Study On The Spatial Variability Of Key Land Surface Variables And Influences On Remotely Sensed Heat Fluxes

Heterogenetiy,including the inhomogeneity of landscapes and surface variables,significantly affects the accuracy of evapotranspiration(ET)(or latent heatflux,LE)estimated from remote sensing satellite data.However,most of the current research uses statistical methods in the mixed pixel to correct the ET or LE estimation error,and there is lack of research from the perspective of the remote sensing model.The method of using frequency distributions or generalized probability density functions(PDFs),which is called the “statistical-dynamical” approach to describe the heterogeneity of land surface characteristics,is a good way to solve the problem.In this study,we systematically analyzes the influence of inhomogeneity on the estimation of ET or LE from remote sensing and eliminates the basic problems that need to be solved when constructing an efficient PDF-based model.That is,which surface variables' heterogeneity must be considered when establishing an ET or LE estimation model.To address these issues,this study reviews the research progress on the estimation of ET or LE over heterogeneous surface and the current spatial heterogeneity description methods.The main research contents and results are as follows:(1)Based on the analysis of simulated and field experimental data,we discussed how to express the inhomogeneity of surface variables.And compared three index,they were Gini coefficient,coefficient of variability(CV)and entropy,and entropy was more stable and accuract than the CV and Gini coefficient for expressing the variability of the surface variables.(2)Combined the entropy and the grey theory,we built an analysis frame of variation trend of spatial heterogeneity,and we found that the variability of land surface temperature and soil moisture are the factors that must be taken into account in the construction of ET or LE calculation model for heterogeneous underlying surfaces.(3)Both the fitting results and the principal component analysis results show that NDVI and albedo contribute more weight to the spatial variability of surface temperature and can be used as an indicator of surface temperature in downscaling.(4)Combined China HJ-1B satellite data and MODIS to estimate subpixed LE by using two temperature-sharpening methods(i.e.,DisTrad and GWR).The validation results showed that the direct use of the MODIS LST approach does not consider LST heterogeneity at all,leading to significant errors,while the downscaling results will greatly reduce the error;the two methods differ greatly on the areas where the underlying surface is more heterogeneous within the footprint area of the site.The GWR-LST is more suitable for areas with more complex underlying surfaces.