Citation: Zhang, F.; Zhang, Y.;
Zhang, Z.; Ding, J. Validation and
Improvement of COCTS/HY-1C Sea
Surface Temperature Products.
Sensors 2022, 22, 3726. https://
doi.org/10.3390/s22103726
Academic Editors: Sergio Stefanni,
Jacopo Aguzzi, Simone Marini,
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Received: 1 April 2022
Accepted: 12 May 2022
Published: 13 May 2022
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Article
Validation and Improvement of COCTS/HY-1C Sea Surface
Temperature Products
Feizhou Zhang
1
, Yulin Zhang
1
, Zihan Zhang
1
and Jing Ding
2,
*
1
Beijing Key Lab of Spatial Information Integration and 3S Application, Institute of Remote Sensing and
Geographic Information System, School of Earth and Space Science, Peking University, Beijing 100871, China;
zhangfz@pku.edu.cn (F.Z.); zhangyulin@stu.pku.edu.cn (Y.Z.); zzh_cytus@pku.edu.cn (Z.Z.)
2
Key Laboratory of Space Ocean Remote Sensing and Application, National Satellite Ocean Application
Service, Ministry of Natural Resources, Beijing 100081, China
* Correspondence: dingjing@mail.nsoas.org.cn
Abstract:
In oceanographic study, satellite-based sea surface temperature (SST) retrieval has always
been the focus of researchers. This paper investigates several multi-channel SST retrieval algorithms
for the thermal infrared band, and evaluates the accuracy of the COCTS/HY-1C SST products.
NEAR-GOOS in situ SST data are utilized for validation and improvement, and a three-step match-
ing procedure including geographic location screening, cloud masking, and homogeneity check is
conducted to match in situ SST data with satellite SST data. Two improvement schemes, includ-
ing nonlinear regression and regularization iteration, are proposed to improve the accuracy of the
COCTS/HY-1C SST products and the typical application scenarios and the algorithm characteristics
of these two schemes are discussed. The standard deviation of residual between retrieved SST and
measured SST for these two data improvement algorithms, which are considered as the main indexes
for assessment, result in an improvement of 13.245% and 14.096%, respectively. In addition, the
generalization ability of the SST models under two data improvement methods is quantitatively
compared, and the factors affecting the model accuracy are also carefully evaluated, including the in
situ data acquisition method and measurement time (day/night). Finally, future works about SST
retrieval with COCTS/HY-1C satellite data are summarized.
Keywords:
COCTS/HY-1C; NEAR-GOOS; sea surface temperature; nonlinear regression;
regularization
1. Introduction
The sea surface temperature (SST) reflects the thermophysical characteristics of the
ocean surface, and it is one of the most widely used marine elements for researchers. It is
of great significance in climate prediction, marine biology, marine chemistry, and marine
geology. SST data are usually obtained by survey ship measurement, buoy measurement,
or satellite remote sensing retrieval [
1
–
4
]. Two types of field observation techniques, namely
the survey ship measurement and buoy measurement, can acquire high precision SST data.
However, these two data acquisition techniques are limited by the measuring cost and the
failure to meet the needs of real-time and wide-range monitoring [
5
]. The satellite remote
sensing technology has a higher temporal resolution and spatial resolution, while data
acquisition is more accessible and convenient, which makes up for the shortcomings of the
two in situ measurement technologies [5,6].
At present, two types of satellite remote sensing technologies, namely the passive
microwave remote sensing and infrared (middle infrared/thermal infrared) remote sensing,
are mainly used for SST retrieval [
7
]. The basic principle is to receive the emitted radiation
(microwave radiation/thermal radiation) from the ocean surface, and then calculate the
SST using the Planck equation. In this paper experiment, band 9 (10.300–11.300
µ
m) and
Sensors 2022, 22, 3726. https://doi.org/10.3390/s22103726 https://www.mdpi.com/journal/sensors
