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At least 217 records · Page 12Linked to original sources
USGS2007

An automated approach to detecting signals in electroantennogram data

Coupled gas chromatography/electroantennographic detection (GC-EAD) is a widely used method for identifying insect olfactory stimulants present in mixtures of volatiles, and it can greatly accelerate the identification of insect semiochemicals. In GC-EAD, voltage changes across an insect's antenna are measured while the antenna is exposed to compounds eluting from a gas chromatograph. The antenna thus serves as a selective GC detector whose output can be compared to that of a "general" GC detector, commonly a flame ionization detector. Appropriate interpretation of GC-EAD results requires that olfaction-related voltage changes in the antenna be distinguishable from background noise that arises inevitably from antennal preparations and the GC-EAD-associated hardware. In this paper, we describe and compare mathematical algorithms for discriminating olfaction-generated signals in an EAD trace from background noise. The algorithms amplify signals by recognizing their characteristic shape and wavelength while suppressing unstructured noise. We have found these algorithms to be both powerful and highly discriminatory even when applied to noisy traces where the signals would be difficult to discriminate by eye. This new methodology removes operator bias as a factor in signal identification, can improve realized sensitivity of the EAD system, and reduces the number of runs required to confirm the identity of an olfactory stimulant. ?? 2007 Springer Science+Business Media, LLC.

Journal of Chemical Ecology↗
USGS1988

A simple approach to nonlinear estimation of physical systems

Recursive algorithms for estimating the states of nonlinear physical systems are developed. This requires some key hypotheses regarding the structure of the underlying processes. Members of this class of random processes have several desirable properties for the nonlinear estimation of random signals. An assumption is made about the form of the estimator, which may then take account of a wide range of applications. Under the above assumption, the estimation algorithm is mathematically suboptimal but effective and computationally attractive. It may be compared favorably to Taylor series-type filters, nonlinear filters which approximate the probability density by Edgeworth or Gram-Charlier series, as well as to conventional statistical linearization-type estimators. To link theory with practice, some numerical results for a simulated system are presented, in which the responses from the proposed and the extended Kalman algorithms are compared.

Mathematical and Computer Modelling↗
USGS2021

Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).

Agricultural and Forest Meteorology↗
USGS2017

Computing spatial correlation of ground motion intensities for ShakeMap

Modeling the spatial correlation of ground motion residuals, caused by coherent contributions from source, path, and site, can provide valuable loss and hazard information, as well as a more realistic depiction of ground motion intensities. The U.S. Geological Survey (USGS) software package, ShakeMap, utilizes a deterministic empirical approach to estimate median ground shaking in conjunction with observed seismic data. ShakeMap-based shaking estimates are used in concert with loss estimation algorithms to estimate fatalities and economic losses after significant seismic events around the globe. Incorporating the spatial correlation of ground motion residuals has been shown to improve seismic loss estimates. In particular, Park, Bazzuro, and Baker (Applications of Statistics and Probability in Civil Engineering, 2007) investigated computing spatially correlated random fields of residuals. However, for large scale ShakeMap grids, computational requirements of the method are prohibitive. In this work, a memory efficient algorithm is developed to compute the random fields and implemented using the ShakeMap framework. This new, iterative parallel algorithm is based on decay properties of an associated ground motion correlation function and is shown to significantly reduce computational requirements associated with adding spatial variability to the ShakeMap g round motion estimates. Further, we demonstrate and quantify the impact of adding peak ground motion spatial variability on resulting earthquake loss estimates.

Computers & Geosciences↗
USGS2024

Hybrid CPU-GPU solution to regularized divergence-free curl-curl equations for electromagnetic inversion problems

The Curl-Curl equation is the foundation of time-harmonic electromagnetic (EM) problems in geophysics. The efficiency of its solution is key to EM simulations, accounting for over 95% of the computation cost in geophysical inversions for magnetotelluric or controlled-source EM problems. However, most published EM inversion codes are still central processing unit (CPU)-based and cannot utilize recent computational developments on the graphic processing units (GPUs). Based on a previously proposed divergence-free algorithm developed on CPUs, this study demonstrates the current limits of the CPU-based inversion procedure. To exploit the high throughput capability of GPUs, we propose a hybrid CPU-GPU framework to solve forward and adjoint problems required for EM inversions. The large sparse linear systems arising from the staggered-grid finite difference approximation of the Curl-Curl equation are solved with a mixed-precision Krylov subspace solver implemented on a GPU. The algorithm is then tested in EM forward and adjoint calculations, with real-world three-dimensional numerical examples. Test results show promising 30× kernel-level speed-ups over the conventional CPU algorithm. This approach may further take the complex frequency domain EM inversions onto the next, practical stage on small affordable GPU platforms.

