Abstract
In recent years, China has developed and launched several satellites with high spatial resolutions, such as the resources satellite No. 3 (ZY-3) with a multi-spectral camera (MUX) and 5.8 m spatial resolution, the satellite GaoFen No. 1 (GF-1) with a wide field of view (WFV) camera and 16 m spatial resolution, and the environment satellite (HJ-1A/B) with a charge-coupled device (CCD) sensor and 30 m spatial resolution. First, to analyze the potential application of ZY-3 MUX, GF-1 WFV, and HJ-1 CCD to extract the leaf area index (LAI) at the regional scale, this study estimated LAI from the relationships between physical model-based spectral vegetation indices (SVIs) and LAI values that were generated from look-up tables (LUTs), simulated from the combination of the PROSPECT-5B leaf model and the scattering by arbitrarily inclined leaves with the hot-spot effect (SAILH) canopy reflectance model. Second, to assess the surface reflectance quality of these sensors after data preprocessing, the well-processed surface reflectance products of the Landsat-8 operational land imager (OLI) sensor with a convincing data quality were used to compare the performances of ZY-3 MUX, GF-1 WFV, and HJ-1 CCD sensors both in theory and reality. Apart from several reflectance fluctuations, the reflectance trends were coincident, and the reflectance values of the red and near-infrared (NIR) bands were comparable among these sensors. Finally, to analyze the accuracy of the LAI estimated from ZY-3 MUX, GF-1 WFV, and HJ-1 CCD, the LAI estimations from these sensors were validated based on LAI field measurements in Huailai, Hebei Province, China. The results showed that the performance of the LAI that was inversed from ZY-3 MUX was better than that from GF-1 WFV, and HJ-1 CCD, both of which tended to be systematically underestimated. In addition, the value ranges and accuracies of the LAI inversions both decreased with decreasing spatial resolution.
Highlights
Leaf area index (LAI) is defined as one-half the total foliage area per unit ground surface area [1], and it is an important parameter for monitoring vegetation growth conditions [2,3]
The spatial representativeness of the leaf area index (LAI) field measurements was first assessed by the relative absolute error (RAE) and coefficient of sill (CS), according to the methods in Section 3.3 to determine the differences between the LAI inversions from the normalized difference vegetation index (NDVI)-LAI relationships for ZY-3 MUX, GaoFen No. 1 (GF-1) wide field of view (WFV), and HJ-1 charge coupled device (CCD) with different spatial resolutions
This study analyzed the application of LAI inversed from ZY-3 MUX, GF-1 WFV, and HJ-1 CCD
Summary
Leaf area index (LAI) is defined as one-half the total foliage area per unit ground surface area [1], and it is an important parameter for monitoring vegetation growth conditions [2,3]. LAI is a common variable that is used for regional and global climate, ecological, and hydrological models [4,5]. LAI has been widely used in global primary productivity measurements [6], agricultural yield. High-spatial-resolution LAI products play important roles in monitoring regional vegetation changes and evaluating the accuracy of low- resolution LAI products [10,11,12]. LAI extracted from this moderate- to high-resolution imagery largely depends on the empirical relationships
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
