Monitoring Double-Cropped Extent with Remote Sensing in Areas with High Crop Diversity
The extent of single- and multi-cropping systems in any region, as well as potential changes to them, has consequences on food security and land- and water-resource use, raising important management questions. However, addressing these questions is limited by a lack of reliable data on multi-croppin...
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| Format: | Article |
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MDPI AG
2025-04-01
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| Series: | Plants |
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| Online Access: | https://www.mdpi.com/2223-7747/14/9/1362 |
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| author | Hossein Noorazar Michael P. Brady Supriya Savalkar Amin Norouzi Kandelati Mingliang Liu Perry Beale Andrew M. McGuire Timothy Waters Kirti Rajagopalan |
| author_facet | Hossein Noorazar Michael P. Brady Supriya Savalkar Amin Norouzi Kandelati Mingliang Liu Perry Beale Andrew M. McGuire Timothy Waters Kirti Rajagopalan |
| author_sort | Hossein Noorazar |
| collection | DOAJ |
| description | The extent of single- and multi-cropping systems in any region, as well as potential changes to them, has consequences on food security and land- and water-resource use, raising important management questions. However, addressing these questions is limited by a lack of reliable data on multi-cropping practices at a high spatial resolution, especially in areas with high crop diversity. In this paper, we develop and apply a relatively low-cost and scalable method to identify double-cropping at the field scale using satellite (Landsat) imagery. The process combines machine learning methods with expert labeling. The process evaluates multiple machine learning methods, including an image classification of a time-series, trained on data where cropping intensity labels were created by experts who are familiar with regional production practices. We demonstrate the process by measuring double-cropping extent in a part of Washington State in the Pacific Northwest United States—an arid region with cold winters and hot summers with significant production of more than 60 distinct types of crops including hay, fruits, vegetables, and grains in irrigated settings. Our results indicate that the current state-of-the-art methods for identifying cropping intensity—which apply simpler rule-based thresholds on vegetation indices—do not work well in regions with a high crop diversity and likely significantly overestimate double-cropped extent. Multiple machine learning models were applied on Landsat-derived vegetation index time-series data and were able to perform better by capturing nuances that the simple rule-based approaches are unable to. In particular, our (image-based) deep learning model was able to capture nuances in this crop-diverse environment and achieve a high accuracy (96–99% overall accuracy and 83–93% producer accuracy for the double-cropped class with a standard error of less than 2.5%) while also identifying double-cropping in the right crop types and locations based on expert knowledge. Our expert labeling process worked well and has potential as a relatively low-cost, scalable approach for remote sensing applications. The product developed here is valuable for the long-term monitoring of double-cropped extent and for informing several policy questions related to food production and resource use. |
| format | Article |
| id | doaj-art-1fa56c4db2c8452fbb0e7054ba846b0a |
| institution | Kabale University |
| issn | 2223-7747 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plants |
| spelling | doaj-art-1fa56c4db2c8452fbb0e7054ba846b0a2025-08-20T03:52:56ZengMDPI AGPlants2223-77472025-04-01149136210.3390/plants14091362Monitoring Double-Cropped Extent with Remote Sensing in Areas with High Crop DiversityHossein Noorazar0Michael P. Brady1Supriya Savalkar2Amin Norouzi Kandelati3Mingliang Liu4Perry Beale5Andrew M. McGuire6Timothy Waters7Kirti Rajagopalan8Department of Biological Systems Engineering, Washington State University, Pullman, WA 99164, USASchool of Economic Sciences, Washington State University, Pullman, WA 99164, USADepartment of Biological Systems Engineering, Washington State University, Pullman, WA 99164, USADepartment of Biological Systems Engineering, Washington State University, Pullman, WA 99164, USADepartment of Civil and Environmental Engineering, Washington State University, Pullman, WA 99352, USAWashington State Department of Agriculture, Yakima, WA 98504, USAAgriculture and Natural Resources Extension Program, Washington State University, Moses Lake, WA 98837, USAAgriculture and Natural Resources Extension Program, Washington State University, Pasco, WA 99301, USADepartment of Biological Systems Engineering, Washington State University, Pullman, WA 99164, USAThe extent of single- and multi-cropping systems in any region, as well as potential changes to them, has consequences on food security and land- and water-resource use, raising important management questions. However, addressing these questions is limited by a lack of reliable data on multi-cropping practices at a high spatial resolution, especially in areas with high crop diversity. In this paper, we develop and apply a relatively low-cost and scalable method to identify double-cropping at the field scale using satellite (Landsat) imagery. The process combines machine learning methods with expert labeling. The process evaluates multiple machine learning methods, including an image classification of a time-series, trained on data where cropping intensity labels were created by experts who are familiar with regional production practices. We demonstrate the process by measuring double-cropping extent in a part of Washington State in the Pacific Northwest United States—an arid region with cold winters and hot summers with significant production of more than 60 distinct types of crops including hay, fruits, vegetables, and grains in irrigated settings. Our results indicate that the current state-of-the-art methods for identifying cropping intensity—which apply simpler rule-based thresholds on vegetation indices—do not work well in regions with a high crop diversity and likely significantly overestimate double-cropped extent. Multiple machine learning models were applied on Landsat-derived vegetation index time-series data and were able to perform better by capturing nuances that the simple rule-based approaches are unable to. In particular, our (image-based) deep learning model was able to capture nuances in this crop-diverse environment and achieve a high accuracy (96–99% overall accuracy and 83–93% producer accuracy for the double-cropped class with a standard error of less than 2.5%) while also identifying double-cropping in the right crop types and locations based on expert knowledge. Our expert labeling process worked well and has potential as a relatively low-cost, scalable approach for remote sensing applications. The product developed here is valuable for the long-term monitoring of double-cropped extent and for informing several policy questions related to food production and resource use.https://www.mdpi.com/2223-7747/14/9/1362double-/multi-croppingcropping intensityLandsatNDVIremote sensingmachine learning |
| spellingShingle | Hossein Noorazar Michael P. Brady Supriya Savalkar Amin Norouzi Kandelati Mingliang Liu Perry Beale Andrew M. McGuire Timothy Waters Kirti Rajagopalan Monitoring Double-Cropped Extent with Remote Sensing in Areas with High Crop Diversity Plants double-/multi-cropping cropping intensity Landsat NDVI remote sensing machine learning |
| title | Monitoring Double-Cropped Extent with Remote Sensing in Areas with High Crop Diversity |
| title_full | Monitoring Double-Cropped Extent with Remote Sensing in Areas with High Crop Diversity |
| title_fullStr | Monitoring Double-Cropped Extent with Remote Sensing in Areas with High Crop Diversity |
| title_full_unstemmed | Monitoring Double-Cropped Extent with Remote Sensing in Areas with High Crop Diversity |
| title_short | Monitoring Double-Cropped Extent with Remote Sensing in Areas with High Crop Diversity |
| title_sort | monitoring double cropped extent with remote sensing in areas with high crop diversity |
| topic | double-/multi-cropping cropping intensity Landsat NDVI remote sensing machine learning |
| url | https://www.mdpi.com/2223-7747/14/9/1362 |
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