基于高分5号卫星中红外数据植被叶片含水量遥感反演方法研究

The knowledge of vegetation leaf water content (VLWC) can contribute to monitor vegetation physiological status, to provide useful information in agriculture for irrigation decisions and drought assessment, and it is also important in forestry in determining fire susceptibility. Unfortunately, most of the VLWC retrieval models have been developed on the basis of the combination of reflectances in visible, near infrared or shortwave infrared, which are sensitive to the variations in water vapor content (WVC) and aerosol content in the atmosphere. The retrieval accuracy of VLWC is consequently affected by the uncertainties of WVC and aerosol in the atmosphere. Radiation in the mid-infrared (MIR) can better penetrate most of the haze layer in the atmosphere and is less sensitive to variations in WVC in the atmosphere. Development of VLWC retrieval model based on MIR reflectances may improve the VLWC retrieval accuracy over complex atmospheric conditions, particularly in recent years increasingly serious haze weather. This project aims to firstly develop a MIR-based leaf optical properties model through studying the leaf optical properties in the MIR domain and the radiative transfer mechanism of MIR spectral reflectivity. Then, coupled with the latest canopy bidirectional reflectance model (4 SAIL), a MIR-based vegetation canopy spectral and directional reflectance model (M-PROSAIL) will be developed. In addition, combining with the MIR spectral response functions, provided by the forthcoming launch of the GF-5 satellite, this project aims to develop a MIR-based VLWC retrieval model. To validate and to improve the proposed MIR-based VLWC model, some field experimental campaigns will be conducted for different vegetation types under different atmospheric conditions. Two field sites, HuaiLai field site located in Hebei province and YuCheng field site located in Shandon province, are chosen as the study sites. The final objective of this project is to accurately retrieve VLWC from mid-infrared measurements in GF-5 satellite.

植被叶片含水量的遥感反演对于监测我国植被生长态势、指导作物灌溉、评估干旱状况以及预防森林火灾等都具有非常重要的意义。现有的各种植被叶片含水量遥感反演模型都是基于可见光、近红外或短波红外反射率的植被指数模型而建立的，反演精度易受大气中的水汽和气溶胶不确定性的影响。中红外能穿透大部分雾、霾、且受水汽含量变化影响小，因此更适用于复杂大气状况（特别是我国近年来日益严重的雾霾天气）下植被叶片含水量的遥感反演。本申请通过研究中红外光谱反射率在植被叶片中的辐射传输机理，建立中红外叶片光学模型，再结合植被冠层模型构建中红外植被冠层光谱与方向反射率模型，同时针对我国即将发射的高分5号卫星，建立中红外植被叶片含水量遥感反演模型，并分别在具有不同大气状况和植被类型的河北怀来试验站和山东禹城试验站开展野外观测实验，对建立的模型进行改进和完善，从而实现我国高分5号卫星植被叶片含水量的遥感精确反演。

项目分析了叶片理化参数分别在可见光近红外到中红外波谱范围内对叶片光谱特性的影响，从理论上分析出了适用于叶片含水量反演的中红外波段反射率。结合传统的植被指数形式，项目提出并建立了基于中红外反射率的叶片含水量植被指数模型和植被叶片含水量遥感反演模型，并根据我国高分5号卫星多光谱成像仪载荷的波段特性，提出并建立了基于高分5号卫星中红外反射率数据的植被指数模型和叶片含水量遥感反演模型。验证结果表明，建立的植被指数与叶片含水量的相关性达到了0.918，反演的叶片含水量误差低至0.0038g/cm2。并且，相对于传统的基于可见光近红外波段反射率建立的植被指数而言，项目建立的基于中红外反射率的植被指数对大气的气溶胶不敏感，即在大气气溶胶不确定性的情况下，能更精确地反演出植被叶片的含水量。项目共发表科技论文18篇，其中SCI论文7篇，EI论文11篇。项目所取得的研究成果将对中红外遥感数据的应用提供技术支持，对植被生长态势、干旱状况监测等研究具有重要意义。