基于PROSAIL混合反演模型的MODIS LAI产品改进及评估  

Improvement and analysis of MODIS LAI product based on PROSAIL hybrid inversion model

作　　者：赫晓慧[1,2] 张乐涵 乔梦佳 田智慧[1,2] 周广胜 HE Xiaohui;ZHANG Lehan;QIAO Mengjia;TIAN Zhihui;ZHOU Guangsheng(School of Earth Science and Technology,Zhengzhou University,Zhengzhou 450001,China;Joint Laboratory of Eco-Meteorology,Zhengzhou University,Chinese Academy of Meteorological Science,Zhengzhou 450001,China;School of Computer and Artificial Intelligence,Zhengzhou University,Zhengzhou 450001,China)

机构地区：[1]郑州大学地球科学与技术学院,郑州450001 [2]郑州大学中国气象科学研究院郑州大学生态气象联合实验室,郑州450001 [3]郑州大学计算机与人工智能学院,郑州450001

出　　处：《生态学报》2023年第22期9328-9341,共14页Acta Ecologica Sinica

基　　金：第二次青藏高原科学考察计划(2019QZKK0106);河南省重大科技专项(201400210900)。

摘　　要：叶面积指数(Leaf Area Index,LAI)是定量陆地生态系统中光合作用、呼吸作用、蒸腾、碳和养分循环等过程中物质与能量交换的重要结构参数。目前大、中尺度的气候和生态水文建模使用的LAI产品主要来源于中分辨率成像光谱仪(MODIS),但由于其反演过程中的不确定性因素导致MODIS LAI产品在部分地区存在质量问题。以青海省复杂植被地区为研究区域,基于实地考察与采样验证了区域内MODIS LAI所存在的质量问题分布,并揭示了不确定因素的影响。与此同时,提出了一种基于PROSAIL模型与深度神经网络(DNN)的混合建模技术,针对MODIS LAI生成机制中地表分类数据、地表反射率数据和反演算法的不确定性进行改进,并基于青海省大范围实测LAI数据评估了改进前后产品的准确度,实测数据的验证结果发现:改进模型的LAI准确度(RMSE=0.48,R^(2)=0.64)显著高于MODIS LAI(RMSE=0.71,R^(2)=0.56),预测结果与实测结果之间的偏差显著减少;区域尺度上,柴达木荒漠植被低覆盖典型区域、三江源高寒草甸中覆盖典型区域与青海湖牧场草地高覆盖典型区域的RMSE分别提高了0.19、0.10、0.54,改进方法有效解决了MODIS LAI产品中高覆盖植被饱和效应导致的高估以及低覆盖植被未检索导致低估的质量问题,改进结果分布连续,更符合真实植被状况。基于以上研究,充分证明了研究方法对MODIS LAI产品的改进具有可靠性,能够在缺少实测样本数据的情况下有效提高MODIS LAI的质量,为全球植被环境监测与生态建模提供重要的数据支持。Leaf Area Index(LAI)is an important structural parameter to quantify the exchange of matter and energy in the processes of photosynthesis,respiration,transpiration,carbon and nutrient cycling in terrestrial ecosystems.At present,the LAI products used for large-scale and medium-scale climate and eco-hydrological modeling are mainly derived from Moderate Resolution Imaging Spectroradiometer(MODIS),but due to the uncertain factors in the inversion process,the MODIS LAI products have quality problems in some areas.This study took the complex vegetation area of Qinghai Province as the research area,based on field investigation and sampling to verify the distribution of quality problems of MODIS LAI products in the research area,and revealed the influence of uncertain factors of MODIS LAI products on their quality problems.At the same time,this paper proposed a hybrid modeling technique based on the PROSAIL radiative transfer model and deep neural network(DNN)model to improve the uncertainties of surface classification data,surface reflectance data,and inversion algorithm in the generation mechanism of MODIS LAI products.It is determined to make improvements and solve the quality problems of MODIS LAI products.In the end,based on the large-scale measured LAI data in Qinghai Province,the accuracy of the product before and after improvement was evaluated,and the validation results of the measured data showed that the LAI accuracy of the improved model(RMSE=0.48,R^(2)=0.64)was significantly higher than that of MODIS LAI products(RMSE=0.71,R^(2)=0.56),and the deviation between the predicted results and the measured results was significantly reduced.On a regional scale,the RMSE accuracy of typical areas of low cover type desert vegetation in the Qaidam region,typical areas of medium cover type alpine meadows in the Sanjiangyuan region,and typical areas of high cover type pasture grassland in the Qinghai Lake region have been increased by 0.19,0.10,and 0.54,respectively.The improved method effectively solved the quality prob

关 键 词：MODIS LAI PROSAIL模型 叶面积指数 深度神经网络 

分 类 号：Q948[生物学—植物学]