Comparative Assessment of Satellite-Retrieved Surface Net Radiation: An Examination on CERES and SRB Datasets in China

Influence paradigms of soil moisture on land surface energy partitioning under different climatic conditions

Soil moisture (SM) directly controls the land surface energy partition which plays an important role in the formation of extreme weather events. However, its dependence on specific climatic conditions is not thoroughly understood due to the complexity of soil moisture effects. Here, we examine the relationship between SM and surface energy partitioning under different climate conditions, and identify the influence paradigms of soil moisture on surface energy partition. We find that temperature changes can explicitly determine the impact paradigm of different physical processes, i.e. evapotranspiration, soil freezing and thawing, and such influence paradigms are also affected by atmospheric aridity (VPD). Globally, there are five paradigms that effects on surface energy partitioning, including the warm-wet paradigm (WW), transitional paradigm (TP), warm-dry paradigm (WD), cool-wet paradigm (CW) and cold paradigm (CP). Since 1981, the global area proportion for TP is observed to increase pronouncedly. We also find that the critical SM threshold exhibits regional variations and the global average is 0.45 m3/m3. The identified paradigms and their long-term change trends provide new insights into the global intensification of land-atmosphere interaction, which has important implications for global warming and the formation of heatwaves.

The accuracy improvement of clear-sky surface shortwave radiation derived from CERES SSF dataset with a simulation analysis

Towards the Xiaotang region along the northern margin of the China's largest desert, a quantitative assessment of the precision of clear-sky satellite observations (the Single Scanner Footprint TOA/Surface Fluxes and Clouds downward surface shortwave radiation product of Clouds and the Earth's Radiant Energy System (CERES), DSSR_CER) is conducted, the localized inversion mode of "absolutely clear-sky" downward surface shortwave radiation (DSSR) is established, and the "absolutely clear-sky" DSSR in Xiaotang during 2005-2018 is simulated by the Santa Barbara Discrete Atmospheric Radiative Transfer (SBDART) model. In general, under the "absolutely clear-sky" condition of Xiaotang region, there is a significant error in DSSR_CER, and the simulated results of SBDART (DSSR_SBD) with same input parameters as DSSR_CER is better and more comparable. Single scattering albedo (SSA), asymmetry parameter (ASY) and aerosol optical depth (AOD) play crucial roles in deciding the accuracy of DSSR, and after parameter adjustment, the DSSR_SBD is better than the initial, which is improved remarkably with all indexes of the fitting results greatly improved. The temporal variation of the DSSR during 2005-2018 indicates that the highest annual average value is found in 2008 (770.00 W·m^-2), while the lowest appears in 2010 (600.97 W·m^-2). Besides, the highest seasonal mean DSSR appears in summer, which between 860.6 and 935.07 W·m^-2, while reaches the lowest in winter (403.79-587.53 W·m^-2). Moreover, the monthly average DSSR changes as a curve with a single peak and is close to normal distribution, the highest appears in June (934.61 W·m^-2), while the minimum with the value of 390.34 W·m^-2 is found in December. All of the solar elevation angle, the characteristics of climate and aerosol particles in different seasons may contribute to the temporal variation.

Discrepancies in the Simulated Global Terrestrial Latent Heat Flux from GLASS and MERRA-2 Surface Net Radiation Products

Surface all-wave net radiation (Rn) is a crucial variable driving many terrestrial latent heat (LE) models that estimate global LE. However, the differences between different Rn products and their impact on global LE estimates still remain unclear. In this study, we evaluated two Rn products, Global LAnd Surface Satellite (GLASS) beta version Rn and Modern-Era Retrospective Analysis for Research and Applications-version 2 (MERRA-2) Rn, from 2007–2017 using ground-measured data from 240 globally distributed in-situ radiation measurements provided by FLUXNET projects. The GLASS Rn product had higher accuracy (R2 increased by 0.04–0.26, and RMSE decreased by 2–13.3 W/m2) than the MERRA-2 Rn product for all land cover types on a daily scale, and the two Rn products differed greatly in spatial distribution and variations. We then determined the resulting discrepancies in simulated annual global LE using a simple averaging model by merging five diagnostic LE models: RS-PM model, SW model, PT-JPL model, MS-PT model, and SIM model. The validation results showed that the estimated LE from the GLASS Rn had higher accuracy (R2 increased by 0.04–0.14, and RMSE decreased by 3–8.4 W/m2) than that from the MERRA-2 Rn for different land cover types at daily scale. Importantly, the mean annual global terrestrial LE from GLASS Rn was 2.1% lower than that from the MERRA-2 Rn. Our study showed that large differences in satellite and reanalysis Rn products could lead to substantial uncertainties in estimating global terrestrial LE.

