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

Thieme, Alison; Yadav, Sunita; Oddo, P.; Fitz, J.; McCartney, Sean; King, LeeAnn; Keppler, Jason; McCarty, Gregory W.; Hively, W. Dean

doi:10.1016/j.rse.2020.111943

Cited by 47 publications

(33 citation statements)

References 33 publications

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“…To analyze the growth pattern of cover crops, a threshold of 0.3 was applied on an average NDVI estimated for each cover crop field. In previous studies [16,17], a NDVI value higher than 0.3 provided an indication for a higher vegetation cover of winter crops. Using this threshold, the fields were grouped and labelled into four categories: Winter Hardy (significant growth in fall and spring), Winter Kill (significant growth only in fall), Spring Emergent (significant growth in spring), and Not Covered (no significant growth in both periods) based on their average NDVI over fall and spring.…”

Section: Field Datamentioning

confidence: 82%

“…This capability has recently been bolstered by the availability of a large volume of freely available data from frequently revisiting medium-resolution satellites such as Landsat (30 m) and Sentinel (10 m). Since green and healthy vegetations have higher reflectance in near-infrared (NIR) than other spectral regions, prior studies have leveraged visible and NIR (VIS NIR) satellite imagery to understand, classify and monitor winter cover crops at a landscape scale [13][14][15][16][17]. For instance, Hively et al [17] assessed cover crop areas in Chesapeake Bay watershed in southeastern Pennsylvania between 2010 and 2013 using one image per winter collected from Landsat and SPOT satellites.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of the Spatial and Temporal Patterns of Cover Crops Using Remote Sensing

Zhao

Romanko

et al. 2021

Remote Sensing

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Cover cropping is a conservation practice that helps to alleviate soil health problems and reduce nutrient losses. Understanding the spatial variability in historic and current adoption of cover cropping practices and their impacts on soil, water, and nutrient dynamics at a landscape scale is an important step in determining and prioritizing areas in a watershed to effectively utilize this practice. However, such data are lacking. Our objective was to develop a spatial and temporal inventory of winter cover cropping practices in the Maumee River watershed using images collected by Landsat satellites (Landsat 5, 7 and 8) from 2008 to 2019 in Google Earth Engine (GEE) platform. Each year, satellite images collected during cover crop growing season (i.e., between October and April) were converted into two seasonal composites based on cover crop phenology. Using these composites, various image-based covariates were extracted for 628 ground-truth (field) data. By integrating ground-truth and image-based covariates, a cover crop classification model based on a random forest (RF) algorithm was developed, trained and validated in GEE platform. Our classification scheme differentiated four cover crop categories: Winter Hardy, Winter Kill, Spring Emergent, and No Cover. The overall classification accuracy was 75%, with a kappa coefficient of 0.63. The results showed that more than 50% of the corn-soybean areas in the Maumee River watershed were without winter crops during 2008–2019 period. It was found that 2019/2020 and 2009/2010 were the years with the largest and lowest cover crop areas, with 34% and 10% in the watershed, respectively. The total cover cropping area was then assessed in relation to fall precipitation and cumulative growing degree days (GDD). There was no apparent increasing trend in cover crop areas between 2008 and 2019, but the variability in cover crops areas was found to be related to higher accumulated GDD and fall precipitation. A detailed understanding of the spatial and temporal distribution of cover crops using GEE could help in promoting site-specific management practices to enhance their environmental benefits. This also has significance to policy makers and funding agencies as they could use the information to localize areas in need of interventions for supporting adoption of cover cropping practice.

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Section: Field Datamentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Assessment of the Spatial and Temporal Patterns of Cover Crops Using Remote Sensing

Zhao

Romanko

et al. 2021

Remote Sensing

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“…Cover crops may be terminated by mowing, tilling, plowing, rolling, or application of herbicides when cover crops are still green. Remote sensing data have been successfully used to estimate winter cover crop biomass and percent ground cover [6,[11][12][13]. Mapping the end-of-season (termination) dates for cover crops at the field scale, however, has been a challenge due to the lack of high temporal and spatial resolution remote sensing data.…”

