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Sensors (Basel). 2017 Jul 8;17(7). pii: E1593. doi: 10.3390/s17071593.

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

Wei X1,2,3, Gu X4,5,6, Meng Q7,8, Yu T9,10, Zhou X11,12, Wei Z13, Jia K14, Wang C15,16.

Author information

1
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China. weixq@radi.ac.cn.
2
University of Chinese Academy of Sciences, Beijing 100049, China. weixq@radi.ac.cn.
3
Application Technology Center of China High-Resolution Earth Observation System, Beijing 100101, China. weixq@radi.ac.cn.
4
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China. guxingfa@radi.ac.cn.
5
University of Chinese Academy of Sciences, Beijing 100049, China. guxingfa@radi.ac.cn.
6
Application Technology Center of China High-Resolution Earth Observation System, Beijing 100101, China. guxingfa@radi.ac.cn.
7
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China. mengqy@radi.ac.cn.
8
Application Technology Center of China High-Resolution Earth Observation System, Beijing 100101, China. mengqy@radi.ac.cn.
9
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China. yutao@radi.ac.cn.
10
Application Technology Center of China High-Resolution Earth Observation System, Beijing 100101, China. yutao@radi.ac.cn.
11
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China. zhouxiang@radi.ac.cn.
12
Application Technology Center of China High-Resolution Earth Observation System, Beijing 100101, China. zhouxiang@radi.ac.cn.
13
Application Technology Center of China High-Resolution Earth Observation System, Beijing 100101, China. weizheng@irsa.ac.cn.
14
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China. jiakun@bnu.edu.cn.
15
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China. wangcm@radi.ac.cn.
16
Application Technology Center of China High-Resolution Earth Observation System, Beijing 100101, China. wangcm@radi.ac.cn.

Abstract

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.

KEYWORDS:

GF-1 satellite; leaf area index; neural networks; radiative transfer model; wide field view

PMID:
28698464
PMCID:
PMC5539751
DOI:
10.3390/s17071593
[Indexed for MEDLINE]
Free PMC Article

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