Prior Season Crop Type Masks for Winter Wheat Yield Forecasting: A US Case Study
Monitoring and forecasting crop yields is a critical component of understanding and better addressing global food security challenges. Detailed spatial information on crop-type distribution is fundamental for in-season crop condition monitoring and yields forecasting over large agricultural areas, a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-10-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/10/10/1659 |
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| author | Inbal Becker-Reshef Belen Franch Brian Barker Emilie Murphy Andres Santamaria-Artigas Michael Humber Sergii Skakun Eric Vermote |
| author_facet | Inbal Becker-Reshef Belen Franch Brian Barker Emilie Murphy Andres Santamaria-Artigas Michael Humber Sergii Skakun Eric Vermote |
| author_sort | Inbal Becker-Reshef |
| collection | DOAJ |
| description | Monitoring and forecasting crop yields is a critical component of understanding and better addressing global food security challenges. Detailed spatial information on crop-type distribution is fundamental for in-season crop condition monitoring and yields forecasting over large agricultural areas, as it enables the extraction of crop-specific signals. Yet, the availability of such data within the growing season is often limited. Within this context, this study seeks to develop a practical approach to extract a crop-specific signal for yield forecasting in cases where crop rotations are prevalent, and detailed in-season information on crop type distribution is not available. We investigated the possibility of accurately forecasting winter wheat yields by using a counter-intuitive approach, which coarsens the spatial resolution of out-of-date detailed winter wheat masks and uses them in combination with easily accessibly coarse spatial resolution remotely sensed time series data. The main idea is to explore an optimal spatial resolution at which crop type changes will be negligible due to crop rotation (so a previous seasons’ mask, which is more readily available can be used) and an informative signal can be extracted, so it can be correlated to crop yields. The study was carried out in the United States of America (USA) and utilized multiple years of NASA Moderate Resolution Imaging Spectroradiometer (MODIS) data, US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) detailed wheat masks, and a regression-based winter wheat yield model. The results indicate that, in places where crop rotations were prevalent, coarsening the spatial scale of a crop type mask from the previous season resulted in a constant per-pixel wheat proportion over multiple seasons. This enables the consistent extraction of a crop-specific vegetation index time series that can be used for in-season monitoring and yield estimation over multiple years using a single mask. In the case of the USA, using a moderate resolution crop type mask from a previous season aggregated to 5 km resolution, resulted in a 0.7% tradeoff in accuracy relative to the control case where annually-updated detailed crop-type masks were available. These findings suggest that when detailed in-season data is not available, winter wheat yield can be accurately forecasted (within 10%) prior to harvest using a single, prior season crop mask and coarse resolution Normalized Difference Vegetation Index (NDVI) time series data. |
| first_indexed | 2024-12-13T10:20:36Z |
| format | Article |
| id | doaj.art-00067d99ef8f419db8a0d6f690163896 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-13T10:20:36Z |
| publishDate | 2018-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-00067d99ef8f419db8a0d6f6901638962022-12-21T23:51:12ZengMDPI AGRemote Sensing2072-42922018-10-011010165910.3390/rs10101659rs10101659Prior Season Crop Type Masks for Winter Wheat Yield Forecasting: A US Case StudyInbal Becker-Reshef0Belen Franch1Brian Barker2Emilie Murphy3Andres Santamaria-Artigas4Michael Humber5Sergii Skakun6Eric Vermote7Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USADepartment of Geographical Sciences, University of Maryland, College Park, MD 20742, USADepartment of Geographical Sciences, University of Maryland, College Park, MD 20742, USADepartment of Geographical Sciences, University of Maryland, College Park, MD 20742, USADepartment of Geographical Sciences, University of Maryland, College Park, MD 20742, USADepartment of Geographical Sciences, University of Maryland, College Park, MD 20742, USADepartment of Geographical Sciences, University of Maryland, College Park, MD 20742, USANASA Goddard Space Flight Center, Greenbelt, MD 20771, USAMonitoring and forecasting crop yields is a critical component of understanding and better addressing global food security challenges. Detailed spatial information on crop-type distribution is fundamental for in-season crop condition monitoring and yields forecasting over large agricultural areas, as it enables the extraction of crop-specific signals. Yet, the availability of such data within the growing season is often limited. Within this context, this study seeks to develop a practical approach to extract a crop-specific signal for yield forecasting in cases where crop rotations are prevalent, and detailed in-season information on crop type distribution is not available. We investigated the possibility of accurately forecasting winter wheat yields by using a counter-intuitive approach, which coarsens the spatial resolution of out-of-date detailed winter wheat masks and uses them in combination with easily accessibly coarse spatial resolution remotely sensed time series data. The main idea is to explore an optimal spatial resolution at which crop type changes will be negligible due to crop rotation (so a previous seasons’ mask, which is more readily available can be used) and an informative signal can be extracted, so it can be correlated to crop yields. The study was carried out in the United States of America (USA) and utilized multiple years of NASA Moderate Resolution Imaging Spectroradiometer (MODIS) data, US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) detailed wheat masks, and a regression-based winter wheat yield model. The results indicate that, in places where crop rotations were prevalent, coarsening the spatial scale of a crop type mask from the previous season resulted in a constant per-pixel wheat proportion over multiple seasons. This enables the consistent extraction of a crop-specific vegetation index time series that can be used for in-season monitoring and yield estimation over multiple years using a single mask. In the case of the USA, using a moderate resolution crop type mask from a previous season aggregated to 5 km resolution, resulted in a 0.7% tradeoff in accuracy relative to the control case where annually-updated detailed crop-type masks were available. These findings suggest that when detailed in-season data is not available, winter wheat yield can be accurately forecasted (within 10%) prior to harvest using a single, prior season crop mask and coarse resolution Normalized Difference Vegetation Index (NDVI) time series data.http://www.mdpi.com/2072-4292/10/10/1659agricultural monitoringyieldcrop masks |
| spellingShingle | Inbal Becker-Reshef Belen Franch Brian Barker Emilie Murphy Andres Santamaria-Artigas Michael Humber Sergii Skakun Eric Vermote Prior Season Crop Type Masks for Winter Wheat Yield Forecasting: A US Case Study Remote Sensing agricultural monitoring yield crop masks |
| title | Prior Season Crop Type Masks for Winter Wheat Yield Forecasting: A US Case Study |
| title_full | Prior Season Crop Type Masks for Winter Wheat Yield Forecasting: A US Case Study |
| title_fullStr | Prior Season Crop Type Masks for Winter Wheat Yield Forecasting: A US Case Study |
| title_full_unstemmed | Prior Season Crop Type Masks for Winter Wheat Yield Forecasting: A US Case Study |
| title_short | Prior Season Crop Type Masks for Winter Wheat Yield Forecasting: A US Case Study |
| title_sort | prior season crop type masks for winter wheat yield forecasting a us case study |
| topic | agricultural monitoring yield crop masks |
| url | http://www.mdpi.com/2072-4292/10/10/1659 |
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