Section: Introductionmentioning

confidence: 99%

Detecting Cover Crop End-Of-Season Using VENµS and Sentinel-2 Satellite Imagery

2020

Self Cite

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Cover crops are planted during the off-season to protect the soil and improve watershed management. The ability to map cover crop termination dates over agricultural landscapes is essential for quantifying conservation practice implementation, and enabling estimation of biomass accumulation during the active cover period. Remote sensing detection of end-of-season (termination) for cover crops has been limited by the lack of high spatial and temporal resolution observations and methods. In this paper, a new within-season termination (WIST) algorithm was developed to map cover crop termination dates using the Vegetation and Environment monitoring New Micro Satellite (VENµS) imagery (5 m, 2 days revisit). The WIST algorithm first detects the downward trend (senescent period) in the Normalized Difference Vegetation Index (NDVI) time-series and then refines the estimate to the two dates with the most rapid rate of decrease in NDVI during the senescent period. The WIST algorithm was assessed using farm operation records for experimental fields at the Beltsville Agricultural Research Center (BARC). The crop termination dates extracted from VENµS and Sentinel-2 time-series in 2019 and 2020 were compared to the recorded termination operation dates. The results show that the termination dates detected from the VENµS time-series (aggregated to 10 m) agree with the recorded harvest dates with a mean absolute difference of 2 days and uncertainty of 4 days. The operational Sentinel-2 time-series (10 m, 4–5 days revisit) also detected termination dates at BARC but had 7% missing and 10% false detections due to less frequent temporal observations. Near-real-time simulation using the VENµS time-series shows that the average lag times of termination detection are about 4 days for VENµS and 8 days for Sentinel-2, not including satellite data latency. The study demonstrates the potential for operational mapping of cover crop termination using high temporal and spatial resolution remote sensing data.

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“…In consideration of seasonality, cloud cover and phenological effects, we selected dry season remote sensing images (Landsat TM & Landsat 8) with a minimum cloud cover of <10% from Earth Explorer (https://earthexplorer.usgs.gov (accessed on 25 January 2022)) from 1998, 2008, and 2018 for land use/cover (LULC) change classifications, downloaded via Google Earth Code Editor [34,35]; see also [26]. The land use prediction was carried out using open-source software QGIS version 2.18 using modules for land use change evaluation (MOLUSCE) plug-in.…”

Section: Remote Sensing Image Classification and Predictive Modelmentioning

confidence: 99%

Land Use Land/Cover Change Reduces Woody Plant Diversity and Carbon Stocks in a Lowland Coastal Forest Ecosystem, Tanzania

Ntukey

Treydte

2022

Sustainability

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The East-African lowland coastal forest (LCF) is one of Africa’s centres of species endemism, representing an important biodiversity hotspot. However, deforestation and forest degradation due to the high demand for fuelwood has reduced forest cover and diversity, with unknown consequences for associated terrestrial carbon stocks in this LCF system. Our study assessed spatio-temporal land use and land cover changes (LULC) in 1998, 2008, 2018 in the LCF ecosystem, Tanzania. In addition, we conducted a forest inventory survey and calculated associated carbon storage for this LCF ecosystem. Using methods of land use change evaluation plug-in in QGIS based on historical land use data, we modelled carbon stock trends post-2018 in associated LULC for the future 30 years. We found that agriculture and grassland combined increased substantially by 21.5% between the year 1998 and 2018 while forest cover declined by 29%. Furthermore, forest above-ground live biomass carbon (AGC) was 2.4 times higher in forest than in the bushland, 5.8 times in the agriculture with scattered settlement and 14.8 times higher than in the grassland. The estimated average soil organic carbon (SOC) was 76.03 ± 6.26 t/ha across the entire study area. Our study helps to identify land use impacts on ecosystem services, supporting decision-makers in future land-use planning.

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

Cited by 47 publications

References 33 publications

Assessment of the Spatial and Temporal Patterns of Cover Crops Using Remote Sensing

Assessment of the Spatial and Temporal Patterns of Cover Crops Using Remote Sensing

Detecting Cover Crop End-Of-Season Using VENµS and Sentinel-2 Satellite Imagery

Land Use Land/Cover Change Reduces Woody Plant Diversity and Carbon Stocks in a Lowland Coastal Forest Ecosystem, Tanzania

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