Rui Dai

The Moon, with its stable surface reflectance and absence of an atmosphere, is an optimal reference for radiometric calibration of satellite sensors. The Robotic Lunar Observatory (ROLO) model, commonly utilized for this purpose, demonstrates phase dependent imprecision, impacting its predictive accuracy at varying phase angles. This limitation is especially problematic for instruments that cannot select the lunar phase for calibration. This study employs multi-source observational data from ground-based and Himawari-8 sensors to correct the phase dependent imprecision in the ROLO model. Analytical results reveal phase dependent imprecision of 1.89–7.08% across 470–2255 nm in the original ROLO model. After correction, this dependence is reduced to 0.24-0.37%, with a corresponding uncertainty of 3.84%. Utilizing multi-phase-angle lunar observations from FY-2H, the ROLO-corrected model successfully tracked its on-orbit degradation. The results indicate a 6.33–8.64% decline in the radiometric response of FY-2H over 6.5 years, thereby validating the model’s effectiveness in multi-phase-angle calibration. This study presents a robust lunar irradiance reference, particularly suitable for calibrating sensors limited by lunar phase selection, which is critical for long-term monitoring of satellite sensor degradation and enhancing the reliability of quantitative Earth observations.

Satellite-based drought indices have been shown to be effective and convenient in detecting drought conditions. The temperature vegetation dryness index (TVDI) is one of the most frequently used drought indices; however, it is not suitable for areas with high fractional vegetation cover (FVC). In this study, a modified temperature vegetation dryness index (mTVDI) was constructed by using the multispectral vegetation dryness index (MVDI) proposed by a PROSAIL simulation and water stress experiments which was based on the theory of the TVDI and utilized MODIS data. Compared with the TVDI, the mTVDI presents a more triangular feature space, as well as obviously increased R² values for dry and wet edges (from 0.37–0.90 to 0.53–0.91 for dry edges and from 0.00–0.77 to 0.24–0.80 for wet edges). The mTVDI was evaluated using standardized precipitation evapotranspiration indices (SPEIs), precipitation, potential evapotranspiration (PET), and the crop water deficit index (CWDI), and the results confirmed that the mTVDI can better reflect the actual spatial changes, compared to the TVDI, under high FVC, as well as presenting an increased Pearson correlation coefficient (by 0.06–0.10) when compared with SPEIs. Moreover, the good performance of the mTVDI in major drought events indicates its reliability and accuracy for drought monitoring. Overall, the mTVDI is a reliable and accurate satellite-based dryness index suitable for high FVC conditions.