Bias Correction of Sentinel-2 MSI Vegetation Indices in a Desert Steppe With Original Assembled Field Online Multiangle Spectrometers

In desert steppe regions with sparse vegetation, there are discrepancies between vertical and oblique observations made by satellite-based sensors. In this study, we developed and deployed an online multiangle spectrometer in the desert steppe area of Inner Mongolia, China, to calibrate satellite-based vegetation indices. One of the key components of the device is a specially designed quarter-arc iron track that holds the fixed view angles of 30°, 45°, 60°, 75°, and 90° that are fixed. These observation positions equipped with high-efficiency multichannel sensors can capture the reflectance of ground objects at visible and near-infrared wavelengths. Real-time experiments were conducted with multiple observation angles and an error-based view angle correction model was constructed to reconcile the differences between angular and vertical observations. The calculated results were subsequently applied to the bias-correction process of the Sentinel-2 vegetation index. Across all view angles, the daily distributions of the normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) exhibited a U-shaped pattern with the nadir occurring at noon. Among the data, RVI demonstrated superior overall stability compared to NDVI. However, with vegetation growth, the NDVI showed less sensitivity, resulting in a decrease in its coefficient of variation (CV) from 32.3% to 18.2%. To correct the bias in Sentinel-2 products, we initially applied path length correction (PLC) to eliminate the topographic influence on the Band4 and Band8 band reflectance. Our findings revealed that Band8 performed better than Band4 in mitigating the effects of topography, as evidenced by a decrease in the determination coefficient from 0.448 to 0.09. In addition, we corrected the view angle error of the NDVI of Sentinel-2 by constructing a view angle correction model ( $R^{2}$ = 0.84 and RMSE = 0.03). The corrected images exhibited significant variation characteristics in areas with relatively large view angles. The results of this study provide valuable scientific support for the correction of satellite image products and the accurate detection of vegetation through remote sensing in regions with low vegetation coverage.