基于 MODIS 指数和随机森林的江西省早稻种植信息提取.

Extracting planting information of rice as early as possible can be great significance in provincial agricultural production for the food security. MODIS data has been demonstrated to be superior in extracting planting information of rice at large scale due to short observation period and wide swath and easy image acquisition. The method of MODIS index during early growth period of rice combined with threshold value was usually used in the conventional provincial decision-making service to ensure the timeliness of service and convenient operation. However, the method is largely influenced by human and phenology difference of rice in various regions leading to highly subjective and poor stability. In addition, the mixed pixels were likely causing misestimation of the MODIS product. Random forest algorithm can make up the deficiency of threshold method due to the characteristics of less manual intervention and difficult over-fitting. In the study, an extraction method of rice planting information was proposed using MODIS index and random forest during early growth period of rice. Firstly, Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI) and Land Surface Water Index (LSWI) were selected to extract planting information based on the variation characteristics of MODIS index from seeding to jointing stage in Jiangxi Province, and then feature data sets were constructed to model. The models were built to extract planting area of early rice and to inverse planting abundance using random forest algorithm. Finally, the accuracy of the planting and the abundance maps of early rice were validated by the verification samples from the measured points, validation sample region obtained by the Sentinel-1A image and the statistical data from Jiangxi Provincial Bureau of Statistics. The results showed that using MODIS index during early growth period of rice and random forest was an effective way to extract plant information. The classification accuracy of early rice planting area extraction model was 93.18% with the Kappa coefficient of 0.915, and the mapping accuracy and user accuracy were 92.04% and 91.23%, respectively when matching the verification samples. Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), decision coefficient of abundance inversion model were 0.07, 0.104 and 0.855, respectively, and the better performance of the abundance inversion model was achieved in high abundance planting areas. The spatial distribution characteristics of early rice planting area and abundance were consistent with validation sample region. Compared with the statistical data, the model accuracy of early rice area was 92.33%. This method can ensure the timeliness of the service and solve the problem that the extraction of rice planting area in the conventional provincial decision-making service is greatly affected by the problems of rationality of threshold selection and mixed pixel, also have no complex operation. The finding can provide a reference to extract planting area of early rice during early growth period. [ABSTRACT FROM AUTHOR]