Remote Sensing Index for Mapping Canola Flowers Using MODIS Data
Mapping and tracing the changes in canola planting areas and yields in China are of great significance for macro-policy regulation and national food security. The bright yellow flower is a distinctive feature of canola, compared to other crops, and is also an important factor in predicting canola yi...
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| Format: | Article |
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MDPI AG
2020-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/23/3912 |
| _version_ | 1797546329262522368 |
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| author | Yunze Zang Xuehong Chen Jin Chen Yugang Tian Yusheng Shi Xin Cao Xihong Cui |
| author_facet | Yunze Zang Xuehong Chen Jin Chen Yugang Tian Yusheng Shi Xin Cao Xihong Cui |
| author_sort | Yunze Zang |
| collection | DOAJ |
| description | Mapping and tracing the changes in canola planting areas and yields in China are of great significance for macro-policy regulation and national food security. The bright yellow flower is a distinctive feature of canola, compared to other crops, and is also an important factor in predicting canola yield. Thus, yellowness indices were previously used to detect the canola flower using aerial imagery or median-resolution satellite data like Sentinel-2. However, it remains challenging to map the canola planting area and to trace long-term canola yields in China due to the wide areal extent of cultivation, different flowering periods in different locations and years, and the lack of high spatial resolution data within a long-term period. In this study, a novel canola index, called the enhanced area yellowness index (EAYI), for mapping canola flowers and based on Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data, was developed. There are two improvements in the EAYI compared with previous studies. First, a method for estimating flowering period, based on geolocation and normalized difference vegetation index (NDVI) time-series, was established, to estimate the flowering period at each place in each year. Second, the EAYI enhances the weak flower signal in coarse pixels by combining the peak of yellowness index time-series and the valley of NDVI time-series during the estimated flowering period. With the proposed EAYI, canola flowering was mapped in five typical canola planting areas in China, during 2003-2017. Three different canola indices proposed previously, the normalized difference yellowness index (NDYI), ratio yellowness index (RYI) and Ashourloo canola index (Ashourloo CI), were also calculated for a comparison. Validation using the samples interpreted through higher resolution images demonstrated that the EAYI is better correlated with the reference canola coverage with R<sup>2</sup> ranged from 0.31 to 0.70, compared to the previous indices with R<sup>2</sup> ranged from 0.02 to 0.43. Compared with census canola yield data, the total EAYI was well correlated with actual yield in Jingmen, Yili and Hulun Buir, and well correlated with meteorological yields in all five study areas. In contrast, previous canola indices show a very low or even a negative correlation with both actual and meteorological yields. These results indicate that the EAYI is a potential index for mapping and tracing the change in canola areas, or yields, with MODIS data. |
| first_indexed | 2024-03-10T14:28:16Z |
| format | Article |
| id | doaj.art-88cb66d38ec74c9e8ef05b39117b1321 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T14:28:16Z |
| publishDate | 2020-11-01 |
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| series | Remote Sensing |
| spelling | doaj.art-88cb66d38ec74c9e8ef05b39117b13212023-11-20T22:47:45ZengMDPI AGRemote Sensing2072-42922020-11-011223391210.3390/rs12233912Remote Sensing Index for Mapping Canola Flowers Using MODIS DataYunze Zang0Xuehong Chen1Jin Chen2Yugang Tian3Yusheng Shi4Xin Cao5Xihong Cui6State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, ChinaState Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaMapping and tracing the changes in canola planting areas and yields in China are of great significance for macro-policy regulation and national food security. The bright yellow flower is a distinctive feature of canola, compared to other crops, and is also an important factor in predicting canola yield. Thus, yellowness indices were previously used to detect the canola flower using aerial imagery or median-resolution satellite data like Sentinel-2. However, it remains challenging to map the canola planting area and to trace long-term canola yields in China due to the wide areal extent of cultivation, different flowering periods in different locations and years, and the lack of high spatial resolution data within a long-term period. In this study, a novel canola index, called the enhanced area yellowness index (EAYI), for mapping canola flowers and based on Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data, was developed. There are two improvements in the EAYI compared with previous studies. First, a method for estimating flowering period, based on geolocation and normalized difference vegetation index (NDVI) time-series, was established, to estimate the flowering period at each place in each year. Second, the EAYI enhances the weak flower signal in coarse pixels by combining the peak of yellowness index time-series and the valley of NDVI time-series during the estimated flowering period. With the proposed EAYI, canola flowering was mapped in five typical canola planting areas in China, during 2003-2017. Three different canola indices proposed previously, the normalized difference yellowness index (NDYI), ratio yellowness index (RYI) and Ashourloo canola index (Ashourloo CI), were also calculated for a comparison. Validation using the samples interpreted through higher resolution images demonstrated that the EAYI is better correlated with the reference canola coverage with R<sup>2</sup> ranged from 0.31 to 0.70, compared to the previous indices with R<sup>2</sup> ranged from 0.02 to 0.43. Compared with census canola yield data, the total EAYI was well correlated with actual yield in Jingmen, Yili and Hulun Buir, and well correlated with meteorological yields in all five study areas. In contrast, previous canola indices show a very low or even a negative correlation with both actual and meteorological yields. These results indicate that the EAYI is a potential index for mapping and tracing the change in canola areas, or yields, with MODIS data.https://www.mdpi.com/2072-4292/12/23/3912canola flower mappingenhanced area yellowness index (EAYI)Moderate Resolution Imaging Spectroradiometer (MODIS) |
| spellingShingle | Yunze Zang Xuehong Chen Jin Chen Yugang Tian Yusheng Shi Xin Cao Xihong Cui Remote Sensing Index for Mapping Canola Flowers Using MODIS Data Remote Sensing canola flower mapping enhanced area yellowness index (EAYI) Moderate Resolution Imaging Spectroradiometer (MODIS) |
| title | Remote Sensing Index for Mapping Canola Flowers Using MODIS Data |
| title_full | Remote Sensing Index for Mapping Canola Flowers Using MODIS Data |
| title_fullStr | Remote Sensing Index for Mapping Canola Flowers Using MODIS Data |
| title_full_unstemmed | Remote Sensing Index for Mapping Canola Flowers Using MODIS Data |
| title_short | Remote Sensing Index for Mapping Canola Flowers Using MODIS Data |
| title_sort | remote sensing index for mapping canola flowers using modis data |
| topic | canola flower mapping enhanced area yellowness index (EAYI) Moderate Resolution Imaging Spectroradiometer (MODIS) |
| url | https://www.mdpi.com/2072-4292/12/23/3912 |
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