基于地表温度和植被指数的农业干旱遥感监测方法

农业旱灾是人类面临的最主要自然灾害之一,对我国农业生产影响非常大。土壤含水量是农业干旱监测的重要指标,通过遥感地表温度(LST)与植被指数(NDVI)结合,可以估算土壤湿度,监测农作物旱情。重点研究了LST/NDVI特征空间中干湿边的提取方法,通过14年全国NOAA/AVHRR的8km合成数据集分析发现,在LST/NDVI特征空间中,曲线斜率与实测土壤湿度显著相关(R=0.78,P<0.01),干边的截距和斜率与湿边有比较稳定的关系,但干湿边存在较大的空间和时间变异性。将全国分为6个区,分别确定LST/NDVI特征空间,根据特征空间干湿边参数反演土壤湿度,在土壤湿度分布图和全国耕地分布图基础上,进行旱情分级,得到全国耕地旱情分布图,可以为农业抗旱救灾提供快速宏观的信息服务。

Monitoring Agricultural Drought by Vegetation Index and Remotely Sensed Temperature

Agricultural drought is one of major natural disasters and has devastated impacts on agriculture.Generally,soil moisture is a key indicator of agricultural drought,which can be estimated based on the relationship between remotely sensed surface temperature(LST)and vegetation index(NDVI),and be used to evaluate crop drought.Specifically,the dry and wet edges in LST/NDVI feature space are identified using 14 years NOAA/NASA Pathfinder AVHRR Land data over China and an algorithm is proposed for the regional estimate of soil moisture.Results show that the slope of the relationship between LST and NDVI(LST/NDVI slope)is significantly correlated to in situ soil moisture(R2=0.78,P<0.01),and the intercept and slope of dry edge have a consistent relationship with those of wet edge,but the dry and wet edges show great spatial and temporal variations.In this paper,the mainland of China is divided into 6 zones,and the parameters of LST/NDVI space are determined for each zone.Then,the soil moisture is obtained by inversion using the proposed method.Based on the arable land map and drought grade,the drought distribution map for arable land is produced,which may be used in quick macroscopic agricultural information services for drought relief.