Publications

Jiang, HY; Jia, K; Wang, Q; Shang, JL; Liu, JG; Xie, XH; Dong, TF (2023). Angular Effect Correction for Improved LAI and FVC Retrieval Using GF-1 Wide Field View Data. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 61, 4407414.

Abstract
Leaf area index (LAI) and fractional vegetation cover (FVC) are two essential vegetation parameters for ecological and climate studies. The Chinese Gaofen-1 (GF-1) wide field view (WFV) satellite data are a valuable data source for LAI and FVC retrieval at high spatiotemporal resolution. Like its name, GF-1 WFV has very large view angle ranging from 0 degrees to 48 degrees, which can impact the accuracy of vegetation parameter retrieval. The primary aim of the study was to develop an angular effect correction (anisotropy flat index, AFX-fix) method that can effectively normalize GF-1 WFV data. Our objective was to enhance the applicability of the corrected data in retrieving LAI and FVC. The AFX-fix method used angular index, AFX, and a fixed set of bidirectional reflectance distribution function (BRDF) parameters. LAI and FVC were retrieved from the GF-1 WFV reflectance data using the PROSPECT+SAIL (PROSAIL) model combined with a random forest method. Results showed that the accuracy of LAI and FVC retrieval in wheat and corn from angular corrected GF-1 WFV data was improved with a decrease in root-mean-square error (RMSE) by 0.66 for LAI and 0.03 for FVC compared to that based on the original data. We anticipate that this new method will help improve the performance of LAI retrieval of these crop types using WFV data.

DOI:
10.1109/TGRS.2023.3304531

ISSN:
1558-0644