Computers and Geosciences↗
USGS2015

Simulating realistic predator signatures in quantitative fatty acid signature analysis

Diet estimation is an important field within quantitative ecology, providing critical insights into many aspects of ecology and community dynamics. Quantitative fatty acid signature analysis (QFASA) is a prominent method of diet estimation, particularly for marine mammal and bird species. Investigators using QFASA commonly use computer simulation to evaluate statistical characteristics of diet estimators for the populations they study. Similar computer simulations have been used to explore and compare the performance of different variations of the original QFASA diet estimator. In both cases, computer simulations involve bootstrap sampling prey signature data to construct pseudo-predator signatures with known properties. However, bootstrap sample sizes have been selected arbitrarily and pseudo-predator signatures therefore may not have realistic properties. I develop an algorithm to objectively establish bootstrap sample sizes that generates pseudo-predator signatures with realistic properties, thereby enhancing the utility of computer simulation for assessing QFASA estimator performance. The algorithm also appears to be computationally efficient, resulting in bootstrap sample sizes that are smaller than those commonly used. I illustrate the algorithm with an example using data from Chukchi Sea polar bears ( Ursus maritimus ) and their marine mammal prey. The concepts underlying the approach may have value in other areas of quantitative ecology in which bootstrap samples are post-processed prior to their use.

Ecological Informatics↗
USGS2012

Do bioclimate variables improve performance of climate envelope models?

Climate envelope models are widely used to forecast potential effects of climate change on species distributions. A key issue in climate envelope modeling is the selection of predictor variables that most directly influence species. To determine whether model performance and spatial predictions were related to the selection of predictor variables, we compared models using bioclimate variables with models constructed from monthly climate data for twelve terrestrial vertebrate species in the southeastern USA using two different algorithms (random forests or generalized linear models), and two model selection techniques (using uncorrelated predictors or a subset of user-defined biologically relevant predictor variables). There were no differences in performance between models created with bioclimate or monthly variables, but one metric of model performance was significantly greater using the random forest algorithm compared with generalized linear models. Spatial predictions between maps using bioclimate and monthly variables were very consistent using the random forest algorithm with uncorrelated predictors, whereas we observed greater variability in predictions using generalized linear models.

Ecological Modelling↗
USGS2018

Using reverse-time egg transport analysis for predicting Asian Carp spawning grounds in the Illinois River

Identifying spawning grounds of Asian carp is important for determining the reproductive front of invasive populations. Ichthyoplankton monitoring along the Illinois Waterway (IWW) has provided information on abundances of Asian carp eggs in the IWW's navigation pools. Post-fertilization times derived from egg development stages and water temperatures can be used to estimate spawning times of Asian carp eggs, but estimating how far these eggs have drifted requires information on river hydraulics. A Fluvial Egg Drift Simulator (FluEgg) program was designed to predict the drift of Asian carp eggs in the riverine environment with egg growth considered. This paper presents a reverse-time particle tracking (RTPT) algorithm for back-casting the spawning location of eggs from their collection site. The RTPT algorithm was implemented as a module in FluEgg. The new version of FluEgg was coupled with an unsteady hydrodynamic model of the IWW to predict the spawning locations for 530 eggs that were collected in June 2015. The results indicate that tailwater sections below the Locks and Dams (L&Ds) in each navigation pool appear to be preferred spawning locations for Silver Carp. From the data analyzed, the most upstream spawning location for the June 2015 spawning period was in the upper Marseilles navigation pool, downstream of the Dresden Island L&D. The RTPT algorithm can efficiently estimate spawning locations for multiple egg samples.

Illinois↗
USGS2024

A probabilistic approach to training machine learning models using noisy data

Machine learning (ML) models are increasingly popular in environmental and hydrologic modeling, but they typically contain uncertainties resulting from noisy data (erroneous or outlier data). This paper presents a novel probabilistic approach that combines ML and Markov Chain Monte Carlo simulation to (1) detect and underweight likely noisy data, (2) develop an approach capable of detecting noisy data during model deployment, and (3) interpret the reasons why a data point is deemed noisy to help heuristically distinguish between outliers and erroneous data. The new algorithm recognizes that there is no unique way to split the training data into noisy and clean data, and thus produces an ensemble of plausible splits. The algorithm successfully detected noisy data in synthetic benchmark problems with varying complexity and a real-world public supply water withdrawal dataset. The algorithm is generic and flexible, making it suitable for application across a broad range of hydrologic and environmental disciplines.

