Abstract
The surface all-wave net radiation (Rn) plays an important role in the energy and water cycles, and most studies of Rn estimations have been conducted using satellite data. As one of the most commonly used satellite data sets, Moderate Resolution Imaging Spectroradiometer (MODIS) data have not been widely used for radiation calculations at mid-low latitudes because of its very low revisit frequency. To improve the daily Rn estimation at mid-low latitudes with MODIS data, four models, including three models built with random forest (RF) and different temporal expansion models and one model built with the look-up-table (LUT) method, are used based on comprehensive in situ radiation measurements collected from 340 globally distributed sites, MODIS top-of-atmosphere (TOA) data, and the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data from 2000 to 2017. After validation against the in situ measurements, it was found that the RF model based on the constraint of the daily Rn from ERA5 (an RF-based model with ERA5) performed the best among the four proposed models, with an overall validated root-mean-square error (RMSE) of 21.83 Wm−2, R2 of 0.89, and a bias of 0.2 Wm−2. It also had the best accuracy compared to four existing products (Global LAnd Surface Satellite Data (GLASS), Clouds and the Earth’s Radiant Energy System Edition 4A (CERES4A), ERA5, and FLUXCOM_RS) across various land cover types and different elevation zones. Further analyses illustrated the effectiveness of the model by introducing the daily Rn from ERA5 into a “black box” RF-based model for Rn estimation at the daily scale, which is used as a physical constraint when the available satellite observations are too limited to provide sufficient information (i.e., when the overpass time is less than twice per day) or the sky is overcast. Overall, the newly-proposed RF-based model with ERA5 in this study shows satisfactory performance and has strong potential to be used for long-term accurate daily Rn global mapping at finer spatial resolutions (e.g., 1 km) at mid-low latitudes.
Highlights
Afterwards, the values, usingusing the random forestforest (RF) and Afterwards, the perforR _ Rn_ins performances of the four new models were evaluated and further analyses conducted with mances of the four new models were evaluated and further analyses conducted with the the the results model compared products bestbest one;one; the results fromfrom this this model werewere thenthen compared withwith otherother products and and prepreliminarily used mapping
The results here and in the previous study demonstrate the limitations of using simand validation ulated values. accuracy of the RF-based ins model, which yields root-meansquare error (RMSE) of 78.06 and
0.91, the smallest RMSEs of 21.83 and 18.86 Wm−2, and biases of 0.20 and 0.28 Wm−2. These results demonstrate that the performance of the RF-based model could be effectively improved by introducing the ERA5 Rn_daily values, which results in a reduction in the values of the RMSE of 1.04 and 1.07 Wm−2 and in bias of 0.05 and 0.08 Wm−2 (Figure 8e,f)
Summary
Where Rns , Rnl , R↓s , R↑s , R↓l , and R↑l are the land surface net shortwave radiation (Wm−2 , where downward is defined as positive), net longwave radiation (Wm−2 ), downward shortwave radiation (Wm−2 ), upward shortwave radiation (Wm−2 ), downward longwave radiation (Wm−2 ), and upward longwave radiation (Wm−2 ), respectively, and α is the land surface broadband albedo. Reanalysis products (e.g., ECMWF Interim Reanalysis from the European Centre for Medium-Range Weather Forecasts [8]) are derived by merging available observations with an atmospheric model [10], and despite their longer temporal spans, they are usually at coarse resolutions and are thought to be less accurate than products obtained from remotely sensed products [11], where the poorer accuracies are possibly caused by inaccurate cloud information [11,12,13]
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