Geology topics

Alison Thieme

Publications and source records attributed to Alison Thieme.

12 recordsLinked to original sources

USGS2026

The impacts of cover crop biomass on satellite-based detectability of cover crops in Maryland

Cover crop adoption in the U.S. has increased over the past decades, increasing the need to quantify their performance and environmental benefits. While remote sensing (RS)-based approaches for detecting cover crop presence have been developed, there has been limited research on how cover crops with varied biomass and management practices influence detectability. Using unique field-level cover crop presence and biomass datasets in Maryland, U.S., we investigated how RS-based detectability changes for cover crops with varied aboveground biomass, planting, and termination dates. Specifically, we proposed a time-integrated satellite-based greenness feature from Harmonized Landsat-8 and Sentinel-2 (HLS) time series from 2017 to 2021 to estimate biomass of cover crops and evaluated their detectability using a phenology-based cover crop detection framework. The impacts of cover crop planting and termination dates on cover crop biomass were also analyzed. Our results demonstrated that Normalized Difference Vegetation Index (NDVI) estimated biomass with higher accuracy compared to other vegetation indices, and the time-integrated model estimated biomass with higher accuracy than the single-date “snapshot” linear model (R2 from 0.53 to 0.66 and RMSE from 922 kg/ha to 747 kg/ha). While the snapshot models were species sensitive, the time-integrated models showed strong robustness across different species. Detectability increased with cover crop biomass, as detected cover crops averaged 963.3 ±719.5 kg/ha compared to 297.2 ± 209.0 kg/ha for non-detected cover crops. Detection accuracy reached 96.1% for fields exceeding 500 kg/ha, compared with an overall accuracy of 62.7%. Earlier planting and later termination increased biomass and detectability, with biomass rising by 4.14 kg/ha/day (p < 0.01). This study demonstrates how management practices affect cover crop biomass and detectability via satellite time series and provides insights that can inform management of cover crops and monitoring of their effects on agroecosystems.

Maryland↗

USGS2026

Mapping winter cover crops using remote sensing of crop phenology

Winter cover crops (WCC) are an effective agricultural conservation practice for improving soil health and water quality. Several U.S. states have established incentive programs to promote planting WCC between cash crop rotations. In recent years, the area planted with WCC has steadily increased due to these efforts, although the planting area varies among states. Since these programs are largely led by individual counties or state programs, there is no centralized data repository that quantifies the total WCC planted annually or their locations. Therefore, there is a practical need to identify fields that have been planted with WCC, as well as to quantify the total planted area both during and after the growing season. Furthermore, incentive programs often require verification that WCC were planted to release payments to participating growers in a timely manner. Satellite remote sensing time series can provide information on WCC growth status. However, it remains unclear what kind of WCC the remote sensing data detects. For example, separating WCC from weeds and winter commodity crops remains a challenge. Additionally, a binary classification of WCC presence and absence cannot accurately describe the category and performance of the WCC. This paper presents a new remote sensing phenology-based approach to map WCC likelihood. We assessed model predictions of WCC likelihood using WCC planting records and ground observations over fields at the Beltsville Agricultural Research Center (BARC) from 2018 to 2024. Regional WCC maps were assessed using the Maryland Department of Agriculture's records of WCC fields enrolled in the 2019 and 2020 incentive programs. Our results show that the balanced overall accuracy for WCC and non-WCC ranges from 75% to 85% but could exceed 90%, with limited training samples. However, the high accuracy of WCC corresponds with low accuracy for non-WCC due to confusion with weeds and perennial grasses. Early-season detection yields promising results, with overall accuracies comparable to late-season detection. The after-season detection can differentiate between winter cereal grain cash crops and incentive WCC based on the ending date of the growing season. With a small set of WCC samples and management information, the phenology-based mapping approach offers a rapid and scalable solution for mapping WCC over a large region during or after each WCC growing season.

Maryland↗

USGS2025

Satellite assessment of winter cover crop and conservation tillage outcomes to support adaptive management in working landscapes

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.

