Unmixed Pixel Method by Using Landsat and MODIS Data Fusion for Detecting Rice Crop Calendar in Small Paddy Fields

Name: Atittaya Russameejaem

ID: – 

Title: Unmixed Pixel Method by Using Landsat and MODIS Data Fusion for Detecting Rice Crop Calendar in Small Paddy Fields

Type: Thesis


In precise farming, monitoring rice growth requires satellite images which are high temporal data, such as MODIS time series data. However, they are low spatial-resolution data which are not suitable to monitor rice growth in small paddy field. As a result, the mixed pixel problem occurs. The aim of this study was investigate the possibility for generating crop calendar in small paddy fields that mixed pixel problem occurred. The spatial and temporal adaptive reflectance fusion model or STARFM algorithm was applied for solving mixed pixel problem in this study. This method blended pure LANDSAT and MODIS mixed pixel surface reflectance in order to estimate NDVI of paddy fields where located in MODIS mixed pixel. This algorithm was tested on small paddy fields where was located in the central regain of Thailand. The result showed STARFM algorithm was high potential to solve mixed pixel problem in small paddy fields. This algorithm can estimate NDVI of paddy fields in mixed pixel problem which was both heterogeneous and homogenous land cover types. However, the accuracy of estimated NDVI depended on percentage coverage of paddy field in MODIS mixed pixel. If percentage coverage of paddy is high, the accuracy of estimated NDVI will be enhanced. The accuracy of estimated NDVI from STARFM algorithm was validated by farmer information about sowing and harvesting period. Most of result showed that the period of sowing and harvesting period from estimated NDVI was similar with farmer information; however, some case error around 8 days. Moreover, this study found that LANDSAT scenes should available at begin, middle and end of rice growing season in case of generating crop calendar; in order to improve the accuracy of estimated NDVI.

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