The magnitude of ecosystem services provided by winter cover crops is linked to their performance (i.e., biomass and associated nitrogen content, forage quality, and fractional ground cover), although few studies quantify these characteristics across the landscape. Remote sensing can produce landscape-level assessments of cover crop performance. However, commonly employed optical vegetation indices (VI) saturate, limiting their ability to measure high-biomass cover crops. Contemporary VIs that employ red-edge bands have been shown to be more robust to saturation issues. Additionally, synthetic aperture radar (SAR) data have been effective at estimating crop biophysical characteristics, although this has not been demonstrated on winter cover crops. We assessed the integration of optical (Sentinel-2) and SAR (Sentinel-1) imagery to estimate winter cover crops biomass across 27 fields over three winter–spring seasons (2018–2021) in Maryland. We used log-linear models to predict cover crop biomass as a function of 27 VIs and eight SAR metrics. Our results suggest that the integration of the normalized difference red-edge vegetation index (NDVI_RE1; employing Sentinel-2 bands 5 and 8A), combined with SAR interferometric (InSAR) coherence, best estimated the biomass of cereal grass cover crops. However, these results were season- and species-specific (R² = 0.74, 0.81, and 0.34; RMSE = 1227, 793, and 776 kg ha⁻¹, for wheat (Triticum aestivum L.), triticale (Triticale hexaploide L.), and cereal rye (Secale cereale), respectively, in spring (March–May)). Compared to the optical-only model, InSAR coherence improved biomass estimations by 4% in wheat, 5% in triticale, and by 11% in cereal rye. Both optical-only and optical-SAR biomass prediction models exhibited saturation occurring at ~1900 kg ha⁻¹; thus, more work is needed to enable accurate biomass estimations past the point of saturation. To address this continued concern, future work could consider the use of weather and climate variables, machine learning models, the integration of proximal sensing and satellite observations, and/or the integration of process-based crop-soil simulation models and remote sensing observations.

In 2019, the Maryland Department of Agriculture's Winter Cover Crop Program introduced a delayed termination incentive (after May 1) to promote springtime biomass accumulation. We used satellite imagery calibrated with springtime in situ measurements collected from 2006–2021 (n = 722) to derive biomass estimates for Maryland fields planted to cereal cover crop species (286,200 ha total over two seasons). Cover crop C content remained steady throughout the cover crop growing season (42.6% of biomass), whereas N concentration had an inverse relationship with biomass and ranged from 1.7 to 2.9%. Throughout Maryland, delayed termination fields (n = 19,120; average termination of May 18) were, on average, estimated to accumulate an additional 789 kg of biomass, 15 kg of N, and 336 kg of C per hectare when compared to fields associated with standard termination dates (n = 28,811; average termination of April 16). Over two cover crop seasons (2019–2021), the delayed termination incentive yielded an extra 75,660,000 kg biomass, 1,526,000 kg N, and 32,230,000 kg C across 96,040 hectares. Regularly terminated field incentives cost an average of US$0.10 per kg of biomass and $4.09 per kg of N, with variability associated with agronomic management (species, planting method). Delayed termination fields cost of $0.08 per kg of biomass and $3.51 per kg of N. Late‐planted cover crops that were terminated early had minimal environmental benefit, and wheat, which comprised 68% of cover crop area, performed poorly compared with other cereal species. Our findings demonstrate that substantial additional springtime biomass, C, and N accumulation were achieved through the delayed termination incentive.

