Winter-time cover crop identification: A remote sensing-based methodological framework for new and rapid data generation
Accurately identifying and systematically mapping winter-time cover crops and their phenological characteristics offer significant benefits to agricultural producers and policymakers, as cover crops are one of several potential solutions to climate change mitigation. We present a methodological fram...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ahmed, Zobaer [verfasserIn] Nalley, Lawton [verfasserIn] Brye, Kristofor [verfasserIn] Steven Green, V. [verfasserIn] Popp, Michael [verfasserIn] Shew, Aaron M. [verfasserIn] Connor, Lawson [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: International journal of applied earth observation and geoinformation - Amsterdam [u.a.] : Elsevier Science, 1999, 125 |
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| Übergeordnetes Werk: |
volume:125 |
| DOI / URN: |
10.1016/j.jag.2023.103564 |
|---|
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ELV066161231 |
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| 520 | |a Accurately identifying and systematically mapping winter-time cover crops and their phenological characteristics offer significant benefits to agricultural producers and policymakers, as cover crops are one of several potential solutions to climate change mitigation. We present a methodological framework for identifying and mapping the presence of winter-time cover crops at the field level and aggregated to county scales from 2013 to 2019 by using the Google Earth Engine (GEE), a random forest classifier with time series data from Landsat 8, and yearly cover crop training data from the United States Department of Agriculture (USDA)-Natural Resources Conservation Service (NRCS). The methodology was tested with data from the Mississippi Alluvial Plain (MAP) region. Despite the inter-annual agronomic and climatic variations across space, results demonstrated an overall mean classification accuracy of 97.7%, with a kappa coefficient of 0.94. Results also revealed a 34% increase in model-predicted cover crop adoption in the study region from 2013 to 2019. Based on GEE, this study created, for the first time, a 30-m spatial and temporal resolution binary annual datasets and then aggregated them at the county level within the MAP study region. This multi-year novel dataset may improve our ability to anticipate and quantify the impact of summer crop production gains owing to cover crop adoption for extended periods and evaluate the adoption of cover crops on local soil ecosystems, biogeochemical cycles, and services. The methodology developed and tested broadly applies to other regions where cover crops have been promoted for climate-change mitigation and improving soil health for long-term sustainability. Agricultural producers, policymakers, and cost-share providers may use this information to develop agricultural conservation methods and land-use policies that minimize soil erosion and help mitigate climate change effects in the long run. | ||
| 650 | 4 | |a Winter-time cover crops | |
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| 700 | 1 | |a Popp, Michael |e verfasserin |4 aut | |
| 700 | 1 | |a Shew, Aaron M. |e verfasserin |4 aut | |
| 700 | 1 | |a Connor, Lawson |e verfasserin |4 aut | |
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10.1016/j.jag.2023.103564 doi (DE-627)ELV066161231 (ELSEVIER)S1569-8432(23)00388-6 DE-627 ger DE-627 rda eng 550 VZ KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Ahmed, Zobaer verfasserin (orcid)0000-0002-5341-363X aut Winter-time cover crop identification: A remote sensing-based methodological framework for new and rapid data generation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurately identifying and systematically mapping winter-time cover crops and their phenological characteristics offer significant benefits to agricultural producers and policymakers, as cover crops are one of several potential solutions to climate change mitigation. We present a methodological framework for identifying and mapping the presence of winter-time cover crops at the field level and aggregated to county scales from 2013 to 2019 by using the Google Earth Engine (GEE), a random forest classifier with time series data from Landsat 8, and yearly cover crop training data from the United States Department of Agriculture (USDA)-Natural Resources Conservation Service (NRCS). The methodology was tested with data from the Mississippi Alluvial Plain (MAP) region. Despite the inter-annual agronomic and climatic variations across space, results demonstrated an overall mean classification accuracy of 97.7%, with a kappa coefficient of 0.94. Results also revealed a 34% increase in model-predicted cover crop adoption in the study region from 2013 to 2019. Based on GEE, this study created, for the first time, a 30-m spatial and temporal resolution binary annual datasets and then aggregated them at the county level within the MAP study region. This multi-year novel dataset may improve our ability to anticipate and quantify the impact of summer crop production gains owing to cover crop adoption for extended periods and evaluate the adoption of cover crops on local soil ecosystems, biogeochemical cycles, and services. The methodology developed and tested broadly applies to other regions where cover crops have been promoted for climate-change mitigation and improving soil health for long-term sustainability. Agricultural producers, policymakers, and cost-share providers may use this information to develop agricultural conservation methods and land-use policies that minimize soil erosion and help mitigate climate change effects in the long run. Winter-time cover crops Remote sensing Methodological framework Random Forest classifier Google Earth Engine Landsat Nalley, Lawton verfasserin aut Brye, Kristofor verfasserin aut Steven Green, V. verfasserin aut Popp, Michael verfasserin aut Shew, Aaron M. verfasserin aut Connor, Lawson verfasserin aut Enthalten in International journal of applied earth observation and geoinformation Amsterdam [u.a.] : Elsevier Science, 1999 125 Online-Ressource (DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X 1872-826X nnns volume:125 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 38.03 Methoden und Techniken der Geowissenschaften VZ 74.48 Geoinformationssysteme VZ 74.41 Luftaufnahmen Photogrammetrie VZ AR 125 |