Maryland↗

USGS2025

Multiyear crop residue cover mapping using narrow-band vs. broad-band shortwave infrared satellite imagery

Crop residue serves an important role in agricultural systems as high levels of fractional crop residue cover ( f R ) can reduce erosion, preserve soil moisture, and build soil organic carbon. However, the ability to accurately quantify f R at scale has been limited. In this study we produced annual maps of f R for farmland in Maryland, USA using WorldView-3 (WV3) imagery paired with on-farm photographs ( n = 895) classified to f R using SamplePoint software. Univariate linear regressions were used to compare photograph f R to WV3 crop residue indices including: 1) Shortwave Infrared Normalized Difference Residue Index (SINDRI), 2) Shortwave Infrared Difference Residue Index (SIDRI), 3) Normalized Difference Tillage Index (NDTI), and 4) Shortwave Infrared Angle Index (SWIRA). SINDRI and SIDRI are based on narrow bands capable of measuring lignocellulose absorption features. NDTI and SWIRA are based on Landsat-comparable broad bands. Our findings demonstrated that SINDRI outperformed other indices in f R estimation in terms of coefficient of determination ( R 2 = 0.869) and root mean square error (RMSE = 0.111), when R 2 and RMSE were averaged across six individual years. For a univariate analysis combining five years of high-quality WV3 imagery, SINDRI again exhibited the highest f R estimation performance ( R 2 = 0.795; RMSE = 0.141), suggesting that SINDRI can map f R accurately with a singular relationship, potentially reducing the need for labor-intensive ground data collection. For broad-band indices, a multiple linear regression analysis that included a Water Index (WI) and Normalized Difference Vegetation Index (NDVI) as additional predictors increased the accuracy of f R estimation significantly, particularly for SWIRA ( R 2 = 0.767; RMSE = 0.144), but also NDTI ( R 2 = 0.654; RMSE = 0.174). Our findings suggest that while indices computed from narrow-band imagery are most accurate for f R estimation, SWIRA has the potential to improve f R estimation compared to NDTI, especially when used in conjunction with WI and NDVI. An index suite of SWIRA, WI, and NDVI can be computed with Landsat 4–9 imagery, providing a more accurate record of global f R dating back to 1982.

Soil and Tillage Research↗

USGS2025

Multispectral red-edge indices accurately estimate nitrogen content in winter cereal cover crops

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. R 2 = 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. R 2 = 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. R 2 = 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. R 2 = 0.765, RMSE = 12.37 kg ha −1 ), management variables (species, season, adj. R 2 = 0.772, and RMSE = 12.13 kg ha −1 ), and biophysical variables (height, fractional ground cover, adj. R 2 = 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.

Maryland↗

USGS2024

Spaceborne imaging spectroscopy enables carbon trait estimation in cover crop and cash crop residues

Purpose Cover crops and reduced tillage are two key climate smart agricultural practices that can provide agroecosystem services including improved soil health, increased soil carbon sequestration, and reduced fertilizer needs. Crop residue carbon traits (i.e., lignin, holocellulose, non-structural carbohydrates) and nitrogen concentrations largely mediate decomposition rates and amount of plant-available nitrogen accessible to cash crops and determine soil carbon residence time. Non-destructive approaches to quantify these important traits are possible using spectroscopy. Methods The objective of this study was to quantify cash and cover crop residue nitrogen and carbon traits using partial least squares regression models and a combination of 1) the band equivalent reflectance (BER) of the PRecursore IperSpettrale della Missione Applicativa (PRISMA) imaging spectroscopy sensor derived from laboratory collected ASD spectra (n = 296) of 11 cover crop species and three cash crop species, and 2) spaceborne PRISMA imagery that coincided with destructive crop residue collections in the spring of 2022 (n = 65). Spectral range was constrained to 1200 to 2400nm to reduce the likelihood of confounding relationships in wavelengths sensitive to plant pigments or those related to canopy structure for both analytical approaches. Results Models using laboratory BER of PRISMA all demonstrated high accuracies and low errors for estimation of nitrogen and carbon traits (adj. R2 = 0.86 – 0.98; RMSE = 0.24 – 4.25%) and results suggest that a single model may be used for a given trait across all species. Models using spaceborne imaging spectroscopy demonstrated that crop residue carbon traits can be successfully estimated using PRISMA imagery (adj. R2 = 0.65 – 0.75; RMSE = 2.71 – 4.16%). We found moderate relationships between nitrogen concentration and PRISMA imagery (adj. R2 = 0.52; RMSE = 0.25%), which is partly related to the range of nitrogen in these senesced crop residues (0.38 – 1.85%). PRISMA imagery models were also impacted by atmospheric absorption, variability in surface moisture content, and some presence of green vegetation. Conclusion As spaceborne imaging spectroscopy data become more widely available from upcoming missions, crop residue trait estimates could be regularly generated and integrated into decision support tools to calculate decomposition rates and associated nitrogen credits to inform precision field management, as well as to enable measurement, monitoring, reporting, and verification of net carbon benefits from climate smart agricultural practice adoption in an emerging carbon marketplace.

