Temperature Emissivity Separaption From Hyperspectral Thermal Infrared Remotely Sensed Dataset Based On Hyper-CAM For Monitoring The Drought Of Cropland

Based on the laboratory platform of hyperspectral thermal infrared remote sensing,this paper designs related experiments,and analyzes the relationship between crop growth status,crop drought detection,emissivity characteristics of different vegetation types and soil moisture content,and the modified algorithm IT2FCM*to extract and identify from different landscape.The vegetation in the interlaced zones of different land types was studied and good results were obtained.The main conclusions are as follows:(1)Through the experiment of potted wheat plant,we conclude that Hyper-CAM hyperspectral thermal infrared imager is one of the most effective tools to explore the changes and growth of vegetation moisture;the image of vegetation canopy structure temperature under different water conditions can be used to judge the growth of wheat plant;the specific emissivity value of vegetation has strong sensitivity to changes in vegetation water content(including soil water content)and water stress,the specific emissivity of vegetation changes significantly,and the emissivity spectrum has a tendency to become larger overall,especially with the increase of water stress,the average emissivity of vegetation canopy also increase.This phenomenon is also found in other scholars' research.The main reason for this phenomenon is due to the cavity effect.The unique absorption characteristics of crops in the thermal infrared spectral region can be addressed by the emissivity retrieved from TES algorithm.(2)Considering the ecological and environmental problems of grassland caused by the drought in northern China,it is urgent to make an early warning of the drought in the grassland.Based on the laboratory Hyper-CAM hyperspectral thermal infrared remote sensing sensor platform,we designed several experiments to obtain the hyperspectral thermal infrared observation data of different kinds of vegetation of the grassland,and using surface hyperspectral thermal infrared surface temperature and specific radiance separation algorithm to separate the surface temperature and emissivity of vegetation,and obtained temperature image and emissivity image of 5 types of vegetation of 4 different species.The temperature uniformity of each type of vegetation is different,and the temperature distribution of vegetation of the same species is different too.The absorption characteristics of emissivity of each type of plant are significantly different,although they are the same species(such as Artemisia subulata Nakai and Artemisia frigida,both of which are belong to Artemisia species,Compositae),for example,the absorption characteristics of emissivity of Artemisia,Artemisia,and Artemisia scoparia are also quite different.(3)In order to detect the drought of early grassland vegetation,it is necessary to measure the emissivity of vegetation under different soil water content.Therefore,this observation experiment takes the Artemisia argyi and Artemisia scoparia as research objects.Under the condition of different soil water content,the specific emissivity of Artemisia frigida are measured through experiments,we found that the emissivity of Artemisia frigida has a significant change with the increase of water content,and the emissivity has an overall increase trend.It can be concluded from the experimental results that thermal infrared remote sensing can be used for the effective identification and classification of different types of vegetation in different types of grassland,and can be used as one of the effective means of grassland dry detection;Hyper-CAM can effectively obtain the emissivities of various types of vegetation of grassland,the absorption characteristics of grassland vegetation in the thermal infrared spectral region are quite notable,which is important for identification and discrimination.Obtaining the emissivity of different grassland vegetation and establishing a typical grassland emissivity spectrum database in the future will provide strong support for the future grassland vegetation drought monitoring.(4)Uncertainty is widespread in remote sensing data,and there is a lot of ambiguity in nature.These two factors determine that we must take into account the uncertainty when classifying remote sensing data.The grassland and other land-type types are too vague,and there are strong fuzzy areas between different grassland types and between different grasslands.Therefore,the uncertainty of classification is greatly increased,considering how to identify grassland vegetation more accurately.When it comes out,the traditional dichotomy(either one or the other)is less accurate,and it is not suitable for a zone where this transition zone or multiple types of vegetation hybridizes.Therefore,the emergence of fuzzy classification based on the probability-based method can effectively deal with the uncertainty of remote sensing data,and provides an effective data processing method for the classification of such transition zones or regions with multiple land-type types.It is believed that the improved IT2FCM*fuzzy classification algorithm proposed in this chapter will play an important role in the classification and identification of grassland,grassland,grassland and woodland interlaced zones.