Winter cover crops reduce erosion and nutrient runoff from agricultural systems. Although cereal cover crops can decrease field nitrate leaching by 50%–95%, the magnitude of this reduction varies within and between fields, making it challenging to monitor the impact of cover crops on nitrate leaching at large spatial extents. Satellite remote sensing using red‐edge bands has been shown to effectively estimate crop nitrogen (N) content (kg ha⁻¹) in later growth‐stage crops with a closed canopy. In this study, we evaluated 15 spectral indices derived from Sentinel‐2 imagery to estimate N concentration (%) and content (kg ha⁻¹) of cereal cover crops, using 1627 destructive samples collected from 2018 to 2023 in Maryland. Observed N content ranged from 0.1 to 214.7 kg ha⁻¹, while N concentration ranged from 0.6% to 5.5%. The 15 indices considered were poor predictors of N concentration (adj. R² = 0.089, root mean squared error [RMSE] = 0.802%), but were more successful at measuring N content (biomass × N concentration). Delta red‐edge (ΔRE) was the best predictor of N content (adj. R² = 0.748, RMSE = 13.10 kg ha⁻¹ from cross‐validation with 80% train and 20% test splits iterated 100 times) using samples with imagery collected within ±4 days of destructive sampling (n = 1110). Our findings indicate that longer red‐edge wavelengths (783 and 740 nm) are more suited for estimating N content in cereal cover crops compared to shorter red‐edge wavelengths, which have been shown to be more sensitive to biomass. Leave‐one‐year‐out cross‐validation demonstrated that the relationship between ΔRE and N content was robust across all four cover crop sampling years included in the study (adj. R² = 0.700–0.769, RMSE = 10.70–15.40 kg ha⁻¹). Regression model performance improved with the addition of multiple predictors, including biomass (estimated from Normalized Difference Vegetation Index), weather variables (adj. R² = 0.765, RMSE = 12.37 kg ha⁻¹), management variables (species, season, adj. R² = 0.772, and RMSE = 12.13 kg ha⁻¹), and biophysical variables (height, fractional ground cover, adj. R² = 0.818, and RMSE = 10.29 kg ha⁻¹). These findings demonstrate the feasibility of quantifying N content in cereal cover crops using a red‐edge‐based spectral index across large geographic extents and indicate the inclusion of additional predictors, such as weather and management data, improves model accuracy. This work has implications for quantifying reductions in N leaching associated with cover crops, aiding in policymaking and evaluation of conservation programs that impact water bodies such as Chesapeake Bay.

The use of winter cover crops and conservation tillage are agricultural practices promoted to reduce nutrient and sediment loss from cropland, improve soil health, increase infiltration, and support farm nutrient cycling and ecosystem services. However, environmental performance of these practices is variable in the working farm landscape. The Lower Chesapeake Bay research project within the USDA Long‐Term Agroecosystem Research (LTAR) network has collaboratively developed satellite remote sensing algorithms to measure the performance and phenology of winter cover crops (aboveground biomass, nitrogen content, fractional cover, and emergence and termination dates) using no‐cost Harmonized Landsat and Sentinel‐2 multispectral satellite imagery. This research supports annual operational assessment of >28,000 fields per year in four states. Results document the impacts of agronomic management on conservation outcomes, support adaptive management of incentive payment structures, and can reduce the workload for conservation district staff by remotely verifying cover crop management. Additionally, super‐spectral satellite applications have been developed to accurately map crop residue cover by measuring lignocellulose absorption in shortwave infrared wavelengths, producing a 7‐year time series of tillage intensity maps for the Delmarva Peninsula. These remote sensing products can be used in decision support and modeling to estimate changes in nutrient, sediment, and carbon cycling resulting from conservation practice implementation in the working farm landscape. This manuscript provides an overview of remote sensing research findings and applications associated with the USDA LTAR and Conservation Effects Assessment Projects (CEAP), documenting a variety of previously published outcomes with update and expansion of techniques using additional unpublished data and analyses as appropriate.

Islands are hotspots of biological and cultural diversity that face growing threats from invasive species and climate change. Invasive mammal eradication on islands is a proven conservation intervention that prevents biodiversity loss and is a foundational activity for restoring degraded island-ocean ecosystems. However, these interventions are prioritized and evaluated primarily on biodiversity-based objectives despite growing evidence that invasive species removal may also serve as an effective nature-based solution to increase climate resilience of island-ocean ecosystems and contribute to climate change solution by protecting and restoring unique carbon stocks of native woody vegetation. To assess the effectiveness of interventions at the global scale, we developed a consistent and scalable framework for the long-term monitoring of tree cover, forest extent, forest carbon, and vegetation productivity in 1078 islands across 17 ecozones. Time-series of satellite-derived estimates of tree cover and the Normalized Difference Vegetation Index over 36 yr (1984–2020) were used to establish annual baselines and changes in forest extent, carbon stocks, and vegetation productivity. The analysis revealed significant and sustained positive trends in all the indices on islands with eradication. The magnitude and potential biological relevance of these effects was highly variable across ecozones, but the overall sustained effects provide strong evidence of a positive ecosystem response to invasive mammal removal. We also found that, collectively, these islands sustain more than 940 000 ha of forest and 53 million MgC of forest carbon. This novel framework enables measuring the climate co-benefits of island restoration interventions in relevant policy terms using a low cost and globally consistent methodology that is applicable across the range of spatial and temporal scales pertinent to ecosystem recovery dynamics on islands.