Maryland↗

USGS2024

Intercomparison of same-day remote sensing data for measuring winter cover crop biophysical traits

Winter cover crops are planted during the fall to reduce nitrogen losses and soil erosion and improve soil health. Accurate estimations of winter cover crop performance and biophysical traits including biomass and fractional vegetative groundcover support accurate assessment of environmental benefits. We examined the comparability of measurements between ground-based and spaceborne sensors as well as between processing levels (e.g., surface vs. top-of-atmosphere reflectance) in estimating cover crop biophysical traits. This research examined the relationships between SPOT 5, Landsat 7, and WorldView-2 same-day paired satellite imagery and handheld multispectral proximal sensors on two days during the 2012–2013 winter cover crop season. We compared two processing levels from three satellites with spatially aggregated proximal data for red and green spectral bands as well as the normalized difference vegetation index (NDVI). We then compared NDVI estimated fractional green cover to in-situ photographs, and we derived cover crop biomass estimates from NDVI using existing calibration equations. We used slope and intercept contrasts to test whether estimates of biomass and fractional green cover differed statistically between sensors and processing levels. Compared to top-of-atmosphere imagery, surface reflectance imagery were more closely correlated with proximal sensors, with intercepts closer to zero, regression slopes nearer to the 1:1 line, and less variance between measured values. Additionally, surface reflectance NDVI derived from satellites showed strong agreement with passive handheld multispectral proximal sensor-sensor estimated fractional green cover and biomass (adj. R 2 = 0.96 and 0.95; RMSE = 4.76% and 259 kg ha−1, respectively). Although active handheld multispectral proximal sensor-sensor derived fractional green cover and biomass estimates showed high accuracies (R 2 = 0.96 and 0.96, respectively), they also demonstrated large intercept offsets (−25.5 and 4.51, respectively). Our results suggest that many passive multispectral remote sensing platforms may be used interchangeably to assess cover crop biophysical traits whereas SPOT 5 required an adjustment in NDVI intercept. Active sensors may require separate calibrations or intercept correction prior to combination with passive sensor data. Although surface reflectance products were highly correlated with proximal sensors, the standardized cloud mask failed to completely capture cloud shadows in Landsat 7, which dampened the signal of NIR and red bands in shadowed pixels.

Maryland↗

USGS2023

Remote sensing evaluation of winter cover crop springtime performance and the impact of delayed termination

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.

