Phenology-pigment based automated peanut mapping using sentinel-2 images
Reliable spatiotemporal crop data are vital for sustainable agricultural management. However, efficient algorithms that can be automatically applied to large regions are scarce, especially for cash crops, since it is hard to distinguish their uniqueness merely from temporal profiles of traditional v...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2021-11-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/15481603.2021.1987005 |
| _version_ | 1827811689861480448 |
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| author | Bingwen Qiu Fanchen Jiang Chongcheng Chen Zhenghong Tang Wenbin Wu Joe Berry |
| author_facet | Bingwen Qiu Fanchen Jiang Chongcheng Chen Zhenghong Tang Wenbin Wu Joe Berry |
| author_sort | Bingwen Qiu |
| collection | DOAJ |
| description | Reliable spatiotemporal crop data are vital for sustainable agricultural management. However, efficient algorithms that can be automatically applied to large regions are scarce, especially for cash crops, since it is hard to distinguish their uniqueness merely from temporal profiles of traditional vegetation indices. The efficiency of knowledge-based temporal features and red-edge pigment indices in characterizing crop growth has been reported in the literature, but the potential of combined applications in identifying crops has not been validated yet. This study fills this gap by developing a knowledge-based automated Peanut mapping Algorithm with a combined consideration of crop Phenology and Pigment content variations (PAPP). Peanut crop has earlier and longer flowering stages compared to other crops such as paddy rice and maize. Peanut fields are distinguished with less variations in anthocyanin and chlorophyll as well as higher carotenoid concentrations. Herein, three phenology and pigment-based indicators were proposed for peanut mapping by exploring the concentration and variations of the chlorophyll, anthocyanin and carotenoid indices, respectively. This PAPP algorithm was validated over large regions (around 250 thousand km2 cropland) covering three provinces of Northeast China using Sentinel-2 time-series images. The results reported that there was 8,371 km2 peanut area in Northeast China in 2018, concentrated in the western Jilin and Liaoning provinces. Validation from the 1,102 field survey sites revealed overall accuracies of 94%, with a kappa index of 0.87 and F1 score of 0.91. The PAPP algorithm was not sensitive to thresholding, and a high classification accuracy could be obtained once the threshold of one indicator was roughly defined. The thresholds could be determined based on the proportions of staple crops (i.e. paddy rice and maize) using the historical agricultural statistical data since peanut fields either show the least or largest values in these three proposed indicators. The PAPP algorithm demonstrates the capabilities of automatic peanut mapping over large regions with no requirements of further training and modifications. This study makes contributions to a sustainable agricultural management society given the potential significant role of legume crops in co-delivering food security and adapting to climate change. |
| first_indexed | 2024-03-11T23:08:09Z |
| format | Article |
| id | doaj.art-1cfe1891061543428f8589bedff1b193 |
| institution | Directory Open Access Journal |
| issn | 1548-1603 1943-7226 |
| language | English |
| last_indexed | 2024-03-11T23:08:09Z |
| publishDate | 2021-11-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj.art-1cfe1891061543428f8589bedff1b1932023-09-21T12:43:07ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262021-11-015881335135110.1080/15481603.2021.19870051987005Phenology-pigment based automated peanut mapping using sentinel-2 imagesBingwen Qiu0Fanchen Jiang1Chongcheng Chen2Zhenghong Tang3Wenbin Wu4Joe Berry5Fuzhou UniversityFuzhou UniversityFuzhou UniversityUniversity of Nebraska-LincolnMinistry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural SciencesCarnegie Institution for ScienceReliable spatiotemporal crop data are vital for sustainable agricultural management. However, efficient algorithms that can be automatically applied to large regions are scarce, especially for cash crops, since it is hard to distinguish their uniqueness merely from temporal profiles of traditional vegetation indices. The efficiency of knowledge-based temporal features and red-edge pigment indices in characterizing crop growth has been reported in the literature, but the potential of combined applications in identifying crops has not been validated yet. This study fills this gap by developing a knowledge-based automated Peanut mapping Algorithm with a combined consideration of crop Phenology and Pigment content variations (PAPP). Peanut crop has earlier and longer flowering stages compared to other crops such as paddy rice and maize. Peanut fields are distinguished with less variations in anthocyanin and chlorophyll as well as higher carotenoid concentrations. Herein, three phenology and pigment-based indicators were proposed for peanut mapping by exploring the concentration and variations of the chlorophyll, anthocyanin and carotenoid indices, respectively. This PAPP algorithm was validated over large regions (around 250 thousand km2 cropland) covering three provinces of Northeast China using Sentinel-2 time-series images. The results reported that there was 8,371 km2 peanut area in Northeast China in 2018, concentrated in the western Jilin and Liaoning provinces. Validation from the 1,102 field survey sites revealed overall accuracies of 94%, with a kappa index of 0.87 and F1 score of 0.91. The PAPP algorithm was not sensitive to thresholding, and a high classification accuracy could be obtained once the threshold of one indicator was roughly defined. The thresholds could be determined based on the proportions of staple crops (i.e. paddy rice and maize) using the historical agricultural statistical data since peanut fields either show the least or largest values in these three proposed indicators. The PAPP algorithm demonstrates the capabilities of automatic peanut mapping over large regions with no requirements of further training and modifications. This study makes contributions to a sustainable agricultural management society given the potential significant role of legume crops in co-delivering food security and adapting to climate change.http://dx.doi.org/10.1080/15481603.2021.1987005automated classificationsentinel-2 imagespigment indicesgoogle earth enginelegume crop |
| spellingShingle | Bingwen Qiu Fanchen Jiang Chongcheng Chen Zhenghong Tang Wenbin Wu Joe Berry Phenology-pigment based automated peanut mapping using sentinel-2 images GIScience & Remote Sensing automated classification sentinel-2 images pigment indices google earth engine legume crop |
| title | Phenology-pigment based automated peanut mapping using sentinel-2 images |
| title_full | Phenology-pigment based automated peanut mapping using sentinel-2 images |
| title_fullStr | Phenology-pigment based automated peanut mapping using sentinel-2 images |
| title_full_unstemmed | Phenology-pigment based automated peanut mapping using sentinel-2 images |
| title_short | Phenology-pigment based automated peanut mapping using sentinel-2 images |
| title_sort | phenology pigment based automated peanut mapping using sentinel 2 images |
| topic | automated classification sentinel-2 images pigment indices google earth engine legume crop |
| url | http://dx.doi.org/10.1080/15481603.2021.1987005 |
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