding sites. The correlation between ForDRI and tree ring increments at the selected eight sites during the summer season ranged between 0.46 and 0.75. Generally, the correlation between the ForDRI and normalized Bowen ratio or tree ring increment are reasonably good and indicate the usefulness of the ForDRI model for estimating drought stress and providing decision support on forest drought management.

Flash drought has garnered major attention due to its devastating impact on both agricultural and ecological systems impacting production and income for producers, due to its rapid onset nature, and poor predictability. Solar‐induced fluorescence (SIF) is an increasingly well‐known and reliable indicator of vegetation health which captures rapid changes in how plants absorb and use light under stress. Recent efforts leveraging machine learning trained on spaceborne SIF and vegetation data from other sensors are producing seamless maps of SIF at spatial resolutions (∼5 km) aligned with land management needs. Three recent studies, Mohammadi et al. (2022, https://doi.org/10.1073/pnas.2202767119 ), Parazoo et al. (2024, https://doi.org/10.1029/2024GL108310 ), and Behera et al. (2025, https://doi.org/10.1029/2024GL113419 ), directly show the value of high‐resolution SIF mapping for flash drought early warning. Through advanced studies and calibration, we contend that SIF‐based data products are ready for immediate use by the drought monitoring community including the U.S. Drought Monitor.

Forest drought monitoring tools are crucial for managing tree water stress and enhancing ecosystem resilience. The Forest Drought Response Index (ForDRI) was developed to monitor drought conditions in forested areas across the contiguous United States (CONUS), integrating vegetation health, climate data, groundwater, and soil moisture content. This study evaluated ForDRI using Pearson correlations with the Bowen Ratio (BR) at 24 AmeriFlux sites and Spearman correlations with the Tree-Ring Growth Index (TRSGI) at 135 sites, along with feedback from 58 stakeholders. CONUS was divided into four forest subgroups: (1) the West/Pacific Northwest, (2) Rocky Mountains/Southwest, (3) East/Northeast, and (4) South/Central/Southeast Forest regions. Strong positive ForDRI-TRSGI correlations (ρ > 0.7, p < 0.05) were observed in the western regions, where drought significantly impacts growth, while moderate alignment with BR (R = 0.35–0.65, p < 0.05) was noted. In contrast, correlations in Eastern and Southern forests were weak to moderate (ρ = 0.4–0.6 for TRSGI and R = 0.1–0.3 for BR). Stakeholders’ feedback indicated that ForDRI realistically maps historical drought years and recent trends, though suggestions for improvements, including trend maps and enhanced visualizations, were made. ForDRI is a valuable complementary tool for monitoring forest droughts and informing management decisions.

Flash drought has garnered major attention due to its devastating impact on both agricultural and ecological systems impacting production and income for producers, due to its rapid onset nature, and poor predictability. Solar induced fluorescence (SIF) is an increasingly well-known and reliable indicator of vegetation health which captures rapid changes in how plants absorb and use light under stress. Recent efforts leveraging machine learning (ML) trained on spaceborne SIF and vegetation data from other sensors are producing seamless maps of SIF at spatial resolutions (~5 km) aligned with land management needs. Three recent studies, Mohammadi et al. (2022), Parazoo et al. (2024) and Behera et al. (2025, https://doi.org/10.1029/2024GL113419), directly show the value of high-resolution SIF mapping for flash drought early warning. Through advanced studies and calibration, we contend that SIF-based data products are ready for immediate use by the drought monitoring community including the U.S. Drought Monitor.