Chinese ocean color satellite HY-1E detected 2025 California wildfires

The chlorophyll-a (Chl-a) concentration, a critical parameter for characterizing marine primary productivity and ecological health, plays a vital role in providing ecological environment monitoring and climate change assessment while serving as a core retrieval product in ocean color remote sensing. Currently, more than ten ocean color satellites operate globally, including China’s HY-1C, HY-1D and HY-1E satellites. However, significant spatial data gaps exist in Chl-a concentration retrieval from satellites because of cloud cover, sun-glint, and limitation of sensor swath. This study aimed to systematically enhance the spatiotemporal integrity of ocean monitoring data through multisource data merging and reconstruction techniques. We integrated Chl-a concentration datasets from four major sensor types—Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Ocean and Land Color Instrument (OLCI), and Chinese Ocean Color and Temperature Scanner (COCTS)—and quantitatively evaluated their global coverage performance under different payload combinations. The key findings revealed that single-sensor 4-day continuous observation achieved effective coverage levels ranging from only 10.45–26.1%, while multi-sensor merging substantially increased coverage, namely, homogeneous payload merging provided 25.7% coverage for two MODIS satellites, 41.1% coverage for three VIIRS satellites, 24.8% coverage for two OLCI satellites, and 37.1% coverage for three COCTS satellites, with 10-payload merging increasing the coverage rate to 55.4%. Employing the Data Interpolating Empirical Orthogonal Functions (DINEOFS) algorithm, we successfully reconstructed data for China’s ocean color satellites. Validation against VIIRS reconstructions indicated high consistency (a mean relative error of 26% and a linear correlation coefficient of 0.93), whereas self-verification yielded a mean relative error of 27% and a linear correlation coefficient of 0.90. Case studies in Chinese offshore and adjacent waters, waters east of Mindanao Island and north of New Guinea, demonstrated the successful reconstruction of spatiotemporal Chl-a dynamics. The results demonstrated that China’s HY-1C, HY-1D, and HY-1E satellites enable daily global-scale Chl-a reconstruction.

The lunar is a stable radiation source, which can be used as an ideal source for in-orbit calibration of remote sensors and evaluation of detector degradation performance. A passive lunar calibration method is proposed for Chinese ocean color satellite, which reuses field of view of cryogenic-deep-space calibration, periodically achieves monthly lunar calibration tasks. This work enriches ocean color satellite in-orbit calibration methods and improves data accuracy of products. The start and stop angle vector of cryogenic-deep-space, satellite-lunar pointing vector and imaging observation model are established in simulation software. The satellite and payload parameters are used as input conditions to carry out the simulation of the lunar calibration timing. The lunar calibration timing of payload COCTS (Chinese Ocean Color and Temperature Scanner) is simulated 00:00:00~24:00:00UTC on June 28, 2020. The result shows that lunar calibration was carried out for twice. The starting UTC (Universal Time Coordinated) time was 15:16:44 and 16:56:37, respectively. The duration was two seconds. Through analyzing the 0-level products of in-orbit satellite received by the application system, the cryogenic-deep-space data showed abnormal changes at 15:16:45 and 16:56:38 on June 28, 2020, and the DN (Digital Number) values dropped from 300 to 62 and 73, respectively. The in-orbit calibration timing is basically the same as the simulation results, and the numerical anomalies of cryogenic-deep-space data are consistent with the principle design, indicating that the simulation model can be used to predict the in-orbit lunar calibration timing of ocean color satellite. The analysis method can be used for cryogenic-deep-space imaging mission mode and payload design of the follow-up ocean color satellites.

Since the 1980s, a great attention has been paid to the advanced technique remote sensing in China, especially to development of satellite programs for marine environment. On September 7, 1988, China launched her first polar orbit satellite FY-1A for meteorological and oceanographic application (water color and temperature) and second satellite FY-1B two years later. In May 1999, China launched her second generation of environment satellite FY-1C with higher sensitivity, more channels and stable operation. The special ocean color satellite HY-1 has been in the orbit on May 15, 2002, whose main purpose is detection of marine environment of China Sea. HY-1 is a first Chinese ocean color satellite which was launched as a piggyback satellite on FY-1 satellite using Long March rocket. On the satellite there are two sensors, one is the Chinese Ocean Color and Temperature Scanner (COCTS), the other is CCD Coastal Zone Imager (CZI). The technique systems of ocean color remote sensing have been developed by Second Institute of Oceanography (SIO), State Oceanic Administration (SOA), in 1997 and by National Satellite Ocean Application Service (NSOAS) in 2002. Those systems include the functions of data receiving, processing, distribution, calibration, validation and application. SIO has capability to receive and process the FY-1 and AVHRR data since 1989. It is also a SeaWiFS scientific research station authorized by NASA, USA, to freely receive SeaWiFS data Since September 16, 1997. NSOAS has capability to receive and process the data of HY-1, AVHRR, MODIS and Geo satellite. In the recent years, some local algorithms of atmospheric correction and inversion of ocean color are developed for FY-1C , SeaWiFS and HY-1 to improve the accuracy of the measurement from satellites efficiently. The satellite data have being applied in monitoring marine environment, such as the spatial distribution of chlorophyll, primary products, suspended material, transparency and yellow substance, red tide detection and coastal current study.

