QUALITY CHECKING OF CROP CUTTING EXPERIMENTS USING REMOTE SENSING DATA: A CASE STUDY FOR RICE CROP IN ODISHA
Under Pradhan Mantri Fasal Bima Yojana (PMFBY), a large number of Crop Cutting Experiments (CCEs) were conducted by Odisha State for Kharif Rice in the year 2016 and 2017. The present study was carried out to examine the quality of the performed CCEs using statistical methods and Remote Sensing (RS)...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2019-07-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W6/461/2019/isprs-archives-XLII-3-W6-461-2019.pdf |
| Summary: | Under Pradhan Mantri Fasal Bima Yojana (PMFBY), a large number of Crop Cutting Experiments (CCEs) were conducted by Odisha State for Kharif Rice in the year 2016 and 2017. The present study was carried out to examine the quality of the performed CCEs using statistical methods and Remote Sensing (RS) technique. Total 24389 and 34725 CCEs were conducted. After removing outliers, 22083 and 26848 CCE points were analyzed for the year 2016 and 2017, respectively. Multi-date RISAT-1 (2016) and Sentinel-1A (2017) satellite data were used for generating the Kharif Rice crop mask, which was used to get NDVI and NDWI values for Rice pixels, from MODIS VI products. The values of these indices were divided into four strata from highest A, followed by B, C, and D (Lowest Value) based on the range (minimum and maximum) of values. The CCE based yield data were then divided into four yield strata of equal proportion. Yield and RS (NDVI+NDWI) based strata were combined to examine whether the CCE Points having high yield fall under good NDVI zone or vice versa. The results showed that there was strong match between CCE strata and the vegetation index strata in both the years. Therefore, it could be be concluded that RS based indices have the capability to assess the quality/accuracy of CCEs. Furthermore, the large variety of information available with CCEs such that crop variety, crop condition, water sources, stress conditions etc., can be used as input parameters to train any model to predict better results. |
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| ISSN: | 1682-1750 2194-9034 |