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10.1016/j.jag.2023.103564 doi (DE-627)ELV066161231 (ELSEVIER)S1569-8432(23)00388-6 DE-627 ger DE-627 rda eng 550 VZ KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Ahmed, Zobaer verfasserin (orcid)0000-0002-5341-363X aut Winter-time cover crop identification: A remote sensing-based methodological framework for new and rapid data generation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurately identifying and systematically mapping winter-time cover crops and their phenological characteristics offer significant benefits to agricultural producers and policymakers, as cover crops are one of several potential solutions to climate change mitigation. We present a methodological framework for identifying and mapping the presence of winter-time cover crops at the field level and aggregated to county scales from 2013 to 2019 by using the Google Earth Engine (GEE), a random forest classifier with time series data from Landsat 8, and yearly cover crop training data from the United States Department of Agriculture (USDA)-Natural Resources Conservation Service (NRCS). The methodology was tested with data from the Mississippi Alluvial Plain (MAP) region. Despite the inter-annual agronomic and climatic variations across space, results demonstrated an overall mean classification accuracy of 97.7%, with a kappa coefficient of 0.94. Results also revealed a 34% increase in model-predicted cover crop adoption in the study region from 2013 to 2019. Based on GEE, this study created, for the first time, a 30-m spatial and temporal resolution binary annual datasets and then aggregated them at the county level within the MAP study region. This multi-year novel dataset may improve our ability to anticipate and quantify the impact of summer crop production gains owing to cover crop adoption for extended periods and evaluate the adoption of cover crops on local soil ecosystems, biogeochemical cycles, and services. The methodology developed and tested broadly applies to other regions where cover crops have been promoted for climate-change mitigation and improving soil health for long-term sustainability. Agricultural producers, policymakers, and cost-share providers may use this information to develop agricultural conservation methods and land-use policies that minimize soil erosion and help mitigate climate change effects in the long run. Winter-time cover crops Remote sensing Methodological framework Random Forest classifier Google Earth Engine Landsat Nalley, Lawton verfasserin aut Brye, Kristofor verfasserin aut Steven Green, V. verfasserin aut Popp, Michael verfasserin aut Shew, Aaron M. verfasserin aut Connor, Lawson verfasserin aut Enthalten in International journal of applied earth observation and geoinformation Amsterdam [u.a.] : Elsevier Science, 1999 125 Online-Ressource (DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X 1872-826X nnns volume:125 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 38.03 Methoden und Techniken der Geowissenschaften VZ 74.48 Geoinformationssysteme VZ 74.41 Luftaufnahmen Photogrammetrie VZ AR 125 |
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10.1016/j.jag.2023.103564 doi (DE-627)ELV066161231 (ELSEVIER)S1569-8432(23)00388-6 DE-627 ger DE-627 rda eng 550 VZ KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Ahmed, Zobaer verfasserin (orcid)0000-0002-5341-363X aut Winter-time cover crop identification: A remote sensing-based methodological framework for new and rapid data generation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurately identifying and systematically mapping winter-time cover crops and their phenological characteristics offer significant benefits to agricultural producers and policymakers, as cover crops are one of several potential solutions to climate change mitigation. We present a methodological framework for identifying and mapping the presence of winter-time cover crops at the field level and aggregated to county scales from 2013 to 2019 by using the Google Earth Engine (GEE), a random forest classifier with time series data from Landsat 8, and yearly cover crop training data from the United States Department of Agriculture (USDA)-Natural Resources Conservation Service (NRCS). The methodology was tested with data from the Mississippi Alluvial Plain (MAP) region. Despite the inter-annual agronomic and climatic variations across space, results demonstrated an overall mean classification accuracy of 97.7%, with a kappa coefficient of 0.94. Results also revealed a 34% increase in model-predicted cover crop adoption in the study region from 2013 to 2019. Based on GEE, this study created, for the first time, a 30-m spatial and temporal resolution binary annual datasets and then aggregated them at the county level within the MAP study region. This multi-year novel dataset may improve our ability to anticipate and quantify the impact of