气候变化领域集成服务门户(CCPortal): A machine learning approach to estimating the error in satellite sea surface temperature retrievals

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DOI	10.1016/j.rse.2020.112227
	A machine learning approach to estimating the error in satellite sea surface temperature retrievals
	Kumar C.; Podestá G.; Kilpatrick K.; Minnett P.
发表日期	2021
ISSN	00344257
卷号	255
英文摘要	Global, repeated, and accurate measurements of Sea Surface Temperature (SST) are critical for weather and climate projections. While thermometers on buoys measure SST relatively accurately, only sensors aboard satellites give global and repeated SST measurements necessary for many applications, including climate modeling. For satellite-based thermal infrared sensors, an atmospheric correction converts calibrated brightness temperatures measured at orbital height into an SST estimate, but imperfect assumptions in the correction algorithm coupled with variability in atmospheric conditions and viewing geometries can lead to a wide range of errors and uncertainties in the satellite-derived SST retrievals. Estimates of the resulting errors are imperative for satellite-derived SST assimilation into climate models. This paper evaluates the use of machine learning Decision Tree algorithms to predict the central tendency and dispersion of errors in satellite-derived SST retrievals. First, using records from the NASA R2014.1 MODIS Aqua SST Matchup Database, which includes matched-up satellite and in situ SST measurements, a set of seven variables was derived that addresses the assumptions and known issues in the satellite SST retrieval process. Then, both Random Forest and Cubist Decision Trees were used to predict the SST residual (satellite SST minus skin-corrected buoy SST) for each matchup. While both Decision Tree methods performed similarly well, the Cubist model is more easily interpreted and yields better predictions of errors for relatively infrequent conditions. Various characteristics of the groups of matchups identified by Cubist were explored, and uncertainty values for the error estimates were derived for each group. Overall, the Cubist model predicted the skin SST residual with a root-mean-squared-error of 0.380 °C across nighttime cloud-filtered domains, demonstrating that a Cubist model is viable for quantitatively and accurately predicting Single Sensor Error Statistics per pixel as required by the Group for High Resolution SST. In this paper, we present the training and testing of both Decision Tree models. Because of its interpretability, we explore in detail the characteristics of the Cubist-derived groups to gain new geophysical insight into the satellite-derived SST retrieval error across different measurement conditions. © 2020 Elsevier Inc.
英文关键词	Cubist; Decision trees; Machine learning; MODIS; Random forests; Sea surface temperature; Single sensor error statistics
语种	英语
scopus关键词	Atmospheric temperature; Climate models; Decision trees; Error statistics; Errors; Forecasting; Infrared detectors; Machine learning; Mean square error; NASA; Orbits; Random forests; Satellites; Submarine geophysics; Surface properties; Surface waters; Turing machines; Uncertainty analysis; Atmospheric conditions; Atmospheric corrections; Brightness temperatures; Decision-tree algorithm; Machine learning approaches; Root mean squared errors; Sea surface temperature (SST); Thermal infrared sensors; Oceanography; algorithm; atmospheric correction; calibration; climate change; machine learning; MODIS; numerical model; pixel; random walk method; satellite data; sea surface temperature; sensor; Satellites
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178967
作者单位	Princeton University, United States; Rosenstiel School of Marine and Atmospheric Sciences, University of Miami, United States
推荐引用方式 GB/T 7714	Kumar C.,Podestá G.,Kilpatrick K.,et al. A machine learning approach to estimating the error in satellite sea surface temperature retrievals[J],2021,255.
APA	Kumar C.,Podestá G.,Kilpatrick K.,&Minnett P..(2021).A machine learning approach to estimating the error in satellite sea surface temperature retrievals.Remote Sensing of Environment,255.
MLA	Kumar C.,et al."A machine learning approach to estimating the error in satellite sea surface temperature retrievals".Remote Sensing of Environment 255(2021).