Applicability of Downscaling Land Surface Temperature by Using Normalized Difference Sand Index

Land surface temperature (LST) in coarse spatial resolution derived from thermal infrared satellite images has limited use in many remote sensing applications. In this study, we improve our previous approach (multiple remote-sensing index approach of random forest) to downscale LST derived from Landsat 8 and MODIS in an arid oasis - desert ecotone of Zhangye city by designing a normalized difference sand index (NDSI), by the removal of land cover datasets and by the input of SAVI, NDBI and NDWI to downscale LST. Our result demonstrates that NDSI can determine the characteristic of the desert region, and that the distribution of downscaled LST matches those of oasis-desert ecosystems. Relative to the ground observation of HiWATER, our approach also produces relatively satisfactory downscaling results at July 21 (2013), with R² and root-mean-square error of 0.99 and 1.25 K, respectively. Compared with other methods, our approach demonstrates higher accuracy and minimization of the retrieved Landsat 8 LST in the desert region. Optimal availability occurs in the vegetation and desert region. Our approach is suitable to LST downscaling in all seasons, especially in spring and summer. The model can further be applied in middle-high and middle-low spatial resolutions. The usefulness of the model is relatively satisfactory in the humid region (Nanjing city) but less accurate in the arid region.

Water, Energy, and Carbon with Artificial Neural Networks (WECANN): A statistically-based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux (H), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed Solar-Induced Fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimates of LE, H and GPP from 2007 to 2015 at 1° × 1° spatial resolution and on monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analysing WECANN retrievals across three extreme drought and heatwave events demonstrates the capability of the retrievals in capturing the extent of these events. Uncertainty estimates of the retrievals are analysed and the inter-annual variability in average global and regional fluxes show the impact of distinct climatic events - such as the 2015 El Niño - on surface turbulent fluxes and GPP.

Downscaling Land Surface Temperature in Complex Regions by Using Multiple Scale Factors with Adaptive Thresholds

Many downscaling algorithms have been proposed to address the issue of coarse-resolution land surface temperature (LST) derived from available satellite-borne sensors. However, few studies have focused on improving LST downscaling in urban areas with several mixed surface types. In this study, LST was downscaled by a multiple linear regression model between LST and multiple scale factors in mixed areas with three or four surface types. The correlation coefficients (CCs) between LST and the scale factors were used to assess the importance of the scale factors within a moving window. CC thresholds determined which factors participated in the fitting of the regression equation. The proposed downscaling approach, which involves an adaptive selection of the scale factors, was evaluated using the LST derived from four Landsat 8 thermal imageries of Nanjing City in different seasons. Results of the visual and quantitative analyses show that the proposed approach achieves relatively satisfactory downscaling results on 11 August, with coefficient of determination and root-mean-square error of 0.87 and 1.13 °C, respectively. Relative to other approaches, our approach shows the similar accuracy and the availability in all seasons. The best (worst) availability occurred in the region of vegetation (water). Thus, the approach is an efficient and reliable LST downscaling method. Future tasks include reliable LST downscaling in challenging regions and the application of our model in middle and low spatial resolutions.

Water, Energy, and Carbon with Artificial Neural Networks (WECANN): A statistically-based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux (H), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed Solar-Induced Fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimates of LE, H and GPP from 2007 to 2015 at 1° × 1° spatial resolution and on monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analysing WECANN retrievals across three extreme drought and heatwave events demonstrates the capability of the retrievals in capturing the extent of these events. Uncertainty estimates of the retrievals are analysed and the inter-annual variability in average global and regional fluxes show the impact of distinct climatic events - such as the 2015 El Niño - on surface turbulent fluxes and GPP.