Environmental Modelling & Software↗
USGS2022

Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering

Discovery of hidden geothermal resources is challenging. It requires the mining of large datasets with diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used play fairway analysis approach typically incorporates subject-matter expertise to analyze regional data to estimate geothermal characteristics and favorability. We demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset from southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. Several of these systems are not well characterized because of insufficient existing data and limited past explorative work. This study discovers hidden patterns and relations in the SWNM geothermal dataset to improve our understanding of the regional hydrothermal conditions and energy-production favorability. This understanding is obtained by applying an unsupervised ML algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden signatures characterizing analyzed datasets, (2) the optimal number of these signatures, (3) the dominant data attributes associated with each signature, and (4) the spatial distribution of the extracted signatures. Here, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, and geothermal attributes at 44 locations in SWNM. Using NMFk, we find data patterns and identify the spatial associations of hydrothermal signatures within two physiographic provinces (Colorado Plateau and Basin and Range) and two sub-regions of these provinces (the Mogollon-Datil volcanic field and the Rio Grande rift) in SWNM. The ML algorithm extracted five hydrothermal signatures in the SWNM datasets that differentiate between low (<90) and medium (90-150)-temperature hydrothermal systems. The algorithm also suggests that the Rio Grande rift and northern Mogollon-Datil volcanic field are the most favorable regions for future geothermal resource discovery. NMFk also identified critical attributes to identify medium-temperature hydrothermal systems in the study area. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored regions. The code to execute the performed analyses as well as the corresponding data can be found at https://github.com/SmartTensors/GeoThermalCloud.jl.

New Mexico↗
USGS2022

A validation of satellite derived cyanobacteria detections with state reported events and recreation advisories across U.S. lakes

Cyanobacteria harmful algal blooms (cyanoHABs) negatively affect ecological, human, and animal health. Traditional methods of validating satellite algorithms with data from water samples are often inhibited by the expense of quantifying cyanobacteria indicators in the field and the lack of public data. However, state recreation advisories and other recorded events of cyanoHAB occurrence reported by local authorities can serve as an independent and publicly available dataset for validation. State recreation advisories were defined as a period delimited by a start and end date where a warning was issued due to detections of cyanoHABs over a state's risk threshold. State reported events were defined as any event that was documented with a single date related to cyanoHABs. This study examined the presence-absence agreement between 160 state reported cyanoHAB advisories and 1,343 events and cyanobacteria biomass estimated by a satellite algorithm called the Cyanobacteria Index (CI cyano ). The true positive rate of agreement with state recreation advisories was 69% and 60% with state reported events. CI cyano detected a reduction or absence in cyanobacteria after 76% of the recreation advisories ended. CI cyano was used to quantify the magnitude, spatial extent, and temporal frequency of cyanoHABs; each of these three metrics were greater ( r > 0.2) during state recreation advisories compared to non-advisory times with effect sizes ranging from small to large. This is the first study to quantitatively evaluate satellite algorithm performance for detecting cyanoHABs with state reported events and advisories and supports informed management decisions with satellite technologies that complement traditional field observations.

Harmful Algae↗
USGS2013

Segmenting images automatically for granulometry and sedimentology: a martian case study

In a companion work, we bridge the gap between mature segmentation software used in terrestrial sedimentology and emergent planetary segmentation with an original algorithm optimized to segment whole images from the Microscopic Imager (MI) of the Mars Exploration Rovers (MER). In this work, we compare its semi-automated outcome with manual photoanalyses using unconsolidated sediment at Gusev and Meridiani Planum sites for geologic context. On average, our code and manual segmentation converge to within ~10% in the number and total area of identified grains in a pseudo-random, single blind comparison of 50 samples. Unlike manual segmentation, it also locates finer grains in an image with internal consistency, enabling robust comparisons across geologic contexts. When implemented in Mathematica- 8, the algorithm segments an entire MI image within minutes, surpassing the extent and speed possible with manual segmentation by about a factor of ten. These results indicate that our algorithm enables not only new sedimentological insight from the MER MI data, but also detailed sedimentology with the Mars Science Laboratory’s Mars Hand Lens Instrument.

Icarus↗
USGS2011

Ground settlement monitoring based on temporarily coherent points between two SAR acquisitions

An InSAR analysis approach for identifying and extracting the temporarily coherent points (TCP) that exist between two SAR acquisitions and for determining motions of the TCP is presented for applications such as ground settlement monitoring. TCP are identified based on the spatial characteristics of the range and azimuth offsets of coherent radar scatterers. A method for coregistering TCP based on the offsets of TCP is given to reduce the coregistration errors at TCP. An improved phase unwrapping method based on the minimum cost flow (MCF) algorithm and local Delaunay triangulation is also proposed for sparse TCP data. The proposed algorithms are validated using a test site in Hong Kong. The test results show that the algorithms work satisfactorily for various ground features.

