关键词: 作物模型, 作物模型, 监测实验, 采样间隔, 预测误差

Abstract: We selected Dabie Mountain in Anqing of Anhui Province as the study area, including Taihu, Yuexi, Susong and Qianshan, and analyzed the rice planting area distribution, and conducted the dynamic realtime monitoring of rice planting area by 6 Landsat 8 satellite remote sensing images from 2016 and 2017. We extracted the planting area of late rice in the study area by remote sensing interpretation method, and verified the accuracy by random point test and Kappa coefficient. The results showed that: the extraction accuracy of rice planting area in 2017 and 2016 was 93.44%, 93.78%, respectively, and the Kappa coefficient was 0.86, 0.83, respectively, proving that the extraction result was accurate; we compared the planting area of late rice in 2017 and 2016, found that the change rate of rice planting area in Taihu, Qianshan and Yuexi was less than 5%, which was a normal one; since some farmland in Susong was flooded in 2016, so its rice planting area in 2017 increased by 13.01% . Compared with traditional statistical methods of crop acreage, the method of remote sensing can save manpower, material and financial resources, and can achieve real-time and dynamic monitoring of crops accurately and quickly.