Paper Info
Title
Assessment of Cornfield LAI Retrieved from Multi-Source Satellite Data Using Continuous Field LAI Measurements Based on a Wireless Sensor Network.
Abstract
Accurate and continuous monitoring of leaf area index (LAI), a widely-used vegetation structural parameter, is crucial to characterize crop growth conditions and forecast crop yield. Meanwhile, advancements in collecting field LAI measurements have provided strong support for validating remote-sensing-derived LAI. This paper evaluates the performance of LAI retrieval from multi-source, remotely sensed data through comparisons with continuous field LAI measurements. Firstly, field LAI was measured continuously over periods of time in 2018 and 2019 using LAINet, a continuous LAI measurement system deployed using wireless sensor network (WSN) technology, over an agricultural region located at the Heihe watershed at northwestern China. Then, cloud-free images from optical satellite sensors, including Landsat 7 the Enhanced Thematic Mapper Plus (ETM+), Landsat 8 the Operational Land Imager (OLI), and Sentinel-2A/B Multispectral Instrument (MSI), were collected to derive LAI through inversion of the PROSAIL radiation transfer model using a look-up-table (LUT) approach. Finally, field LAI data were used to validate the multi-temporal LAI retrieved from remote-sensing data acquired by different satellite sensors. The results indicate that good accuracy was obtained using different inversion strategies for each sensor, while Green Chlorophyll Index (CIgreen) and a combination of three red-edge bands perform better for Landsat 7/8 and Sentinel-2 LAI inversion, respectively. Furthermore, the estimated LAI has good consistency with in situ measurements at vegetative stage (coefficient of determination R-2 = 0.74, and root mean square error RMSE = 0.53 m(2) m(-2)). At the reproductive stage, a significant underestimation was found (R-2 = 0.41, and 0.89 m(2) m(-2) in terms of RMSE). This study suggests that time-series LAI can be retrieved from multi-source satellite data through model inversion, and the LAINet instrument could be used as a low-cost tool to provide continuous field LAI measurements to support LAI retrieval.
Year
DOI
Venue
2020
10.3390/rs12203304
REMOTE SENSING
Keywords
DocType
Volume
leaf area index,PROSAIL,look-up-table (LUT),multi-source satellite data,LAINet,wireless sensor network (WSN)
Journal
12
Issue
Citations 
PageRank 
20
0
0.34
References 
Authors
0
12
Name
Order
Citations
PageRank
Lihong Yu100.34
Jiali Shang217626.53
Zhiqiang Cheng301.01
Zebin Gao400.34
Zixin Wang500.68
Luo Tian601.01
Dantong Wang700.34
Tao Che8308.66
Rui Jin99016.41
Jiangui Liu103410.62
Taifeng Dong11277.86
Yonghua Qu1202.37