摘要
MODIS数据的1、2波段是具有250m空间分辨率的红和近红外波段,并具有较高的时间分辨率,可对农作物进行动态跟踪监测。随着农作物的生长,NDVI值逐渐增大,并在一定生育期达到最大值后开始下降。由于不同作物具有不同的生育期,NDVI峰值的大小、出现的时间各不相同。通过对北京市主要农作物的种植结构调查和3月中旬到11月上旬的不同作物的NDVI值采样分析,得出:①冬小麦NDVI值3月下旬相对较高,5月上旬最大;②春玉米的NDVI峰值出现于8月上旬;③夏玉米的NDVI峰值出现于8月中旬;④大豆的NDVI峰值也出现在8月中上旬,可通过物候历与春玉米区分开来(春玉米是单季作物,大豆是双季作物),通过峰值大小与夏玉米区分开来。结合北京市1:10万土地利用数据,通过NDVI值时序变化规律从MODIS数据中提取了冬小麦、春玉米、夏玉米、大豆等作物的种植面积,总体精度达到95%以上。
One of the major problems in crop monitoring study is to extract planting areas and their spatial distribution automatically. This paper presents an automatic approach to planting area extraction for mixed planting crops (winter wheat, spring maize, summer maize, and soybean) using moderate spatial resolution and high temporal resolution MODIS data around Beijing, China. With a spatial resolution of 250 meters, band 1 and band 2 of MODIS data in red and near-infrared spectral regions can be used for dynamic monitoring of crops. NDVI values increase with the growth of the crops, and gradually decrease after reaching the maximum at a certain growth stage of the crops. Because different crops have different growth stages, the NDVI peak values and their occurrences can be different. After investigating the planting structure of the main crops and analyzing the NDVI values of different crops from mid-March to mid-November of 2002 in Beijing, following results are obtained: a. NDVI value of winter wheat is higher than other objects in the end of March, and its maximum appears in the beginning of May; b. maximum value of spring maize NDVI appears in the beginning of August; c. maximum value of summer maize NDVI appears in the middle of August; d. maximum value of bean NDVI appears also in the beginning of August, but it can be recognized from spring maize by the growth stage. Spring maize is a single-season crop; whenas bean is a double-season crop. So the temporal changes of NDVI profiles were obtained. In corporation with 1∶100,000 scale landuse data of Beijing, the planting areas of winter wheat, spring maize, summer maize and bean were extracted from MODIS data, and the total accuracy is over 95%. In this research, multiple-phase MODIS data were received during main growing seasons and preprocessed; NDVI temporal profiles of main crop types were generated; models for planting areas extraction were developed based on the analysis of temporal NDVI profiles; and spatial distribution map of planting areas of winter wheat, maize and soybean in Beijing in 2002 were created. The study suggests that it is possible to extract planting areas automatically from MODIS data for large areas. Accuracy analysis showed that the results are highly reliable, especially in plain areas.
出处
《资源科学》
CSSCI
CSCD
北大核心
2004年第6期17-22,共6页
Resources Science
基金
国家发展计划委员会高技术产业化示范工程项目"北京精准农业示范工程"之课题"作物长势
病虫害遥感监测研究"(编号:A00300100584 RS02)。