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS2017

Automated cropland mapping of continental Africa using Google Earth Engine cloud computing

The automation of agricultural mapping using satellite-derived remotely sensed data remains a challenge in Africa because of the heterogeneous and fragmental landscape, complex crop cycles, and limited access to local knowledge. Currently, consistent, continent-wide routine cropland mapping of Africa does not exist, with most studies focused either on certain portions of the continent or at most a one-time effort at mapping the continent at coarse resolution remote sensing. In this research, we addressed these limitations by applying an automated cropland mapping algorithm (ACMA) that captures extensive knowledge on the croplands of Africa available through: (a) ground-based training samples, (b) very high (sub-meter to five-meter) resolution imagery (VHRI), and (c) local knowledge captured during field visits and/or sourced from country reports and literature. The study used 16-day time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) composited data at 250-m resolution for the entire African continent. Based on these data, the study first produced accurate reference cropland layers or RCLs (cropland extent/areas, irrigation versus rainfed, cropping intensities, crop dominance, and croplands versus cropland fallows) for the year 2014 that provided an overall accuracy of around 90% for crop extent in different agro-ecological zones (AEZs). The RCLs for the year 2014 (RCL2014) were then used in the development of the ACMA algorithm to create ACMA-derived cropland layers for 2014 (ACL2014). ACL2014 when compared pixel-by-pixel with the RCL2014 had an overall similarity greater than 95%. Based on the ACL2014, the African continent had 296 Mha of net cropland areas (260 Mha cultivated plus 36 Mha fallows) and 330 Mha of gross cropland areas. Of the 260 Mha of net cropland areas cultivated during 2014, 90.6% (236 Mha) was rainfed and just 9.4% (24 Mha) was irrigated. Africa has about 15% of the world’s population, but only about 6% of world’s irrigation. Net cropland area distribution was 95 Mha during season 1, 117 Mha during season 2, and 84 Mha continuous. About 58% of the rainfed and 39% of the irrigated were single crops (net cropland area without cropland fallows) cropped during either season 1 (January-May) or season 2 (June-September). The ACMA algorithm was deployed on Google Earth Engine (GEE) cloud computing platform and applied on MODIS time-series data from 2003 through 2014 to obtain ACMA-derived cropland layers for these years (ACL2003 to ACL2014). The results indicated that over these twelve years, on average: (a) croplands increased by 1 Mha/yr, and (b) cropland fallows decreased by 1 Mha/year. Cropland areas computed from ACL2014 for the 55 African countries were largely underestimated when compared with an independent source of census-based cropland data, with a root-mean-square error (RMSE) of 3.5 Mha. ACMA demonstrated the ability to hind-cast (past years), now-cast (present year), and forecast (future years) cropland products using MODIS 250-m time-series data rapidly, but currently, insufficient reference data exist to rigorously report trends from these results.

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS2021

Using bottom trawls to monitor subsurface water clarity in marine ecosystems

Biophysical processes that affect subsurface water clarity play a key role in ecosystem function. However, subsurface water clarity is poorly monitored in marine ecosystems because doing so requires in-situ sampling that is logistically difficult to conduct and sustain. Novel solutions are thus needed to improve monitoring of subsurface water clarity. To that end, we developed a sampling method and data processing algorithm that enable the use of bottom trawl fishing gear as a platform for conducting subsurface water clarity monitoring using trawl-mounted irradiance sensors without disruption to fishing operations. The algorithm applies quality control checks to irradiance measurements and calculates the downwelling diffuse attenuation coefficient, K d , and optical depth, ζ – apparent optical properties (AOPs) that characterize the rate of decrease in downwelling irradiance and relative irradiance transmission to depth, respectively. We applied our algorithm to irradiance measurements, obtained using bottom-trawl-mounted archival tags equipped with a photodiode collected during NOAA’s Alaska Fisheries Science Center annual summer bottom trawl surveys of the eastern Bering Sea continental shelf from 2004 to 2018. We validated our AOPs by quantitatively comparing surface-weighted K d from tags to the multi-sensor K d (490) product from the Ocean Colour Climate Change Initiative project (OC-CCI) and qualitatively evaluating whether tag K d was consistent with patterns of subsurface chlorophyll-a concentrations predicted by a coupled regional physical-biological model (Bering10K-BESTNPZ). We additionally examined patterns and trends in water clarity in the eastern Bering Sea. Key findings are: 1) water clarity decreased significantly from 2004 to 2018; 2) a recurrent, pycnocline-associated, maximum in K d occurred over much of the northwestern shelf, putatively due to a subsurface chlorophyll maximum; and 3) a turbid bottom layer (nepheloid layer) was present over a large portion of the eastern Bering Sea shelf. Our study demonstrates that bottom trawls can provide a useful platform for monitoring water clarity, especially when trawling is conducted as part of a systematic stock assessment survey.

