Abstract: Abstract: Flood disaster occurs frequently in China and brings severe disaster to crops. Therefore, the crop plant structure in seasons prone to waterlogging becomes significant information for studies on flood loss, flood control, and disaster mitigation. Under these conditions, this paper presents a fast and convenient method to extract the crop plant structure in small scale areas during seasons prone to waterlogging, based on multi-sensor and multi-temporal remote sensing data. Landsat8 OLI and MODIS data were chosen because of the advantages such as it being free of charge and easy to search for and download. These two types of data showed the characteristics of crops' growth respectively in space and time, leading to a proper combination for crop planting structure extraction. If one only uses MODIS data to build the extraction model, the spatial resolution is too low to get the planting structure in small scale areas. On the other hand, just classifying the OLI images by visual interpretation sometimes could not determine the types of crops. The Jianli County in Jingzhou City, Hubei Province was chosen as the study area. The seasons prone to waterlogging in Jianli mainly include June, July, and August, and are related to crops such as early-season rice, middle-season rice, late rice, and cotton. Here are the extracting models for the four major kinds of crops: NDVI value of cotton grows to the peak in early July and stays high until September; early-season rice NDVI maximum value appears in middle June, and it becomes completely mature in late July; middle-rice NDVI value starts to increase at the end of May and reaches its peak in early or middle July before falling to a decline; NDVI value of late rice goes up in mid-July and the peak value appears in middle or late August, and then the value begins to decline. First, a time series curve of NDVI was built from the MODIS data, which was later smoothed by an improved Savitzky-Golay filter. The improved Savitzky-Golay filter reserved the authenticity of data at both ends of the NDVI time series while further improving the smoothness of the curve. To distinguish the types of crops, threshold values of NDVI for different crops were set according to corresponding phenophases. Based on the characteristics and threshold values of NDVI time series, appropriate ROIs (Region of Interest) in the Landsat8 OLI images in key growth stages of different crops were selected as prior knowledge for training. Finally, the area and distribution of the four studied crops were extracted by a BP Neural Net Supervised Classification. The experimental results agreed well with the statistical data and a ZY-3 image which had a spatial definition of 6 meters, and obtained an average precision above 90%. It was concluded that the proposed method in this paper is simple and easy operating. Moreover, it accurately reflected the real situation of crop distribution in the Jianli area, and is suited for extraction of the plant structure in small scale areas like Jianli. Therefore, this method provides a reliable basis for related research studies on flood disaster.