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.

The land surface shortwave downward radiation (SWDR), also known as surface incident solar radiation, is usually defined as the total solar spectral energy between 300 nm and 3000 nm that arrives at the earth's surface. The SWDR is an essential variable for numerous land models and thus need to be accurately mapped using various approaches. Although numerous efforts have been made to assess the applicability and accuracy of the SWDR datasets produced by satellite and numeric models, few studies have evaluated the performance of the SWDR products from Himawari-8. In this paper, the Himawari-8 SWDR products with both 1 km and 5 km spatial scales were evaluated using ground measurements. The validation results indicate that both of the Himawari-8 SWDR products are in close agreement with ground measurements, even though obvious biases are found. Also, an in-depth intercomparison between the Himawari-8 SWDR product at a 5 km resolution and three other SWDR radiation datasets (CERES-SYN, MERRA-2, and ERA-Interim) was conducted. Here, the Himawari-8 showed the highest levels of accuracy among the four SWDR datasets. Considering its higher temporal and spatial resolutions, the Himawari-8 SWDR products are more detailed and provide the unprecedented potential to improve land surface modeling and reveal the cloud-radiation interactions, even at a 10-min temporal scale.

Near surface air temperature (Ta) is one of the key input parameters in land surface models and hydrological models as it affects most biogeophysical and biogeochemical processes of the earth surface system. For distributed hydrological modeling over glacierized basins, obtaining high resolution Ta forcing is one of the major challenges. In this study, we proposed a new high resolution daily Ta estimation scheme under both clear and cloudy sky conditions through integrating the moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and China Meteorological Administration (CMA) land data assimilation system (CLDAS) reanalyzed daily Ta. Spatio-temporal continuous MODIS LST was reconstructed through the data interpolating empirical orthogonal functions (DINEOF) method. Multi-variable regression models were developed at CLDAS scale and then used to estimate Ta at MODIS scale. The new Ta estimation scheme was tested over the Langtang Valley, Nepal as a demonstrating case study. Observations from two automatic weather stations at Kyanging and Yala located in the Langtang Valley from 2012 to 2014 were used to validate the accuracy of Ta estimation. The RMSEs are 2.05, 1.88, and 3.63 K, and the biases are 0.42, −0.68 and −2.86 K for daily maximum, mean and minimum Ta, respectively, at the Kyanging station. At the Yala station, the RMSE values are 4.53, 2.68 and 2.36 K, and biases are 4.03, 1.96 and −0.35 K for the estimated daily maximum, mean and minimum Ta, respectively. Moreover, the proposed scheme can produce reasonable spatial distribution pattern of Ta at the Langtang Valley. Our results show the proposed Ta estimation scheme is promising for integration with distributed hydrological model for glacier melting simulation over glacierized basins.

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.

Satellite measurements of the spatiotemporal distributions of atmospheric CO₂ concentrations are a key component for better understanding global carbon cycle characteristics. Currently, several satellite instruments such as the Greenhouse gases Observing SATellite (GOSAT), SCanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY), and Orbiting Carbon Observatory-2 can be used to measure CO₂ column-averaged dry air mole fractions. However, because of cloud effects, a single satellite can only provide limited CO₂ data, resulting in significant uncertainty in the characterization of the spatiotemporal distribution of atmospheric CO₂ concentrations. In this study, a new physical data fusion technique is proposed to combine the GOSAT and SCIAMACHY measurements. On the basis of the fused dataset, a gap-filling method developed by modeling the spatial correlation structures of CO₂ concentrations is presented with the goal of generating global land CO₂ distribution maps with high spatiotemporal resolution. The results show that, compared with the single satellite dataset (i.e., GOSAT or SCIAMACHY), the global spatial coverage of the fused dataset is significantly increased (reaching up to approximately 20%), and the temporal resolution is improved by two or three times. The spatial coverage and monthly variations of the generated global CO₂ distributions are also investigated. Comparisons with ground-based Total Carbon Column Observing Network (TCCON) measurements reveal that CO₂ distributions based on the gap-filling method show good agreement with TCCON records despite some biases. These results demonstrate that the fused dataset as well as the gap-filling method are rather effective to generate global CO₂ distribution with high accuracies and high spatiotemporal resolution.

Surface downward longwave radiation (SDLR) is significant with regard to surface energy budgets and climate research. The uncertainty of cloud base height (CBH) retrieval by remote sensing induces the vast majority of SDLR estimation errors under cloudy conditions; reliable CBH observation and estimation are crucial for determining the cloud radiative effect. This study presents a CBH retrieval methodology built from 10 thermal spectral data from Himawari-8 (H-8) observations, utilizing the random forest (RF) algorithm to fully account for each band’s contribution to CBH. The algorithm utilizes only infrared band data, making it possible to obtain CBH 24 h a day. Considering some factors that can significantly affect the CBH estimation, RF models are trained for different clouds using inputs from multiple H-8 channels together with geolocation information to target CBH derived from CloudSat/CALIPSO combined measurements. The validation results reveal that the new methodology performs well, with a root-mean-square error (RMSE) of only 1.17 km for all clouds. To evaluate the effect of CBH on SDLR estimation, an all-sky SDLR estimation algorithm based on previous CBH predictions is proposed. The new SDLR product not only has a resolution that is noticeably higher than that of benchmark products of the SDLR, such as the Clouds and the Earth’s Radiant Energy System (CERES) and the next-generation reanalysis (ERA5) of the European Centre for Medium-Range Weather Forecasts (ECMWF), but it also has greater accuracy, with an RMSE of 21.8 W m⁻² for hourly surface downward longwave irradiance (SDLI).