饱和水汽压差的卫星遥感研究综述

Remote Sensing Estimates of Vapor Pressure Deficit: An Overview

饱和水汽压差是许多陆表生态和陆面过程模型中非常重要的输入参数之一,精确估算并获取其时空分布特征是生态、农业遥感领域亟待解决的问题之一。在简要回顾饱和水汽压差的应用和传统获取方法的基础上重点介绍遥感估算饱和水汽压差的数据源和估算方法,即统计方法和解析计算法,并对解析计算法所需关键参数的国内外相关研究进展进行概述,探讨目前遥感估算饱和水汽压差存在的不足,并结合目前遥感技术发展现状和此领域研究存在的问题探讨其发展趋势,展望了联合多源遥感数据进行高时空分辨率的饱和水汽压差遥感估算问题。

Vapor Pressure Deficit （VPD） is an important climatic variable widely used in many ecosystem models to simulate fluxes and states of water and carbon; it plays an important role in fire warning and epidemic disease early warning systems. Accurate estimation of spatio-temporally distributed VPD is critical for ecosystem and climate modeling efforts. In this paper, the available remote sensing datasets for satellite-based VPD estimation are analyzed, the precision and spatial resolution are two important factors for selecting remote sensing data. Then, the principle and advantages of different estimation algorithms are analyzed, which include the regression method and analytic method. The regression method is simple, but requires mass sample data and can not be used in other region before calibration. The analytic method is more complex, but can be used anywhere once established. The near surface air temperature and humidity are two key parameters for estimating VPD, which are usually estimated from the satellite retrieved land surface temperature and total precipitable water vapor. The errors in estimated VPD cloud are further eliminated by improving the accuracy of input remote sensing data and improving estimation algo- rithms of near surface air temperature and humidity. Finally, the existing problems and the VPD estimation research prospect are discussed. Most research work is limited in clear sky days until now, and VPD estimation under cloudy days is a challenging work, but it is important for many applications. A full VPD map could be achieved by combining several satellite data from different instruments, especially by taking the advantages of optical and microwave remote sensing. The prospects of the satellite-based VPD estimation technologies are presented.