summer crop production gains owing to cover crop adoption for extended periods and evaluate the adoption of cover crops on local soil ecosystems, biogeochemical cycles, and services. The methodology developed and tested broadly applies to other regions where cover crops have been promoted for climate-change mitigation and improving soil health for long-term sustainability. Agricultural producers, policymakers, and cost-share providers may use this information to develop agricultural conservation methods and land-use policies that minimize soil erosion and help mitigate climate change effects in the long run. Winter-time cover crops Remote sensing Methodological framework Random Forest classifier Google Earth Engine Landsat Nalley, Lawton verfasserin aut Brye, Kristofor verfasserin aut Steven Green, V. verfasserin aut Popp, Michael verfasserin aut Shew, Aaron M. verfasserin aut Connor, Lawson verfasserin aut Enthalten in International journal of applied earth observation and geoinformation Amsterdam [u.a.] : Elsevier Science, 1999 125 Online-Ressource (DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X 1872-826X nnns volume:125 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 38.03 Methoden und Techniken der Geowissenschaften VZ 74.48 Geoinformationssysteme VZ 74.41 Luftaufnahmen Photogrammetrie VZ AR 125 |
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10.1016/j.jag.2023.103564 doi (DE-627)ELV066161231 (ELSEVIER)S1569-8432(23)00388-6 DE-627 ger DE-627 rda eng 550 VZ KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Ahmed, Zobaer verfasserin (orcid)0000-0002-5341-363X aut Winter-time cover crop identification: A remote sensing-based methodological framework for new and rapid data generation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurately identifying and systematically mapping winter-time cover crops and their phenological characteristics offer significant benefits to agricultural producers and policymakers, as cover crops are one of several potential solutions to climate change mitigation. We present a methodological framework for identifying and mapping the presence of winter-time cover crops at the field level and aggregated to county scales from 2013 to 2019 by using the Google Earth Engine (GEE), a random forest classifier with time series data from Landsat 8, and yearly cover crop training data from the United States Department of Agriculture (USDA)-Natural Resources Conservation Service (NRCS). The methodology was tested with data from the Mississippi Alluvial Plain (MAP) region. Despite the inter-annual agronomic and climatic variations across space, results demonstrated an overall mean classification accuracy of 97.7%, with a kappa coefficient of 0.94. Results also revealed a 34% increase in model-predicted cover crop adoption in the study region from 2013 to 2019. Based on GEE, this study created, for the first time, a 30-m spatial and temporal resolution binary annual datasets and then aggregated them at the county level within the MAP study region. This multi-year novel dataset may improve our ability to anticipate and quantify the impact of summer crop production gains owing to cover crop adoption for extended periods and evaluate the adoption of cover crops on local soil ecosystems, biogeochemical cycles, and services. The methodology developed and tested broadly applies to other regions where cover crops have been promoted for climate-change mitigation and improving soil health for long-term sustainability. Agricultural producers, policymakers, and cost-share providers may use this information to develop agricultural conservation methods and land-use policies that minimize soil erosion and help mitigate climate change effects in the long run. Winter-time cover crops Remote sensing Methodological framework Random Forest classifier Google Earth Engine Landsat Nalley, Lawton verfasserin aut Brye, Kristofor verfasserin aut Steven Green, V. verfasserin aut Popp, Michael verfasserin aut Shew, Aaron M. verfasserin aut Connor, Lawson verfasserin aut Enthalten in International journal of applied earth observation and geoinformation Amsterdam [u.a.] : Elsevier Science, 1999 125 Online-Ressource (DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X 1872-826X nnns volume:125 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 38.03 Methoden und Techniken der Geowissenschaften VZ 74.48 Geoinformationssysteme VZ 74.41 Luftaufnahmen Photogrammetrie VZ AR 125 |
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Winter-time cover crop identification: A remote sensing-based methodological framework for new and rapid data generation |
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Winter-time cover crop identification: A remote sensing-based methodological framework for new and rapid data generation |
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Ahmed, Zobaer Nalley, Lawton Brye, Kristofor Steven Green, V. Popp, Michael Shew, Aaron M. Connor, Lawson |
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winter-time cover crop identification: a remote sensing-based methodological framework for new and rapid data generation |
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Winter-time cover crop identification: A remote sensing-based methodological framework for new and rapid data generation |
