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Remote Sens. 2017, 9(1), 13; doi:10.3390/rs9010013

A Prior Knowledge-Based Method to Derivate High-Resolution Leaf Area Index Maps with Limited Field Measurements

1,2
,
1,2,* , 1,2
and
1,2
1
State Key Laboratory of Remote Sensing Science, Research Center for Remote Sensing and GIS, School of Geography, Beijing Normal University, Beijing 100875, China
2
Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Academic Editors: Devrim Akca, Jose Moreno and Prasad S. Thenkabail
Received: 14 July 2016 / Revised: 27 November 2016 / Accepted: 21 December 2016 / Published: 27 December 2016
View Full-Text   |   Download PDF [3684 KB, uploaded 27 December 2016]   |  

Abstract

High-resolution leaf area index (LAI) maps from remote sensing data largely depend on empirical models, which link field LAI measurements to the vegetation index. The existing empirical methods often require the field measurements to be sufficient for constructing a reliable model. However, in many regions of the world, there are limited field measurements available. This paper presents a prior knowledge-based (PKB) method to derivate LAI with limited field measurements, in an effort to improve the accuracy of empirical model. Based on the assumption that the experimental sites with the same vegetation type can be represented by similar models, a priori knowledge for crops was extracted from the published models in various cropland sites. The knowledge, composed of an initial guess of each model parameter with the associated uncertainty, was then combined with the local field measurements to determine a semi-empirical model using the Bayesian inversion method. The proposed method was evaluated at a cropland site in the Huailai region of Hebei Province, China. Compared with the regression method, the proposed PKB method can effectively improve the accuracy of empirical model and LAI estimation, when the field measurements were limited. The results demonstrate that a priori knowledge extracted from the universal sites can provide important auxiliary information to improve the representativeness of the empirical model in a given study area. View Full-Text
Keywords: leaf area index (LAI); prior knowledge; semi-empirical model; limited field measurements leaf area index (LAI); prior knowledge; semi-empirical model; limited field measurements
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Shi, Y.; Wang, J.; Wang, J.; Qu, Y. A Prior Knowledge-Based Method to Derivate High-Resolution Leaf Area Index Maps with Limited Field Measurements. Remote Sens. 2017, 9, 13.

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