Historic maps showing the temporal distribution of rice fields are important for precision agriculture,irrigation optimisation,forecasting crop yields,land use management and formulating policies.However,mapping rice ...Historic maps showing the temporal distribution of rice fields are important for precision agriculture,irrigation optimisation,forecasting crop yields,land use management and formulating policies.However,mapping rice felds using traditional ground surveys is impractical when high cost,time and labour requirements are considered,and the availability of such detailed records is limited.Although satellite remote sensing appears to be a viable solution,conventional segmentation and classification methods with spectral bands are often unable to contrast the distinct characteristics between rice fields and other vegetation classes.To this end,we explored a novel,Google Earth Engine(GEE)based multiindex random forest(RF)classification approach to map rice fields over two decades.Landsat images from 2000 to 2020 of two Sri Lankan rice cultivation districts were extracted from GEE and a multi-index RF classification algorithm was applied to distinguish the rice fields.The results showed above 80%accuracy for both training and validation,when compared against high spatial resolution Google Earth imagery.In essence,multi-index sampling and RF together synergised the compelling classifcation accuracy by effectively capturing vegetation,water(ponding)and soil characteristics unique to the rice felds using a single-click approach.The maps developed in this study were further compared against the MODIS land cover type product(MCD12Q1)and the corresponding superior statistics on rice fields demonstrated the robustness of the proposed approach.Future work seeking effective index combinations is recommended,and this approach can potentially be extended to other crop analyses elsewhere.展开更多
文摘Historic maps showing the temporal distribution of rice fields are important for precision agriculture,irrigation optimisation,forecasting crop yields,land use management and formulating policies.However,mapping rice felds using traditional ground surveys is impractical when high cost,time and labour requirements are considered,and the availability of such detailed records is limited.Although satellite remote sensing appears to be a viable solution,conventional segmentation and classification methods with spectral bands are often unable to contrast the distinct characteristics between rice fields and other vegetation classes.To this end,we explored a novel,Google Earth Engine(GEE)based multiindex random forest(RF)classification approach to map rice fields over two decades.Landsat images from 2000 to 2020 of two Sri Lankan rice cultivation districts were extracted from GEE and a multi-index RF classification algorithm was applied to distinguish the rice fields.The results showed above 80%accuracy for both training and validation,when compared against high spatial resolution Google Earth imagery.In essence,multi-index sampling and RF together synergised the compelling classifcation accuracy by effectively capturing vegetation,water(ponding)and soil characteristics unique to the rice felds using a single-click approach.The maps developed in this study were further compared against the MODIS land cover type product(MCD12Q1)and the corresponding superior statistics on rice fields demonstrated the robustness of the proposed approach.Future work seeking effective index combinations is recommended,and this approach can potentially be extended to other crop analyses elsewhere.