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Accurately identifying and systematically mapping winter-time cover crops and their phenological characteristics offer significant benefits to agricultural producers and policymakers, as cover crops are one of several potential solutions to climate change mitigation. We present a methodological framework for identifying and mapping the presence of winter-time cover crops at the field level and aggregated to county scales from 2013 to 2019 by using the Google Earth Engine (GEE), a random forest classifier with time series data from Landsat 8, and yearly cover crop training data from the United States Department of Agriculture (USDA)-Natural Resources Conservation Service (NRCS). The methodology was tested with data from the Mississippi Alluvial Plain (MAP) region. Despite the inter-annual agronomic and climatic variations across space, results demonstrated an overall mean classification accuracy of 97.7%, with a kappa coefficient of 0.94. Results also revealed a 34% increase in model-predicted cover crop adoption in the study region from 2013 to 2019. Based on GEE, this study created, for the first time, a 30-m spatial and temporal resolution binary annual datasets and then aggregated them at the county level within the MAP study region. This multi-year novel dataset may improve our ability to anticipate and quantify the impact of summer crop production gains owing to cover crop adoption for extended periods and evaluate the adoption of cover crops on local soil ecosystems, biogeochemical cycles, and services. The methodology developed and tested broadly applies to other regions where cover crops have been promoted for climate-change mitigation and improving soil health for long-term sustainability. Agricultural producers, policymakers, and cost-share providers may use this information to develop agricultural conservation methods and land-use policies that minimize soil erosion and help mitigate climate change effects in the long run. |
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Accurately identifying and systematically mapping winter-time cover crops and their phenological characteristics offer significant benefits to agricultural producers and policymakers, as cover crops are one of several potential solutions to climate change mitigation. We present a methodological framework for identifying and mapping the presence of winter-time cover crops at the field level and aggregated to county scales from 2013 to 2019 by using the Google Earth Engine (GEE), a random forest classifier with time series data from Landsat 8, and yearly cover crop training data from the United States Department of Agriculture (USDA)-Natural Resources Conservation Service (NRCS). The methodology was tested with data from the Mississippi Alluvial Plain (MAP) region. Despite the inter-annual agronomic and climatic variations across space, results demonstrated an overall mean classification accuracy of 97.7%, with a kappa coefficient of 0.94. Results also revealed a 34% increase in model-predicted cover crop adoption in the study region from 2013 to 2019. Based on GEE, this study created, for the first time, a 30-m spatial and temporal resolution binary annual datasets and then aggregated them at the county level within the MAP study region. This multi-year novel dataset may improve our ability to anticipate and quantify the impact of summer crop production gains owing to cover crop adoption for extended periods and evaluate the adoption of cover crops on local soil ecosystems, biogeochemical cycles, and services. The methodology developed and tested broadly applies to other regions where cover crops have been promoted for climate-change mitigation and improving soil health for long-term sustainability. Agricultural producers, policymakers, and cost-share providers may use this information to develop agricultural conservation methods and land-use policies that minimize soil erosion and help mitigate climate change effects in the long run. |
| abstract_unstemmed |
Accurately identifying and systematically mapping winter-time cover crops and their phenological characteristics offer significant benefits to agricultural producers and policymakers, as cover crops are one of several potential solutions to climate change mitigation. We present a methodological framework for identifying and mapping the presence of winter-time cover crops at the field level and aggregated to county scales from 2013 to 2019 by using the Google Earth Engine (GEE), a random forest classifier with time series data from Landsat 8, and yearly cover crop training data from the United States Department of Agriculture (USDA)-Natural Resources Conservation Service (NRCS). The methodology was tested with data from the Mississippi Alluvial Plain (MAP) region. Despite the inter-annual agronomic and climatic variations across space, results demonstrated an overall mean classification accuracy of 97.7%, with a kappa coefficient of 0.94. Results also revealed a 34% increase in model-predicted cover crop adoption in the study region from 2013 to 2019. Based on GEE, this study created, for the first time, a 30-m spatial and temporal resolution binary annual datasets and then aggregated them at the county level within the MAP study region. This multi-year novel dataset may improve our ability to anticipate and quantify the impact of summer crop production gains owing to cover crop adoption for extended periods and evaluate the adoption of cover crops on local soil ecosystems, biogeochemical cycles, and services. The methodology developed and tested broadly applies to other regions where cover crops have been promoted for climate-change mitigation and improving soil health for long-term sustainability. Agricultural producers, policymakers, and cost-share providers may use this information to develop agricultural conservation methods and land-use policies that minimize soil erosion and help mitigate climate change effects in the long run. |
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