基于MODIS与TM时序插补的省域尺度玉米遥感估产

针对省域尺度作物估产中的TM影像时相不一致和覆盖能力不足的问题，以山东省2008年玉米产量为研究对象，在6景不同玉米物候期的TM影像和长时间序列的MODIS全覆盖影像的支持下，构建基于玉米生长过程的时序插补模型，将不同物候期的TM影像插补为玉米乳熟期的同期数据集，并通过地面实割实测样本数据，建立地面-TM、TM-MODIS的两阶段遥感估产模型，开展省域尺度玉米产量全覆盖遥感估测方法研究。结果表明，基于时序插补的省域尺度玉米遥感估产方法能充分发挥TM和MODIS影像的各自优势，有效地避免TM影像时相不同所造

Maize yield estimation at province scale by interpolation of TM and MODIS time-series images

This study aims to overcome the shortage of the temporal variance and small coverage of TM images, which makes low accuracy for estimating the crop yield at province scale. The paper chose the maize yield in Shandong province in 2008 as the object of study. The data used in the paper mainly included six TM images with different phenophase of maize and the long time-series MODIS images with full coverage. The paper developed the time-series interpolating model based on the growth process of maize, which could interpolate the TM images with different phenophase into the dataset with the same milky maturity period of maize. Then through the in-situ measured samples of per unit area yield, the paper set up yield estimation models including ground-TM model and TM-MODIS model to obtain the full coverage yield information of maize at province scale. Results show that the method of yield estimation at province scale based on time-series interpolating model could make the most of the advantage of TM and MODIS data and avoid the regional disparity of NDVI derived from the temporal difference of TM images. The paper could reach high accuracy in the estimation of per unit area yield of maize in Shandong province. This will provide a new method to estimating crop yield at province scale.