A direct algorithm for estimating clear-sky surface longwave net radiation (SLNR) from MODIS imagery

长波 天空 纬度 环境科学 发射长波辐射 气象学 技术 大气科学 气候学 遥感 辐射 物理 地质学 大地测量学 光学 地球物理学 电离层 对流
作者
Jie Cheng,Zeng Qi,Jiancheng Shi
出处
期刊:International Journal of Remote Sensing [Taylor & Francis]
卷期号:43 (5): 1655-1683 被引量:5
标识
DOI:10.1080/01431161.2022.2048116
摘要

Surface longwave net radiation (SLNR) is the key component of the surface radiation budget. SLNR is estimated by using the conventional method, in which surface longwave downward radiation (SLDR) and surface longwave upward radiation (SLUR) are estimated separately, and then SLNR is calculated by subtracting SLUR from SLDR. However, uncertainties in SLDR and SLUR may accumulate and propagate into the calculated SLNR, affecting the SLNR estimate accuracy. This paper proposes a direct algorithm that links clear-sky SLNR with the top-of-atmosphere (TOA) radiances in MODIS channels 29, 31 and 32 and column water vapour (CWV). The multivariate regression (MVR) and extreme gradient boosting (×GBoost) were employed to establish the relationships sequentially with collocated ground measurements and satellite data for high-latitude, mid-latitude, and low-latitude areas. According to the training and validation results, the GBoost model performs slightly better than the MVR model, which indicates that weak nonlinearity exists between clear-sky SLNR and its predictors. The absolute biases of the XGBoost models are approximately 1.0 Wm−2, and the RMSEs of the XGBoost model are 17.77, 27.33, and 19.60 Wm−2 in high-latitude, mid-latitude, and low-latitude areas. These results indicate that nonlinear relationships exist between clear-sky SLNR and predictors, but this nonlinearity is not strong. The superiority of the direct algorithm over the conventional method was also demonstrated. In the future, we will collect more data to improve the performance of the proposed direct algorithm especially in high-latitude and mid-latitude areas.
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