综合季相节律和特征光谱的冬小麦种植面积遥感估算

及时准确地获取区域和国家尺度的作物种植面积和空间分布具有重要意义。针对目前中低分辨率遥感数据相结合方法的局限，提出一种新的作物类型识别方法。首先基于MODIS NDVI数据的时间优势，提取研究区各类植被的NDVI时间序列曲线，从而分析冬小麦在季相节律上的识别特征，构建冬小麦识别模型。再将MODIS像元分类处理，纯耕地像元利用冬小麦的季相节律特征识别；耕地与其他植被的混合像元利用混合像元分解的思想提取耕地组分的NDVI时间序列，从而进行识别，进一步根据空间关系将识别结果重新定位到中分辨率尺度上；冬小麦与其他作物的混合像元覆盖区则利用TM遥感影像的光谱差异加以区分。在伊洛河流域主要农业区，以冬小麦为识别对象，结果表明识别精度达到96.3%。该方法为作物种植信息的提取提供了新的解决问题的途径，也对其他类型作物的识别也具有重要的参考价值。

Remote sensing estimation of planting area for winter wheat by integrating seasonal rhythms and spectral characteristics

Abstract: Research on winter wheat has an important significance for timely and accurately obtaining the crop acreage and their spatial distribution at regional and national scales. In traditional methods combining medium-resolution and low-resolution remote sensing data, only the area percentage of crops in a low-resolution pixel is extracted, thus the crop area is obtained. For this limitation, this paper proposes a new crop identification method. The land cover of the study area is summarized in six categories (farmland, forestland, shrub land, grassland, waters, and other). Each type of land cover's purity is calculated in the corresponding MODIS pixel. First, NDVI time series curves are extracted for various types of land cover based on MODIS time advantage, analyzed for identifying characteristics of winter wheat on the seasonal rhythm, and used to build the identification model. Then, MODIS pixels are classified based on the purity of farmland, including farmland pure pixel, other crop pure pixel, mixed pixel from farmland and other land cover, mixed pixel from winter wheat and other crops, and other pixel. The MODIS pixels involving winter wheat include three types, i.e. the farmland pure pixel, mixed pixel from farmland and other land cover, mixed pixel from winter wheat, and/or other crops. For the farmland pure pixels, the winter wheat is identified according to seasonal characteristics of winter wheat. For the mixed pixel from farmland and other land cover, their sub-pixel NDVI time series are extracted based on the pixel un-mixing method, in order to identify whether the sub-pixel belongs to winter wheat. Further, the identification results are repositioned to the medium-resolution scales according to the spatial relationship. The mixed pixels area from winter wheat and other crops are identified based on spectral differences of Landsat TM remote sensing images. Finally, these three types of identified results can be integrated into the medium-resolution scales. In this paper, the winter wheat identified method is applied to the dominating agricultural area of the Yiluo basin. A total of 11 016 MODIS farmland pure pixels with 250 m spatial resolution, corresponding 1 101 600 farmland pixel with 25 m spatial resolution, were identified as winter wheat; 18 630 MODIS mixed pixels integrating farmland and other land cover, corresponding 882 192 farmland pixels, were identified as winter wheat; 10 275 MODIS mixed pixels integrating winter wheat and other crops, corresponding 595 296 farmland pixels, were identified as winter wheat. Winter wheat acreage of our study area is 161 193.00 hm2. By random sampling, the identified results of winter wheat show an accuracy of 96.3%. The error rate is 2.79% compared with statistical data of Yearbook. The superiority of this identified method, compared with the other methods combining medium-resolution and low-resolution remote sensing data, is that not only was the acreage of crops accurately extracted, but also its spatial distribution was determined at the medium-resolution scales. This paper provides a new way to solve problems for extraction of crop cultivation area and spatial distribution information. It can be applied not only to the identification of winter wheat, but also has important reference value for the identification of other types of crops.