Maryland↗

USGS2023

Near real-time detection of winter cover crop termination using harmonized Landsat and Sentinel-2 (HLS) to support ecosystem assessment

Cover crops are planted to reduce soil erosion, increase soil fertility, and improve watershed management. In the Delmarva Peninsula of the eastern United States, winter cover crops are essential for reducing nutrient and sediment losses from farmland. Cost-share programs have been created to incentivize cover crops to achieve conservation objectives. This program required that cover crops be planted and terminated within a specified time window. Usually, farmers report cover crop termination dates for each enrolled field (∼28,000 per year), and conservation district staff confirm the report with field visits within two weeks of termination. This verification process is labor-intensive and time-consuming and became restricted in 2020–2021 due to the COVID-19 pandemic. This study used Harmonized Landsat and Sentinel-2 (HLS, version 2.0) time-series data and the within-season termination (WIST) algorithm to detect cover crop termination dates over Maryland and the Delmarva Peninsula. The estimated remote sensing termination dates were compared to roadside surveys and to farmer-reported termination dates from the Maryland Department of Agriculture database for the 2020–2021 cover crop season. The results show that the WIST algorithm using HLS detected 94% of terminations (statuses) for the enrolled fields (n = 28,190). Among the detected terminations, about 49%, 72%, 84%, and 90% of remote sensing detected termination dates were within one, two, three, and four weeks of agreement to farmer-reported dates, respectively. A real-time simulation showed that the termination dates could be detected one week after termination operation using routinely available HLS data, and termination dates detected after mid-May are more reliable than those from early spring when the Normalized Difference Vegetation Index (NDVI) was low. We conclude that HLS imagery and the WIST algorithm provide a fast and consistent approach for generating near-real-time cover crop termination maps over large areas, which can be used to support cost-share program verification.

Maryland↗

USGS2023

Atmospheric correction intercomparison of hyperspectral and multispectral imagery over agricultural study sites

In this research effort we assess the performance of atmospheric correction-based surface reflectance (SR) retrievals from two satellite image sources, one with very high spatial resolution (VHR) (<5-m) and the other high spectral resolution (~10-nm). The VHR images are from MAXARs WorldView-3 (WV3) satellite and the high spectral resolution images are from Agenzia Spaziale Italianas (ASI) PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite. We use various atmospheric correction (AC) tools to provide intercomparisons of both AC tools and image source SR estimates. The AC tools we evaluated include Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) within ENVI version 4.7, MODerate resolution atmospheric TRANsmission (MODTRAN) versions 5.3.3 and 6.0, and ASIs Level-2D correction for PRISMA imagery. Prior to correcting WV3 and PRISMA imagery to SR, we performed manual geometric corrections of imagery as both image sources were found to lack consistent georegistration. We performed comparisons at two study sites in Maryland, USA, including the United States Department of Agriculture Beltsville Agricultural Research Center (BARC) and an agricultural study site on Marylands Eastern Shore region. For the BARC site, we used WV3 imagery acquired on 2022-04-02 and PRISMA imagery acquired on 2022-04-28, focusing on evaluation of AC tool SR retrieval performance for each image source separately due to large time differences in image acquisitions where SR values are likely impacted by changing field conditions. For the Eastern Shore site, WV3 imagery was acquired on 2022-05-18 and 2022-05-30, and PRISMA imagery was acquired on 2022-05-21, allowing for quantitative evaluation of both AC tool performance and intercomparison between WV3 and PRISMA imagery. Having WV3 imagery acquired before and after PRISMA imagery allows for interpretation of major changes in field conditions and thus, identification of fields to exclude from intercomparisons. For intercomparison assessments, we computed relative percent difference (RPD) between the AC tool SR retrievals. For image source comparisons, 4-m WV3 pixels were resampled to 30-m PRISMA pixels after which 30-m WV3 bands and PRISMA spectra were compared to one another visually for both study sites. To provide rigorous SR retrieval intercomparisons between image sources, PRISMA spectra were resampled to WV3-equivalent bands for RPD computation for the Eastern Shore site. In addition to the SR retrieval intercomparisons between the AC tools, we carry out a quasi-validation where we retrieve fractional crop residue cover (fR) from the satellite image sources by calculating established spectral indices (SIs) and calibrating SIs with ground-measured fR acquired within several days of satellite overpasses. These SIs include the Cellulose Absorption Index (CAI) (Nagler et al. 2000), Shortwave Infrared Normalized Difference Residue Index (SINDRI) (Serbin et al. 2009), Lignin-Cellulose Absorption Index (LCAI) (Daughtry et al. 2005), and Lignin-Cellulose Peak Center Difference Index (LCPCDI) (Hively et al. 2021) 1-4. The most accurate crop residue SIs are generally based on shortwave infrared (SWIR) reflectance bands ranging from 2000 nm to 2400 nm that measure dry vegetation lignocellulose absorption features at 2100 and 2300 nm 1-5. For instance, the CAI identifies a 2100 nm cellulose absorption feature with a central band positioned on this feature, and two spectrally adjacent bands at 2040 and 2210 nm, while the LCAI identifies the 2300 nm lignin absorption feature compared to bands at 2165 and 2210 nm. Particular focus on intercomparisons for the SWIR region is critical as atmospheric water, carbon dioxide, and methane impact accurate SR retrieval as shown in Figure 1.a. Our final analysis concludes with the selection of the top-performing AC approach between the WV3 and PRISMA imagery (as indicated by low SR RPD) and then compares PRIMSA and 30-m WV3 imagery with original 4-m WV3 imagery to assess the degree to which spatial resolution impacts the retrieval of fR. Figure 1 provides a comparative example of WV3 and PRISMA imagery used to compute SINDRI which is then calibrated to fR using second order polynomial equations from Hively et al. (2018) 6. Figure 1 fR calibrations will be updated with newly acquired ground survey data from May 2022 to further improve the accuracy of image source and AC tool intercomparisons.

