多颗极轨卫星观测资料融合估算逐日地面气温的融合框架。

Merging framework for estimating daily surface air temperature by integrating observations from multiple polar-orbiting satellites.

机构信息

School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China.

School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China; Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, China; Key Laboratory of Digital Mapping and Land Information Application Engineering, National Administration of Surveying, Mapping and Geo-Information, Wuhan University, Wuhan 430079, China; Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China.

出版信息

Sci Total Environ. 2022 Mar 15;812:152538. doi: 10.1016/j.scitotenv.2021.152538. Epub 2021 Dec 23.

DOI:10.1016/j.scitotenv.2021.152538

PMID:34953831

Abstract

Reconstructing spatially continuous surface air temperature (SAT) is of great significance to climate and environmental studies. Substantial efforts have been made to estimate daily SAT based on land surface temperature (LST) derived from polar-orbiting satellites. However, previous studies are nearly all limited to estimating daily SAT based on MODIS LST from NASA's Terra or Aqua by applying different statistical learning methods. Various satellites from earth observation missions, particularly the missions for meteorological satellites, are capable of acquiring thermal infrared observations, but its implications for SAT estimation are significantly ignored. In this study, for the first time, we proposed a merging framework for estimating daily mean SAT by integrating LST datasets from multiple polar-orbiting satellites, including Metop-B from EUMETSAT's Polar System (EPS), SNPP and JPSS-1 from NOAA's Joint Polar Satellites System (JPSS), and Terra and Aqua from NASA's EOS. This study is also the first to explore the estimating of daily SAT based on LST derived from the meteorological satellites in EPS and JPSS. The framework integrates 10 estimation models based on different LST from the five satellites and generates daily merged SAT by averaging the daily SAT estimates from the models. Here we show that the framework significantly improves the spatial coverage of daily SAT estimates for cloud-free areas by an overall increase of 39% with respect to the mean coverage of the LST datasets from the five satellites. Daily coverage of the merged SAT from the framework is nearly all above 75% with an average of 91%. Compared to the SAT estimated from MODIS LST, overall increases in the coverage of daily SAT are 37%-51%. Estimation models in the framework all achieved comparable and satisfactory predicative performances with an average RMSE of 1.7-1.9 K for sample-based cross-validation, and 1.9-2.2 K for site-based cross-validation.

摘要

重建空间连续的地表气温（SAT）对于气候和环境研究具有重要意义。人们已经做出了大量努力，基于极地轨道卫星上的地表温度（LST）来估算逐日 SAT。然而，之前的研究几乎都局限于基于美国宇航局（NASA）Terra 或 Aqua 上的 MODIS LST，通过应用不同的统计学习方法来估算逐日 SAT。地球观测任务中的各种卫星，特别是气象卫星任务中的卫星，都能够获取热红外观测数据，但这些数据在 SAT 估算中的应用却被严重忽视。在本研究中，我们首次提出了一个融合框架，通过整合来自多个极地轨道卫星的 LST 数据集来估算逐日平均 SAT，这些数据集包括来自欧洲气象卫星开发组织（EUMETSAT）极地系统（EPS）的 Metop-B、来自美国国家海洋和大气管理局（NOAA）联合极地卫星系统（JPSS）的 SNPP 和 JPSS-1，以及来自 NASA 的地球观测系统（EOS）的 Terra 和 Aqua。这也是首次探索利用 EPS 和 JPSS 中的气象卫星的 LST 估算逐日 SAT。该框架整合了基于五颗卫星上的 LST 的 10 个估算模型，并通过对模型生成的逐日 SAT 估算值进行平均，生成逐日融合 SAT。研究表明，该框架将五颗卫星的 LST 数据集的平均覆盖率提高了 39%，显著提高了无云区域逐日 SAT 估算值的空间覆盖范围。框架生成的融合 SAT 的逐日覆盖率几乎都在 75%以上，平均覆盖率为 91%。与从 MODIS LST 估算的 SAT 相比，逐日 SAT 的覆盖率总体增加了 37%-51%。框架中的估算模型在基于样本的交叉验证中均取得了相当满意的预测性能，平均均方根误差（RMSE）为 1.7-1.9 K，在基于站点的交叉验证中平均 RMSE 为 1.9-2.2 K。