Leaf Area Index Estimation Of Summer Corn In Typical Area Of Huanghuaihai Plain, China

The objective of this research is leaf area index estimation of summer corn by remote sensing techniques. It is also an important part of the project "Monitoring Carbon parameters of East China"(Grant No.XDA05050106) under the project "Strategic Priority Research Program-Climate Change:Carbon Budget and Relevant Issues" of the Chinese Academy of Sciences and the project "Monitoring Land Cover and Land surface parameters of East China"(Grant No.STSN-01-05) under the project "National ecological environment change in decade (2000-2010)"Leaf area index as an important vegetation biophysical parameter, is one of input parameters in the surface model, which has a very close relationship with vegetation photosynthesis, evapotranspiration, precipitation and carbon flux exchange process. Crop leaf area index is a vital agronomic parameter, which can reflect the status of crop and predict crop yields. Accurate crop leaf area index have an important role in crop identification, growth monitoring, food production estimation. This research makes use of HJ-1CCD data to estimate corn leaf area index based on PROSAIL radiative transfer model in the typical area of Huanghuaihai Plain-Yucheng area, Shandong. The main contents and results of the research are presented as follows:1) Sensitivity between parameters of the PROSAIL model and wavelength are analyzed. Mesophyll structure parameter N, chlorophyll content Cab, dry matter content Cm, equivalent water thickness Cw, LAI and hot spot parameter SL are all insensitive to BLUE waveband reflectance. Cab has an obvious impact on GREEN waveband reflectance. LAI shows a high sensitivity in NIR and RED reflectance of wavebands. Cw has almost no effect on VIS wavebands, but very sensitive in SWIR waveband reflectance. Cm、N and SL are sensitive to NIR waveband reflectance.2) Sensitivity among model parameters, vegetation index and LAI are analyzed. Canopy reflectance increases with LAI increasing in the NIR waveband, while decreases in the RED and GREEN waveband. The parameters average leaf angle ALA, Cm, N, SL show more sensitive with LAI increasing in the NIR waveband. On the contrary, soil background reflectance is less sensitive to NIR waveband reflectance with the LAI increasing. NDVI is easily saturated when LAI>3, while SAVI is saturated when LAI reaches to5. EVI and RVI perform better than NDVI in saturation. Therefore, we tend not to estimate leaf area index using NDVI when LAI is high.3) Influences of cost function and priori knowledge on the estimation results are analyzed. Different cost functions make different results. The results show that the estimation accuracy based on the cost function in the form of weighted sum of squared deviations is higher than the sum of squared deviations. Priori knowledge as the "hard boundary" of an estimation model, can improve the accuracy with R2increasing16.6%, RMSE decreasing12.5%.4) Corn leaf area index is estimated using look-up table method, which can simplify physical model. The mean estimation result can effectively improve the leaf area index estimation accuracy with R2=0.54, RMSE=0.32, through priori knowledge and different cost function estimating tests. The results indicate that PROSAIL model can be used to estimate leaf area index effectively from the perspective of radiative transfer principle.Corn leaf area index is estimated with domestic HJ-1CCD remotely sensed data, using PROSAIL radiative transfer model. The mechanism and impact of leaf and canopy parameters on the canopy reflectance in different bands are analyzed quantitatively using the simulated data. Leaf area index can be estimated, according to process of the sensitivity analysis of parameters, LAI modeling, estimation results and evaluation. This is beneficial and can provide reference data for corn leaf area index estimation and growth monitoring in this area.