A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. Issue 4 (16th February 2022)

• Record Type: Journal Article
• Authors: Xiao, Zhiqiang; Song, Jinling; Yang, Hua; Sun, Rui; Li, Juan
• Is Part Of: International journal of remote sensing. Volume 43:Issue 4(2022)
• Page Start: 1409
• Page End: 1429
• Publication Date: 2022-02-16
• Subjects: MUSES -- leaf area index -- MODIS -- general regression neural networks -- validation -- 250 m; Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2022.2039415
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: There are several global leaf area index (LAI) products currently available. The spatial resolution of these products is 500 m and above, which is unsuitable for many applications requiring higher spatial resolution. In the past several years, we developed a method to estimate the LAI from time series satellite remote sensing data using general regression neural networks. The method has been used to generate global LAI products at 500 m and 1000 m from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data, and a global LAI product at 0.05° from Advanced Very High Resolution Radiometer (AVHRR) surface reflectance data. In this study, the method was extended to generate a global LAI product at 250 m (one of the MUltiscale Satellite remotE Sensing (MUSES) product suite) from MODIS surface reflectance data in the red and near-infrared (NIR) bands. As far as we know, it is the first global LAI product at 250 m spatial resolution and is the highest spatial resolution global LAI product available. The spatial and temporal consistency of the MUSES LAI product was evaluated by comparing it with the MODIS LAI product, and the MUSES LAI product was validated by high-resolution reference maps at the Validation of Land European Remote Sensing Instruments (VALERI) and Implementing Multi-Scale Agricultural Indicators Exploiting Sentinels (IMAGINES) sites representative of different biomes. The root mean square error (RMSE) of the MUSES LAI product versus the LAI values derived from the high-resolution reference maps over the VALERI and IMAGINES sites was 0.9984, and the bias of the MUSES LAI product was −0.2005.