A global long term (1981–2019) daily land surface radiation budget product from AVHRR satellite data using a residual convolutional neural network

The surface radiation budget, also known as all-wave net radiation (Rn), is a key parameter for various land surface processes including hydrological, ecological, agricultural, and biogeochemical processes. Satellite data can be effectively used to estimate Rn, but existing satellite products have coarse spatial resolutions and limited temporal coverage. In this study, a point-surface matching estimation (PSME) method is proposed to estimate surface Rn using a residual convolutional neural network (RCNN) integrating spatially adjacent information to improve the accuracy of retrievals. A global high-resolution (0.05°) long-term (1981–2019) Rn product was subsequently generated from Advanced Very High-Resolution Radiometer (AVHRR) data. Specifically, the RCNN was employed to establish a nonlinear relationship between globally distributed ground measurements from 537 sites and AVHRR top of atmosphere (TOA) observations. Extended triplet collocation (ETC) technology was applied to address the spatial scale mismatch issue resulting from the low spatial support of ground measurements within the AVHRR footprint by selecting reliable sites for model training. The overall independent validation results show that the generated AVHRR Rn product is highly accurate, with R2, root-mean-square error (RMSE), and bias of 0.84, 26.66 Wm−2 (31.66 %), and 1.59 Wm−2 (1.89 %), respectively. Inter-comparisons with three other Rn products, i.e., the 5 km Global Land Surface Satellite (GLASS), the 1° Clouds and the Earth's Radiant Energy System (CERES), and the 0.5° × 0.625° Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), illustrate that our AVHRR Rn retrievals have the best accuracy under all of the considered surface and atmospheric conditions, especially thick cloud or hazy conditions. The spatiotemporal analyses of these four Rn datasets indicate that the AVHRR Rn product reasonably replicates the spatial pattern and temporal evolution trends of Rn observations. This dataset is freely available at https://doi.org/10.5281/zenodo.5509854 for 1981–2019 (Xu et al., 2021).

Assessment of the RegCM4 Performance in Simulating the Surface Radiation Budget and Hydrologic Balance Variables in South America

The ability of the Regional Climate Model v4 (RegCM4) to simulate the surface radiation budget and hydrological balance variables over South America have been evaluated. For this purpose, a 34-year long simulation was carried out with the regional climate model RegCM4 over South America on the CORDEX domain. The model is forcing by ERA-Interim reanalysis. The results show that RegCM4 simulates the main patterns of the variables associated with the surface radiation budget and hydrological balance in the four seasons of the year compared to the observations (CLARA2 and CRU/PERSIANN). However, the cloudiness and surface radiation budget variables: Cloud Fraction Cover (CFC), net shortwave (SW) and longwave (LW) radiation at surface are overestimated, mainly over the oceans. This is associated with the errors in the CFC due to the deficiency of the model in representing the low-level clouds. Some differences are also noted in the hydrological balance. The intensity and temporal evolution of precipitation, especially in the central and southern Amazon, may be associated with the selected domain, which fails to adequately represent the influence of the adjoining oceans. In northern and northeast parts, the differences are associated with deficiencies of RegCM4 in representing precipitation rates. Although the deficiencies, taking into account that the model is capable to reproduce the general pattern of some important variables of the surface radiation budget and hydrological cycle, it may be a useful tool for climate studies.

Retrieving Crop Albedo Based on Radar Sentinel-1 and Random Forest Approach

Monitoring agricultural crops is of paramount importance for preserving water resources and increasing water efficiency over semi-arid areas. This can be achieved by modelling the water resources all along the growing season through the coupled water–surface energy balance. Surface albedo is a key land surface variable to constrain the surface radiation budget and hence the coupled water–surface energy balance. In order to capture the hydric status changes over the growing season, optical remote sensing becomes impractical due to cloud cover in some periods, especially over irrigated winter crops in semi-arid regions. To fill the gap, this paper aims to generate cloudless surface albedo product from Sentinel-1 data that offers a source of high spatio-temporal resolution images. This can help to better capture the vegetation development along the growth season through the surface radiation budget. Random Forest (RF) algorithm was implemented using Sentinel-1 backscatters as input. The approach was tested over an irrigated semi-arid zone in Morocco, which is known by its heterogeneity in term of soil conditions and crop types. The obtained results are evaluated against Landsat-derived albedo with quasi-concurrent Landsat/Sentinel-1 overpasses (up to one day offset), while a further validation was investigated using in situ field scale albedo data. The best model-hyperparameters selection was dependent on two validation approaches (K-fold cross-validation ‘k = 10’, and holdout). The more robust and accurate model parameters are those that represent the best statistical metrics (root mean square error ‘RMSE’, bias and correlation coefficient ‘R’). Coefficient values ranging from 0.70 to 0.79 and a RMSE value between 0.0002 and 0.00048 were obtained comparing Landsat and predicted albedo by RF method. The relative error ratio equals 4.5, which is acceptable to predict surface albedo.

