| Leaf area index(LAI)is an important parameter for characterizing vegetation canopy structure.Due to factors such as atmospheric conditions,there are problems such as missing data and low quality in MODIS LAI data products,which seriously affects the application of LAI data sets.In the land surface model,the leaf area index affects the photosynthesis,canopy transpiration efficiency and energy balance of the vegetation,and is related to the cycle of the whole material,energy and water.In order to obtain higher-precision model simulation results,this study uses the Qinghai-Tibet Plateau as a research area,and comprehensively uses pixel quality analysis,SG filtering and annual sequence outlier detection filtering methods to carry out LAI data reconstruction technology research and reconstruct 2015 MODIS.The LAI time series data set was used to apply the reconstructed high-quality LAI data to the land surface process simulation study.Two sets of comparative tests were designed to simulate the surface temperature and soil moisture,and the application effect of the reconstructed LAI data was compared.The main conclusions are as follows:(1)Statistical analysis of pixel quality shows that the high-quality pixels of broad-leaved forests have the lowest proportion,only 51.76%,and the overall proportion of low-quality and inversion failed pixels in each category is 18.68%.Aiming at the problem of low data set quality,an integrated filtering method is proposed,which effectively preserves the high quality pixel information value and improves the S-G filtering effect.Compared with the S-G filtering method,the LAI mean value of the high quality pixels reconstructed by the integrated filtering method is more consistent with the original mean value,and the correlation coefficient between the high quality pixel reconstruction and the original data reaches 0.99,which has better fidelity.The outliers of low-and medium-quality pixel reconstruction are filtered,filled with null regions,the standard deviation is reduced,and low-value regions or abnormal points are better identified and repaired.The overall stability is better and can be effectively fitted timing curve.(2)Using the newly developed high-temporal resolution land surface data assimilation(HRCLDAS/CLM)system and different LAI data,design two sets of comparative tests HRCLM_TG(model's own LAI data)and test NEW/TG(reconstructed LAI data))Carry out surface temperature simulation.The results show that the surface temperature of the two groups is gradually increasing from the northwest to the southeast in the Qinghai-Tibet Plateau,and it can better simulate the time trend of the surface temperature rising first and then decreasing.The correlation coefficient between the surface temperature and the observed value of the NEW/TG simulation reached 0.863,which was slightly larger than the test HRCLM/TG.The simulation results were relatively good,and the difference between the simulated and the measured values in summer and autumn was larger;the test NEW/TG The square root error is reduced by 0.109 °C;the deviation is negative in all three seasons,the actual surface temperature value is underestimated,and the summer surface temperature simulation is relatively poor.Overall,the improvement in LAI quality has improved the accuracy of surface temperature simulation.(3)The effects of LAI improvement on soil moisture simulation accuracy in the Qinghai-Tibet Plateau were studied by designing two sets of test HRCLM/SOIL(model's own LAI data)and test NEW/SOIL(reconstructed LAI data).The results show that the two groups of experiments can better simulate the trend of soil moisture time,the correlation coefficient between the simulation results and the observed values is about 0.800,and the correlation coefficient between the simulated NEW/SOIL simulation values and the observed values is slightly higher than the test HRCLM/SOIL.It is 0.023,in which the difference between the simulated value and the measured value in spring is the largest.The simulated soil moisture values of the two groups were consistent with the seasonal roots of the observed root mean square error and deviation,with the smallest in spring and larger in summer and autumn.From the spatial distribution of soil moisture,both groups showed a trend of increasing soil moisture from northwest to southeast,and overestimated the actual soil moisture.The simulation results of the NEW/SOIL experiment show that the high-quality leaf area index data set can improve the simulation effect of the land surface model on soil moisture. |
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