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生态学杂志 ›› 2024, Vol. 43 ›› Issue (4): 1113-1121.doi: 10.13292/j.1000-4890.202404.030

• 研究报告 • 上一篇    下一篇

基于多源遥感影像的作物信息提取与需水量时空分析#br#

宋伟1,王运涛2*,王立波1,吴倩3,彭岩波4


  

  1. 1北京师范大学水科学研究院, 北京 100875; 2北京师范大学地理科学学部, 北京 100875; 3中国石油集团安全环保技术研究院有限公司, 北京 102206; 4山东省环境规划研究院, 济南 250101)

  • 出版日期:2024-04-10 发布日期:2024-04-09

Crop information extraction and spatial-temporal analysis of water demand based on multi-source remote sensing images.

SONG Wei1, WANG Yuntao2*, WANG Libo1, WU Qian3, PENG Yanbo4   

  1. (1College of Water Sciences, Beijing Normal University, Beijing 100875, China; 2Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; 3CNPC Research Institute of Safety & Environment Technology, Beijing 102206, China; 4Shandong Academy for Environmental Planning, Jinan 250101, China).

  • Online:2024-04-10 Published:2024-04-09

摘要: 在我国水资源短缺的大背景下,合理规划农业水土资源尤为重要。为实现区域作物种植结构提取,分析作物需水量的时空分布规律,以山东省济宁市重要农业区老万福河流域为研究区,以Landsat 8影像为基础构建作物生育期NDVI时间序列,综合考虑GF-2影像与实地调研数据,结合作物物候期,采用决策树方法提取老万福河流域作物种植结构,并结合Penman-Monteith公式与GIS空间分析功能,使用单作物系数法计算流域作物需水量。结果表明:(1)多时相NDVI序列下,采用决策树方法提取小麦-水稻的平均误差最小,为13.5%,蔬菜的平均误差最大,为23.0%。分类结果显示:小麦-水稻轮作模式分布在流域东部,小麦-玉米轮作模式主要分布在流域西部,经济作物蔬菜主要分布在流域中、西部。(2)作物需水量年内时空分布存在显著差异。时间尺度上,冬季作物需水量明显小于其他季节,夏季作物需水量最大,春夏作物需水量占全年作物需水量的70%左右;空间尺度上,流域东部水稻种植区的作物需水量明显大于其他地区。同一种植模式在不同种植区域的需水量有所差异,其中小麦-玉米年需水量范围在915.6~952.9 mm,不同区域需水量差异达37.3 mm。(3)主要作物水稻、玉米、蔬菜、小麦的生育期需水量存在显著差异,作物需水量分别为846.87、561.7、519.37、415.24 mm。在拔节孕穗(抽薹)期,作物的需水量达到峰值。研究表明,基于Landsat 8和GF-2影像数据构建NDVI时间序列并结合决策树分类的方法可以实现区域作物种植结构的提取。研究结果可为当地灌溉制度的制定及农业水土资源的优化管理和分配提供科学依据。


关键词: 作物蒸散发, 老万福河流域, NDVI, 单作物系数法, 时空特征

Abstract: Under the context of water shortage in China, it is important to rationally plan agricultural soil and water resources. To realize the extraction of regional crop planting structure and analyze the spatial-temporal variations of crop water demand, this study took the Old Wanfu River Basin, an important agricultural area in Jining City, Shandong Province, as the study area. NDVI time series of crop growth period were constructed based on Landsat-8 image, combined with GF-2 image, field survey data, and crop phenology. The decision tree method was used to extract crop planting structure. Furthermore, the single crop coefficient method was used to calculate crop water demand, combined with the Penman-Monteith formula and GIS spatial analysis function. Results showed that the average error of wheat-rice extraction using decision tree method was the smallest (13.5%), and the average error of vegetable extraction was the largest (23.0%). The wheat-rice rotation pattern was distributed in the eastern part of the basin, wheat-corn rotation pattern mainly distributed in the western part of the basin, and vegetables mainly distributed in the middle and west of the basin. There were significant differences in the annual spatial and temporal distribution of crop water demands. On the temporal scale, water demand of winter crops was significantly lower than that of other seasons, water demand of summer crops was the largest, and spring and summer crops accounted for about 70% of the annual crop water demand. On the spatial scale, crop water demand in the eastern rice-growing area was significantly higher than that in other areas. The water demand of the same planting pattern was different in different planting areas. The annual water demand of wheat-corn ranged from 915.6 mm to 952.9 mm, and the difference between different areas was 37.3 mm. There were significant differences in water demands of rice, corn, vegetables, and wheat during the growth period, being 846.87, 561.7, 519.37, and 415.24 mm, respectively. Furthermore, crop water demand reached its peak at the jointing booting (bolting) stage. The results indicated the method by combining NDVI time series, generated with on Landsat-8 and GF-2 image data, and decision tree classification could realize the extraction of regional crop planting structure. This study could provide a scientific basis for the formulation of local irrigation system and the optimization management and allocation of agricultural soil and water resources.


Key words: crop evapotranspiration, the Old Wanfu River Basin, NDVI, single crop coefficient method, spatial-temporal characteristics