The Interplay between Canopy Structure and Topography and Its Impacts on Seasonal Variations in Surface Reflectance Patterns in the Boreal Region of Alaska—Implications for Surface Radiation Budget

Forests play an essential role in maintaining the Earth’s overall energy balance. The variability in forest canopy structure, topography, and underneath vegetation background conditions create uncertainty in modeling solar radiation at the Earth’s surface, particularly for boreal regions in high latitude. The purpose of this study is to analyze seasonal variation in visible, near-infrared, and shortwave infrared reflectance with respect to land cover classes, canopy structures, and topography in a boreal region of Alaska. We accomplished this investigation by fusing Landsat 8 images and LiDAR-derived canopy structural data and multivariate statistical analysis. Our study shows that canopy structure and topography interplay and influence reflectance spectra in a complex way, particularly during the snow season. We observed that deciduous trees, also tall with greater rugosity, are more dominant on the southern slope than on the northern slope. Taller trees are typically seen in higher elevations regardless of vegetation types. Surface reflectance in all studied wavelengths shows similar relationships with canopy cover, height, and rugosity, mainly due to close connections between these parameters. Visible and near-infrared reflectance decreases with canopy cover, tree height, and rugosity, especially for the evergreen forest. Deciduous forest shows more considerable variability of surface reflectance in all studied wavelengths, particularly in March, mainly due to the mixing effect of snow and vegetation. The multivariate statistical analysis demonstrates a significant tree shadow effect on surface reflectance for evergreen forests. However, the topographic shadow effect is prominent for deciduous forests during the winter season. These results provide great insight into understanding the role of vegetation structure and topography in surface radiation budget in the boreal region.

Interplay Between Canopy Structure and Topography and Its Impacts on Seasonal Variations in Surface Reflectance in the Boreal Region of Alaska – Implication for Surface Radiation Budget

Forests are critical in regulating the world’s climate and they maintain overall Earth’s energy balance. The variability in forest canopy structure, topography and underneath vegetation background condition creates uncertainty in the estimation and modelling of Earth’s surface radiation particularly for boreal regions in high latitude. We studied seasonal variation in surface reflectance with respect to land cover classes, canopy structures, and topography in a boreal region of Alaska by fusing together Landsat 8 surface reflectance and LiDAR-derived canopy matrices. Our study shows that canopy structure and topography interplay and influence surface reflectance in a complex way particularly during the snow season. Topographic aspect and elevation control vegetation growth, type and structure. The southern slope is featured with more deciduous and taller trees having greater rugosity than the northern slope. Higher elevation is associated with taller trees for both vegetation types, particularly in the southern slope. In general, surface reflectance shows similar relationships with canopy cover, height and rugosity, mainly due to close relationships between these parameters. Surface reflectance decreases with canopy cover, tree height, and rugosity especially for evergreen forest. Deciduous forest shows larger variability of surface reflectance, particularly in March, mainly due to the mixing effect of snow and vegetation. The relationship between vegetation structure and surface reflectance is greatly impacted by topography. The negative relationship between elevation and surface reflectance may be due to taller and denser vegetation distribution in higher elevation. Surface reflectance in the southern slope is slightly larger than the northern slope for both deciduous and evergreen forest. The shadow effect from topography and tree crowns on surface reflectance play a different role for deciduous and evergreen forests. For deciduous forest, topographic shadow effect on surface reflectance is stronger than from tree shadowing in all seasons. For evergreen forest, shadow effects from topography and tree crowns on surface reflectance are both equally dominant, however tree shadow effect is more significant in March than in May and August. The generalized additive models (GAM) based on non-linear relationships between response (surface reflectance) and predictor (canopy structures and topography) variables confirms such observations. Our study not only provides accurate quantification of surface radiation budget but also helps in parametrization of climate change models.