Alaska↗
USGS2017

Mapping burned areas using dense time-series of Landsat data

Complete and accurate burned area data are needed to document patterns of fires, to quantify relationships between the patterns and drivers of fire occurrence, and to assess the impacts of fires on human and natural systems. Unfortunately, in many areas existing fire occurrence datasets are known to be incomplete. Consequently, the need to systematically collect burned area information has been recognized by the United Nations Framework Convention on Climate Change and the Intergovernmental Panel on Climate Change, which have both called for the production of essential climate variables (ECVs), including information about burned area. In this paper, we present an algorithm that identifies burned areas in dense time-series of Landsat data to produce the Landsat Burned Area Essential Climate Variable (BAECV) products. The algorithm uses gradient boosted regression models to generate burn probability surfaces using band values and spectral indices from individual Landsat scenes, lagged reference conditions, and change metrics between the scene and reference predictors. Burn classifications are generated from the burn probability surfaces using pixel-level thresholding in combination with a region growing process. The algorithm can be applied anywhere Landsat and training data are available. For this study, BAECV products were generated for the conterminous United States from 1984 through 2015. These products consist of pixel-level burn probabilities for each Landsat scene, in addition to, annual composites including: the maximum burn probability and a burn classification. We compared the BAECV burn classification products to the existing Global Fire Emissions Database (GFED; 1997–2015) and Monitoring Trends in Burn Severity (MTBS; 1984–2013) data. We found that the BAECV products mapped 36% more burned area than the GFED and 116% more burned area than MTBS. Differences between the BAECV products and the GFED were especially high in the West and East where the BAECV products mapped 32% and 88% more burned area, respectively. However, the BAECV products found less burned area than the GFED in regions with frequent agricultural fires. Compared to the MTBS data, the BAECV products identified 31% more burned area in the West, 312% more in the Great Plains, and 233% more in the East. Most pixels in the MTBS data were detected by the BAECV, regardless of burn severity. The BAECV products document patterns of fire similar to those in the GFED but also showed patterns of fire that are not well characterized by the existing MTBS data. We anticipate the BAECV products will be useful to studies that seek to understand past patterns of fire occurrence, the drivers that created them, and the impacts fires have on natural and human systems.

continental United States↗
USGS2026

Towards global mapping of dynamic surface water extents using Sentinel-1 SAR data

We introduce a fully automated and scalable method for mapping surface water extents from single-acquisition Sentinel-1 synthetic aperture radar (SAR) imagery. This approach integrates adaptive thresholding of radiometric terrain-corrected SAR backscatter data, fuzzy-logic classification, region growing, dark land estimation, and a bimodality test to minimize false positives in low-backscattering areas and false negatives in high-backscattering areas. By combining these steps, the algorithm achieves classification accuracies exceeding 85% in detecting surface water extents across diverse environmental conditions. Accuracy was first assessed at meter scale using 52 PlanetScope scenes acquired worldwide in September–October 2019; the algorithm achieved 93% overall accuracy, 86% user's accuracy, and 94% producer's accuracy. Global robustness was then evaluated by processing every Sentinel-1 acquisition from 1 to 12 November 2023 and cross-comparing the resulting maps with 6561 temporally matched observational products for end-users from remote sensing analysis (OPERA) dynamic surface water extent from Harmonized Landsat and Sentinel-2 (DSWx-HLS) products. This large-scale test yielded 90% user's and 94% producer's accuracies, confirming reliable performance at continental extent. Additional case studies demonstrate the algorithm's ability to handle surface water extent in sand-dominated deserts, to track seasonal amplitude in Folsom Lake (California), drought-induced loss in Cerro Prieto Reservoir (Mexico), and rapid filling of the Grand Ethiopian Renaissance Dam. These results show that the method scales across local to global domains and maintains high accuracy, providing a practical tool for near-real-time monitoring of floods, droughts, and water-resource management. Because the approach is sensor-agnostic, it can be ported to forthcoming L- and S-band missions such as NASA-ISRO synthetic aperture radar (NISAR), broadening its applicability to future hydrologic observations.

Remote Sensing of Environment↗
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↗