Conference Paper↗

USGS2022

Integration of satellite-based optical and synthetic aperture radar imagery to estimate winter cover crop performance in cereal grasses

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 (R 2 = 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.

Maryland↗

USGS2020

Using NASA Earth observations and Google Earth Engine to map winter cover crop conservation performance in the Chesapeake Bay watershed

Winter cover crops such as barley, rye, and wheat help to improve soil structure by increasing porosity, aggregate stability, and organic matter, while reducing the loss of agricultural nutrients and sediments into waterways. The environmental performance of cover crops is affected by choice of species, planting date, planting method, nutrient inputs, temperature, and precipitation. The Maryland Department of Agriculture (MDA) oversees an agricultural cost-share program that provides farmers with funding to cover costs associated with planting winter cover crops, and the U.S. Geological Survey (USGS) and the U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS) have partnered with the MDA to develop satellite remote sensing techniques for measuring cover crop performance. The MDA has developed the capacity to digitize field boundaries for all fields enrolled in their cover crop programs (>26,000 fields per year) to support a remote sensing performance analysis at a statewide scal,e and has requested assistance with the associated imagery processing from the National Aeronautics and Space Administration (NASA). Using the Google Earth Engine (GEE) cloud computing platform, scripts were developed to process Landsat 5/7/8 and Harmonized Sentinel-2 imagery to measure winter cover crop performance. We calibrated cover crop performance models using linear regression between satellite vegetation indices and USGS / USDA-ARS field sampling data collected on Maryland farms between 2006 and 2012 (1298 samples). Satellite-derived Normalized Difference Vegetation Index (NDVI) values showed significant correlation with the natural logarithm of cover crop biomass ( p ≤0.01, R 2 = 0.56) and with observed percent vegetative ground cover (p ≤0.01, R 2 = 0.68). The GEE scripts were used to composite seasonal maximum NDVI values for each enrolled cover crop field and calculate performance metrics for the winter and spring seasons of three enrollment years (2014–15, 2015–16, and 2017–18) for four Maryland counties. Results from winter 2017–18 demonstrate that rye and barley fields had higher biomass than wheat fields, and that early planting, along with planting methods that increase seed-soil contact, increased performance. The processing capabilities of GEE will support the MDA in scaling up remote sensing performance analysis statewide, providing information to evaluate the environmental outcomes associated with various agronomic management strategies. The tool can be modified for different seasonal cutoffs, utilize new sensors to capture phenology in winter and spring, and scale to larger regions for use in adaptive management of winter cover crops planted for environmental benefit.

Maryland↗