caa a22 4500 445300817 CHVBK 20180317142557.0 cr unu---uuuuu 170323e20100501xx s 000 0 eng 10.1007/s11430-009-0203-z doi (NATIONALLICENCE)springer-10.1007/s11430-009-0203-z Retrieving crop leaf area index by assimilation of MODIS data into a crop growth model [Elektronische Daten] [DongWei Wang, JinDi Wang, ShunLin Liang] Leaf area index (LAI) is an important parameter in monitoring crop growth. One of the methods for retrieving LAI from remotely sensed observations is through inversion of canopy reflectance models. Many model inversion methods fail to account for variable LAI values at different crop growth stages. In this research, we use the crop growth model to describe the LAI changes with crop growth, and consider a priori LAI values at different crop growth stages as constraint information. The key approach of this research is to assimilate multiple canopy reflectance values observed at different growth stages and a priori LAI values into a coupled crop growth and radiative transfer model sequentially using a variational data assimilation algorithm. Adjoint method is used to minimize the cost function. Any other information source can be easily incorporated into the inversion cost function. The validation results show that the time series of MODIS canopy reflectance can greatly reduce the uncertainty of the inverted LAI values. Compared with MODIS LAI product at Changping and Shunyi Counties of Beijing, this method has significantly improved the estimated LAI temporal profile. Science China Press and Springer-Verlag Berlin Heidelberg, 2010 inversion nationallicence leaf area index nationallicence crop growth model nationallicence time series nationallicence MODIS nationallicence Wang DongWei State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing Applications, CAS, 100875, Beijing, China aut Wang JinDi State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing Applications, CAS, 100875, Beijing, China aut Liang ShunLin Department of Geography, University of Maryland, 20742, College Park, MD, USA aut Science China Earth Sciences SP Science China Press 53/5(2010-05-01), 721-730 1674-7313 53:5<721 2010 53 11430 https://doi.org/10.1007/s11430-009-0203-z text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11430-009-0203-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Wang DongWei State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing Applications, CAS, 100875, Beijing, China aut NATIONALLICENCE 700 1- Wang JinDi State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing Applications, CAS, 100875, Beijing, China aut NATIONALLICENCE 700 1- Liang ShunLin Department of Geography, University of Maryland, 20742, College Park, MD, USA aut NATIONALLICENCE 773 0- Science China Earth Sciences SP Science China Press 53/5(2010-05-01), 721-730 1674-7313 53:5<721 2010 53 11430 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer