Affiliation:
1. College of Forestry, Hebei Agricultural University, Baoding 071000, China
Abstract
The LAI is a key parameter used to describe the exchange of material and energy between soil, vegetation and the atmosphere. It has become an important driving datum in the study of carbon and water cycle mechanism models at many regional scales. In order to obtain high temporal resolution and high spatial resolution LAI products, this study proposed a method to combine the high temporal resolution of MODIS LAI products with the high spatial resolution of Sentinel-2 data. The method first used the LACC algorithm to smooth the LAI time-series data and extracted the normalized growth curve of the MODIS LAI of forest and used this curve to simulate the annual variation of the LAI. Secondly, it estimated the LAI at the period of full leaf spread based on the traditional remote sensing statistical model and Sentinel-2 remote sensing data as the maximum value of the forest LAI in the study area and used it to control the LAI growth curve. Finally, the time-series LAI data set was created by multiplying the maximum LAI by the normalized forest LAI growth curve. The results indicate that: (1) the remote sensing statistical estimation model of LAI was developed using the atmospherically resistant vegetation index ARVI (R2 = 0.494); (2) the MODIS LAI normalized growth curve keeps a good level of agreement with the actual variation. This study provides a simple and efficient method for obtaining effective time-series forest LAI data for the scope of small- and medium-sized areas.
Funder
National Natural Science Foundation of China
National Key R&D Program of China
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference75 articles.
1. Mao, Y., Michel, O., Yu, Y., Fan, W., Sui, A., Liu, Z., and Wu, G. (2021). Retrieval of Boreal Forest Heights Using an Improved Random Volume over Ground (RVoG) Model Based on Repeat-Pass Spaceborne Polarimetric SAR Interferometry: The Case Study of Saihanba, China. Remote Sens., 13.
2. Quantitative retrieving forest ecological parameters based on remote sensing in Liping County of China;Owe;Remote Sens. Agric. Ecosyst. Hydrol. VIII,2006
3. Characterizing 3D vegetation structure from space: Mission requirements;Hall;Remote Sens. Environ.,2011
4. Automated estimation of forest height and underlying topography over a Brazilian tropical forest with single-baseline single-polarization TanDEM-X SAR interferometry;Yang;Remote Sens. Environ.,2021
5. Importance of biomass in the global carbon cycle;Houghton;J. Geophys. Res. Biogeosci.,2009
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献