Snow cover plays a crucial role in surface hydrology and energy balance, especially in the Qinghai-Tibetan Plateau (QTP). This study used 12 years (2000–2011) of ground-observed snow depth at 87 meteorological stations to assess and verify the accuracy of the daily cloud-free snow-cover product from the Moderate Resolution Imaging Spectroradiometer (MODIS) over the QTP. On average, the daily cloud-free MODIS snow-cover product correctly identified the occurrence of snow cover with an accuracy of 90.74%, ranging from 54.39% to 99.07% among the 87 sites. The MODIS-derived data have large uncertainties in identifying the snow-cover phenology on the threshold of FSC >0 and FSC >50% (FSC, fractional snow cover). However, the MODIS-derived data can capture the interannual variability of the snow-cover phenology as compared with in situ observations. This study highlights the uncertainties in the daily snow-free MODIS snow-cover product to reflect snow-cover phenology over the QTP.

An ecosystem model serves as an important tool to understand the carbon cycle in the forest ecosystem. However, the sensitivities of parameters and uncertainties of the model outputs are not clearly understood. Parameter sensitivity analysis (SA) and uncertainty analysis (UA) play a crucial role in the improvement of forest gross primary productivity GPP simulation. This study presents a global SA based on an extended Fourier amplitude sensitivity test (EFAST) method to quantify the sensitivities of 16 parameters in the Flux-based ecosystem model (FBEM). To systematically evaluate the parameters’ sensitivities, various parameter ranges, different model outputs, temporal variations of parameters sensitivity index (SI) were comprehensively explored via three experiments. Based on the numerical experiments of SA, the UA experiments were designed and performed for parameter estimation based on a Markov chain Monte Carlo (MCMC) method. The ratio of internal CO₂ to air CO₂ ( $f_C{}_i)$ , canopy quantum efficiency of photon conversion $({\alpha }_q)$ , maximum carboxylation rate at 25 $°\mathrm{C}$ ( $V_m^{25})$ were the most sensitive parameters for the GPP. It was also indicated that ${\mathsf{\alpha }}_{\mathrm{q}},E_{\mathrm{V}}{}_m^{}\mathrm{and}{Q}_{10}$ were influenced by temperature throughout the entire growth stage. The result of parameter estimation of only using four sensitive parameters (RMSE = 1.657) is very close to that using all the parameters (RMSE = 1.496). The results of SA suggest that sensitive parameters, such as $f_{ci},{\mathsf{\alpha }}_{\mathrm{q}},{\mathrm{E}}_{\mathrm{V}}{}_m^{},V_m^{25}$ strongly influence on the forest GPP simulation, and the temporal characteristics of the parameters’ SI on GPP and NEE were changed in different growth. The sensitive parameters were a major source of uncertainty and parameter estimation based on the parameter SA could lead to desirable results without introducing too great uncertainties.

The release of high-spatiotemporal-resolution Sentinel-1 Synthetic Aperture Radar (SAR) data to the public has provided an unprecedented opportunity to map soil moisture at watershed and agricultural field scales. However, the existing retrieval algorithms fail to derive soil moisture with expected accuracy. Insufficient understanding of the effects of soil and vegetation parameters on the backscatters is an important reason for this failure. To this end, we present a Sensitivity Analysis (SA) to quantify the effects of parameters on the dual-polarized backscatters of Sentinel-1 based on a Water Cloud Model (WCM) and multiple global SA methods. The identification of the incidence angle and polarization of Sentinel-1 and the description scheme of vegetation parameters (A, B and α) in WCM are especially emphasized in this analysis towards an optimal estimation of parameters. Multiple SA methods derive identical parameter importance ranks, indicating that a highly reasonable and reliable SA is performed. Comparison between two existing vegetation description schemes shows that the scheme using Vegetation Water Content (VWC) outperforms the scheme combing particle moisture content and VWC. Surface roughness, soil moisture, VWC, and B, are most sensitive on the backscatters. Variation of parameter sensitivity indices with incidence angle at different polarizations indicates that VV- and VH- polarized backscatters at small incidence angles are the optimal options for soil moisture and surface roughness estimation, respectively, while VV-polarized backscatter at larger incidence angles is well-suited for VWC and B estimation and HH-polarized backscatter is well suited for roughness estimation. This analysis improves the understanding of the effects of vegetated surface parameters on multi-angle and multi-polarized backscatters of Sentinel-1 SAR, informing improvement in SAR-based soil moisture retrieval.