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Open AccessArticle

Leaf Area Index Estimation Using Chinese GF-1 Wide Field View Data in an Agriculture Region

by Xiangqin Wei 1,2,3, Xingfa Gu 1,2,3,*, Qingyan Meng 1,3, Tao Yu 1,3, Xiang Zhou 1,3, Zheng Wei 3, Kun Jia 4,* and Chunmei Wang 1,3
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
University of Chinese Academy of Sciences, Beijing 100049, China
Application Technology Center of China High-Resolution Earth Observation System, Beijing 100101, China
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Authors to whom correspondence should be addressed.
Sensors 2017, 17(7), 1593;
Received: 26 April 2017 / Revised: 20 June 2017 / Accepted: 5 July 2017 / Published: 8 July 2017
(This article belongs to the Special Issue Sensors in Agriculture)
Leaf area index (LAI) is an important vegetation parameter that characterizes leaf density and canopy structure, and plays an important role in global change study, land surface process simulation and agriculture monitoring. The wide field view (WFV) sensor on board the Chinese GF-1 satellite can acquire multi-spectral data with decametric spatial resolution, high temporal resolution and wide coverage, which are valuable data sources for dynamic monitoring of LAI. Therefore, an automatic LAI estimation algorithm for GF-1 WFV data was developed based on the radiative transfer model and LAI estimation accuracy of the developed algorithm was assessed in an agriculture region with maize as the dominated crop type. The radiative transfer model was firstly used to simulate the physical relationship between canopy reflectance and LAI under different soil and vegetation conditions, and then the training sample dataset was formed. Then, neural networks (NNs) were used to develop the LAI estimation algorithm using the training sample dataset. Green, red and near-infrared band reflectances of GF-1 WFV data were used as the input variables of the NNs, as well as the corresponding LAI was the output variable. The validation results using field LAI measurements in the agriculture region indicated that the LAI estimation algorithm could achieve satisfactory results (such as R2 = 0.818, RMSE = 0.50). In addition, the developed LAI estimation algorithm had potential to operationally generate LAI datasets using GF-1 WFV land surface reflectance data, which could provide high spatial and temporal resolution LAI data for agriculture, ecosystem and environmental management researches. View Full-Text
Keywords: leaf area index; radiative transfer model; neural networks; GF-1 satellite; wide field view leaf area index; radiative transfer model; neural networks; GF-1 satellite; wide field view
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Wei, X.; Gu, X.; Meng, Q.; Yu, T.; Zhou, X.; Wei, Z.; Jia, K.; Wang, C. Leaf Area Index Estimation Using Chinese GF-1 Wide Field View Data in an Agriculture Region. Sensors 2017, 17, 1593.

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