Atmospheric correction can introduce errors in surface spectral reflectance, and hence induces errors in plant water estimation from remote sensing water indices. We intend to develop water indices that are less impacted by atmospheric effects for plant water content estimation based on the 970-nm water absorption feature. A simulation study using the PROSAIL and 6S models showed that uncertainty in atmospheric water vapor (WV) content can induce large variation in existing 970-nm water indices, such as WI, NWI-1, and NWI-3. An attempt was made to incorporate atmospheric WV absorption at 940 nm to correct for the perturbation due to atmospheric WV variability, leading to the development of improved indices, named as ARWI, NARWI-1, and NARWI-3. The performance of these indices was evaluated using the simulated and field spectral reflectance data, as well as Hyperion and GF5 satellite data. Results showed that the new indices were resistant to uncertainty of WV and could be used to deliver improved estimation of canopy water content, with a smaller root-mean-square-error (ARWI: 7.4 mg/cm2, NARWI-1: 8.3 mg/cm2, and NARWI-3: 8.8 mg/cm2) compared to that obtained using the traditional water indices (WI: 8.9 mg/cm2, NWI-1: 9.4 mg/cm2, and NWI-3: 16.6 mg/cm2). The water indices developed in this study, although needing further assessment in wide application scenarios, have great potential for monitoring of vegetation water status using satellite hyperspectral data with reflectance measurement around 970 nm.
The surface reflectance is an essential parameter for the quantitative applications using remote sensing satellite data; therefore, it is of great importance for the scientific community to produce standard surface reflectance products using an operational running algorithm and system. There have been various medium- to high-resolution satellites in China, yet there is still a lack of relevant surface reflectance products and systems. In this paper, high-resolution GF-1/GF-2 data from the year 2014 and 2017 were utilized for retrieval of surface reflectance products over land by using an operational atmospheric correction algorithm, adaptive to most multispectral satellites with visible and near-infrared bands (VNIR), namely, the VNIR approach. This method was based on the Second Simulation of a Satellite Signal in the Solar Spectrum, Vector (6SV) code and the look-up tables (LUTs). The surface reflectance products over land were validated against the ground-based atmospherically corrected reflectance over Beijing-Tianjin-Hebei regions and middle and lower regions of the Yangtze River in China. The preliminary validation results showed that the surface reflectance products agreed quiet well with the ground-based corrected reflectance, with the linear regression fitting coefficients being 1.09– 1.03, the correlation coefficients of R2 being 0.97–0.99, and the Root Mean Square Error (RMSE) being 0.01. Simultaneously, the mean reflectance normalized residuals between the surface reflectance products and the ground-based corrected reflectance were 19.7 %, 13.5 %, 8.7 %, and 6.6 %, respectively, indicating that the surface reflectance products over land derived from VNIR atmospheric correction approach had a good accuracy.