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.

Accurate knowledge of spatiotemporal characteristics of land surface energy budget is essential to understand the Earth's surface system. To some extent, the continuous development of remote sensing technology makes the estimation of global high‐resolution surface energy budget possible. However, it is still challenging to accurately derive the global longwave downward radiation (LWDR) from space. In this study, two advanced methods were proposed to estimate clear‐sky LWDR based on Moderate‐resolution Imaging Spectroradiometer (MODIS) products and corresponding ground measured LWDR from several global distributed radiation networks. One is a sensor‐based Random Forest (RF) method that uses MODIS's surface elevation, sensor zenith angle, column precipitable water vapor (PWC), top of atmosphere radiance of band 28, 31, 34, and 36 as inputs; the other is a sensor‐independent nonlinear regression method, which only uses the land surface temperature (LST) and PWC as inputs. The validation results show that the root mean square errors of RF and nonlinear regression method are less than 25.5 W/m² and bias are less than 0.5 W/m² under different tenfold cross‐validation schemes. In addition, the sensitivity analysis indicates that the two models are very stable, and the retrieval errors are independent of changes of LST and PWC, and the accuracy does not rely on variations of land cover types, different observing angles, and different seasons. The comparison analysis indicates that the proposed methods are comparable or even better than existing algorithms. More importantly, the proposed sensor‐independent regression method was designed for generality purpose so can easily be utilized with reasonable accuracy.

To improve the spatial density and quality of measurement points in multitemporal interferometric synthetic aperture radar, distributed scatterers (DSs) should be processed. An essential procedure in DS interferometry is phase estimation, which reconstructs a consistent phase series from all available interferograms. Influenced by the well-known suboptimality of coherence estimation, the performance of the state-of-the-art phase estimation algorithms is severely degraded. Previous research has addressed this problem by introducing the coherence bias correction technique. However, the precision of phase estimation is still insufficient because of the limited correction capabilities. In this paper, a modified phase estimation approach is proposed. Particularly, by incorporating the information on both interferometric coherence and the number of looks, a significant bias correction to each element of the coherence magnitude matrix is achieved. The bias-corrected coherence matrix is combined with advanced statistically homogeneous pixel selection and time series phase optimization algorithms to obtain the optimal phase series. Both the simulated and Sentinel-1 real data sets are used to demonstrate the superiority of this proposed approach over the traditional phase estimation algorithms. Specifically, the coherence bias can be corrected with considerable accuracy by the proposed scheme. The mean bias of coherence magnitude is reduced by more than 29%, and the standard deviation is reduced by more than 18% over the existing bias correction method. The proposed approach achieves higher accuracy than the current methods over the reconstructed phase series, including smoother interferometric phases and fewer outliers.

Surface energy budget has great influence on earth surface biogeophysical and biogeochemical processes. As one of the components of surface energy budget, the longwave downward radiation (LWDR) is essential to many land applications. In this research, a new LWDR estimation scheme under clear sky by integrating MODIS thermal bands radiance and in situ measurements through the multivariate adaptive regression splines (MARS) has been proposed. The MODIS products and corresponding multinetwork in situ LWDR observations under clear skies in 2004 were extracted for model training and cross-validation. The in situ LWDR observations from seven SURFRAD sites in 2008 were used to test the new method. In training phase, the cross-validation RMSE, bias, and R-square of the new method are, respectively, 24.66 W/m2, −0.446 W/m2, and 0.88 in the daytime and 23.16 W/m2, −0.272 W/m2, and 0.92 in the nighttime. The test results at the seven SURFRAD sites show that the proposed method has good performance, with RMSE, bias, and R-square of 17.15 W/m2, 0.028 W/m2, and 0.91 in the daytime and 14.08 W/m2, −3.609 W/m2, and 0.94 in the nighttime, respectively. More importantly, the error analysis reveals that the predicted residual errors of the proposed method have a weak correlation with related meteorological parameters at global scale, which reflects the stability and robustness of the new method.