热红外遥感图像中有云像元的地表温度估算方法研究

项目名称： 热红外遥感图像中有云像元的地表温度估算方法研究

项目编号： No.41471300

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 天文学、地球科学

项目作者： 覃志豪

作者单位： 中国农业科学院农业资源与农业区划研究所

项目金额： 90万元

中文摘要： 云是农业遥感应用和热红外图像地表温度反演的重要障碍。热红外图像中的云现象可分为：遮挡和覆盖。云遮挡指云遮挡了遥感观测而没有阻挡太阳照射，常见于高分辨率图像中。云覆盖不仅是遮挡遥感观测而且也阻挡了太阳照射，常见于MODIS等数据中。本项目将以农业遥感常用的MODIS数据为例，重点研究云覆盖像元的地表温度估算方法。云覆盖对地表温度变化的影响，主要因为云减弱了到达地表面的大气辐射强度，但云覆盖像元的地表温度变化，还因地表类型、云覆盖特征(形状、大小、时间、厚度等)、地理纬度等不同而变得复杂。本研究首先根据地表热量平衡原理，模拟典型地表类型的地表温度随大气辐射强度的变化规律，然后根据像元尺度地表构成，通过估算云像元的大气辐射强度，建立云覆盖像元地表温度估计方法。在华北、长江中下游和华南地区野外试验站开展云覆盖、大气辐射和地表温度变化的观测验证，为农业遥感应用提供技术支撑。

中文关键词： 热红外遥感；大气遥感建模；统计模型；辐射传输模型；可见光-近红外遥感

英文摘要： Cloud is an essential obstacle for remote sensing (RS) application to agriculture and land surface temperature (LST) retrieval from RS data. Two kinds of cloud can be identified in RS images: occlusion and cover. Cloud occlusion is due to difference between sensing direction and solar radiation. In cloud occlusion, the ground was not visually seen by remote sensor but still was under sunshine. However, in cloud cover, the glound can not only be seen by the sensor but also blocked from sunshine. This is the general case in low spatial resolution images such as MODIS frequently used in agricultural monitoring. In this study, we intend to develop an applicable approach to esitmate LST for the cloud-covered pixels in MODIS images. Impact of cloud cover on LST change relies on the fact that cloud reduces or blocks the solar radiance reaching the ground. However the machenism behind the impact is very complex. LST of cloud-covered pixels changes not only with the radiance but also with the characteristics of ground surface and cloud cover itsself such as form, size,thickness, and period. In order to investigate the impact, we classifies the pixels into 3 typical surfaces: natural, urban and water. Natural surface can be seen as composed of vegetation and bare soil in different fractions. The same is for urban urface: vegetation and buiding. Thus we have 4 typical surfaces: vegetation, bare soil, building and water. Surface energy balance model will be used to simulate the change of LST with the radiance reaching the typical surfaces of vegetation, bare soil and building under various conditions. On the basis of this LST-radiance relationship, we are able to establish an approach to estimate the LST for the cloud-covered pixels in MODIS images. This can be done through the estimation of radiance for the pixels using the scatering models and the composition of the pixels from the 4 typical surfaces. Field observations and experiments will be done in the study to provide required data sets for the simulation and validation of the relationship. We intend to focus our study in the 3 important agricultural regions of China: North China, Middle Yangtze Basin and South China. The available 4 agricultural experiment stations (Beijing Shunyi, Shandong Dezhou, Jianxi Ji'an and Guangdong Jiangmen) locating in the 3 regions will be used for field observation and experiments.Such parameters as LST, atmospheric scattering radiance, global radiance, cloud condition, crop growing condition, air temperature and humidity, soil temperature and moisture, wind speed and sunshine hour will be measured in the stations. After proofing its applicability, we intend to program the approach for fast estimation of LST for entire China from MODIS data, which is required by agricultural monitoring.

英文关键词： Thermal infrared remote sensing;Land surface temperature;Cloud-covered pixels