基于GF-1影像的耕地地块破碎区水稻遥感提取

耕地地块破碎区水稻遥感提取是作物监测研究的热点问题之一。以苏州市高新区为例，通过挖掘关键物候期水稻与下垫面水体光谱特征组合差异，基于分蘖期与齐穗期两景16 m分辨率的GF-1 WFV数据，构建归一化差值植被指数（NDVI）差值法、归一化水体指数和比值植被指数（NDWI-RVI）差值法提取水稻分布，并深入探究了水稻面积提取精度及空间重合度影响因素。结果显示：与非监督分类和监督分类方法相比，植被指数差值法水稻识别精度贡献率可提升30%以上，NDVI差值法提取水稻种植面积的精度、空间重合度、制图总体精度和Kappa系数分别为86.2%、66.1%、92.2%和0.72；NDWI-RVI差值法上述指标分别高达95.5%、78.4%、93.5%和0.846，实现了利用少量中高分辨率遥感影像精确提取耕地地块破碎区水稻分布的目的，可实际服务于太湖地区农业生产及相关决策支持。

Study on Extraction of Paddy Rice Planting Area in Low Fragmented Regions based on GF-1 WFV Images

The extraction of paddy rice planting area in low fragmented regions based on remote sensing images is a hotspot in crop monitoring. Taking Gaoxin district of Suzhou city in Taihu Lake region as a case study, the rice and underlying water spectral characteristics in critical phenophase were studied in-depth to reduce the demand of remote sensing images, and only two GF-1 WFV images with resolution of 16 m during rice tillering and full heading stages were employed to extract the paddy rice planting area. Two vegetation index methods, including difference of Normalized Differential Vegetation Index (NDVI) and the combination of difference of Normalized Differential Water Index (NDWI) and Ratio Vegetation Index (RVI) were studied. The results suggested that both the methods effectively promoted the extraction precision, comparing with the results of supervised classification and unsupervised classification methods. The area recognition accuracy, space consistency mapping accuracy and kappa coefficient of NDVI method were 86.2%, 66.1%, 92.2% and 0.72, while those of NDWI-RVI method were up to 95.5%, 78.4%, 93.5% and 0.85, respectively. The two methods realized the purpose of accurately extracting rice area in low fragmented regions by using a few medium and high resolution remote sensing images, and can be effectively serviced for actual production and relevant decision support in Taihu Lake region.