Snow water equivalent (SWE) is a key parameter in the Earth’s energy budget and water cycle. It has been demonstrated that SWE can be retrieved using active microwave remote sensing from space. This necessitates the development of forward models that are capable of simulating the interactions of microwaves and the snow medium. Several proposed models have described snow as a collection of sphere- or ellipsoid-shaped ice particles embedded in air, while the microstructure of snow is, in reality, more complex. Natural snow usually forms a sintered structure following mechanical and thermal metamorphism processes. In this research, the bi-continuous vector radiative transfer (bi-continuous-VRT) model, which firstly constructs snow microstructure more similar to real snow and then simulates the snow backscattering signal, is used as the forward model for SWE estimation. Based on this forward model, a parameterization scheme of snow volume backscattering is proposed. A relationship between snow optical thickness and single scattering albedo at X and Ku bands is established by analyzing the database generated from the bi-continuous-VRT model. A cost function with constraints is used to solve effective albedo and optical thickness, while the absorption part of optical thickness is obtained from these two parameters. SWE is estimated after a correction for physical temperature. The estimated SWE is correlated with the measured SWE with an acceptable accuracy. Validation against two-year measurements, using the SnowScat instrument from the Nordic Snow Radar Experiment (NoSREx), shows that the estimated SWE using the presented algorithm has a root mean square error (RMSE) of 16.59 mm for the winter of 2009–2010 and 19.70 mm for the winter of 2010–2011.

Clouds play a significant role in the derivation of land surface temperature (LST) from optical remote sensing. The estimation of LST under cloudy sky conditions has been a great challenge for the community for a long time. In this study, a scheme for recovering the LST under cloudy skies is proposed by accounting for the solar‐cloud‐satellite geometry effect, through which the LSTs of shadowed and illuminated pixels covered by clouds in the image are estimated. The validation shows that the new scheme can work well and has reasonable LST accuracy with a root mean square error < 4.9 K and bias < 3.5 K. The application of the new method to the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat‐8 data reveals that the LSTs under cloud layers can be reasonably recovered and that the fraction of valid LSTs in an image can be correspondingly improved. The method is not data specific; instead, it can be used in any optical remote sensing images as long as the proper input variables are provided. As an alternative approach to derive cloudy sky LSTs based only on optical remote sensing data, it gives some new ideas to the remote sensing community, especially in the fields of surface energy balance.

Snow depth or snow water equivalent in mountainous region is crucial for hydrology, water resources management, meteorological and climate research. Remote sensing can be used for snow depth monitoring in regional scale or global scale. However, the spaceborne remote sensing of snow depth in mountain is challenging because of the sensor sensitivity and spatial resolution problems. Recently, time series Sentinel-1 is used for snow depth retrieval in mountains, which shows encouraging accuracy. In this study, an algorithm to estimate the snow depth in mountainous regions using optical and passive microwave remote sensing observations is proposed, which can be applied in periods before and after the launch of Sentinel-1. The optical and passive microwave remote sensing observations and the Sentinel-1 derived snow depth in 2016-2021 are used to train the snow depth retrieval algorithm using the Extreme gradient boosting (XGBoost) machine learning algorithm. Validations are performed using cross validation method and independent in-situ snow depth data. The cross validation shows correlation coefficient of 0.81 and mean absolute error (MAE) of 0.17m. The correlation coefficient and MAE of predicted snow depth and in-situ snow depth in 2002-2016 are 0.61 and 0.33 m, respectively, which shows significant higher accuracy compared with AMSR-E/AMSR2 snow depth products. The site dependence of the machine learning method is also discussed. The machine learning based snow depth retrieval presented in this study can be applied to mountains globally to the optical and passive microwave remote sensing era.

Clouds and their associated shadows are major obstacles to most land surface remote sensing applications. Meanwhile, solar-cloud-satellite geometry (SCSG) makes the effect of clouds and shadows on derived land surface biophysical parameters more complicated. However, in most existing studies, the SCSG effect has been frequently neglected although it is pointed out by many works that SCSG effect is a noticeable problem, especially in the field of land surface radiation budget. Taking shortwave downward radiation (SWDR) as a testing variable, this study quantified the SCSG effect on the derived SWDR, and proposed an operational scheme to correct the big effect. The results demonstrate that the proposed correcting scheme is very effective and works very well. It is revealed that a significant under- or overestimation is detected in retrieved SWDR if the SCSG effect is ignored. Typically, the induced error in SWDR can reach up to 80%. The scheme and findings of this study are expected to be inspirational for the land surface remote sensing community, wherein solar-cloud-satellite geometry is an unavoidable issue.