New Methods for Deriving Clear‐Sky Surface Longwave Downward Radiation Based on Remotely Sensed Data and Ground Measurements

Abstract

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/m 2 and bias are less than 0.5 W/m 2 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. Key Points:• A general scheme to estimate LWDR from space based only on LST and column precipitable water vapor (PWC) was provided • An alternative sensor-based Random Forest (RF) method with improved accuracy for deriving LWDR based on MODIS data was also proposed • Both methods are proved accurate (RMSE <25.5 W/m 2 , bias <0.5 W/m 2 ) and the model errors are independent of LST and PWC Supporting Information:• Supporting Information S1

Assessment of the Long-Term High-Spatial-Resolution Global LAnd Surface Satellite (GLASS) Surface Longwave Radiation Product Using Ground Measurements

“…The hybrid methods were validated using limited ground measurements during the stage of algorithm development, with biases and RMSEs of 0.06 and 21.01 W/m 2 , respectively, for LWUP, and 0.57 and 24.29 W/m 2 , respectively, for LWDN. Recently, Zhou et al [59] reported that our hybrid method for LWDN has a bias of 4.81 W/m 2 and an RMSE of 17.18 W/m 2 when verified with ground measurements that were collected from 148 sites. However, the accuracy of the produced LW radiation product remains unknown.…”