Validation of remote-sensing reflectance (Rrs) products is necessary for the quantitative application of ocean color satellite data. While validation of Rrs products has been performed in low to moderate turbidity waters, their performance in highly turbid water remains poorly known. Here, we used in situ Rrs data from Hangzhou Bay (HZB), one of the world’s most turbid estuaries, to evaluate agency-distributed Rrs products for multiple ocean color sensors, including the Geostationary Ocean Color Imager (GOCI), Chinese Ocean Color and Temperature Scanner aboard HaiYang-1C (COCTS/HY1C), Ocean and Land Color Instrument aboard Sentinel-3A and Sentinel-3B, respectively (OLCI/S3A and OLCI/S3B), Second-Generation Global Imager aboard Global Change Observation Mission-Climate (SGLI/GCOM-C), and Visible Infrared Imaging Radiometer Suite aboard the Suomi National Polar-orbiting Partnership satellite (VIIRS/SNPP). Results showed that GOCI and SGLI/GCOM-C had almost no effective Rrs products in the HZB. Among the others four sensors (COCTS/HY1C, OLCI/S3A, OLCI/S3B, and VIIRS/SNPP), VIIRS/SNPP obtained the largest correlation coefficient (R) with a value of 0.7, while OLCI/S3A obtained the best mean percentage differences (PD) with a value of −13.30%. The average absolute percentage difference (APD) values of the four remote sensors are close, all around 45%. In situ Rrs data from the AERONET-OC ARIAKE site were also used to evaluate the satellite-derived Rrs products in moderately turbid coastal water for comparison. Compared with the validation results at HZB, the performances of Rrs from GOCI, OLCI/S3A, OLCI/S3B, and VIIRS/SNPP were much better at the ARIAKE site with the smallest R (0.77) and largest APD (35.38%) for GOCI, and the worst PD for these four sensors was only −13.15%, indicating that the satellite-retrieved Rrs exhibited better performance. In contrast, Rrs from COCTS/HY1C and SGLI/GCOM-C at ARIAKE site was still significantly underestimated, and the R values of the two satellites were not greater than 0.7, and the APD values were greater than 50%. Therefore, the performance of satellite Rrs products degrades significantly in highly turbid waters and needs to be improved for further retrieval of ocean color components.

Since China launched first marine satellite HY-1A in May 2002, the second Chinese ocean color satellite HY-1B sponsored by the State Oceanic Administration (SOA) was launched by the Long March rocket on April 11, 2007. There are two sensors in the satellite, one is the Chinese ocean color and temperature scanner (COCTS), and the other is the Coastal Zone Imager (CZI). So far, more than one thousand five hundred orbits data have been received and analysis for application study. In this paper, first the properties and characteristics of HY-1B are briefly introduced with comparing to the SeaWiFS. Second, the data processing technique of COCTS will be discussed in detail, such as satellite cross radiation calibration, atmospheric correction, etc. Third, the remote sensing products of ocean color and temperature are mapped by HY-1B to study its application potentiality. The results show that the HY-1B has its latent capability for the application of marine environment detection. Finally, some suggestion is proposed to modify the next ocean satellite and sensors, such as adding the properties of tilt scanning case, modification of CZI element uniform and future ocean color satellite development in China.

The Chinese Ocean Color and Temperature Scanner (COCTS) on-board the Chinese second ocean color satellite, HY-1B, obtained approximately 6 years of data between 2007 and 2013 in China coastal seas and the adjacent waters. However, its radiometric performance has hardly been analyzed, which confuses its applicability in ocean remote sensing. This study tracked the long-term radiometric responsivity trend of HY-1B COCTS based on a stable marine target. Firstly, we identified a temporally stable maritime site of 12° ~ 15°N and 116°~119°E according to the water and atmospheric optical properties using Aqua MODIS products. Then, the time-series of top-of-atmosphere (TOA) reflectance was obtained for each band of HY-1B COCTS and Aqua MODIS over this site according to the criteria of sun-target-view geometry. Finally, exponential or linear degradation models were built and used to adjust the radiometric levels of HY-1B COCTS. Results indicate that the radiometric performance exhibited continuous degradation for all bands at varying levels between 0.4% and 8.1% yr−1. The worst degradation occurred at 412 nm, with an annual average rate of 8.1%. The degradation at 443 nm reached 5.5% yr−1 following 412 nm. The radiometric performance at 490 nm, 520 nm, and 565 nm was relatively stable with a drift of ~3% yr−1. The 670 nm, 750 nm, and 865 nm bands remain most stable with the degradation of ~1% yr−1. Taking Terra MODIS as a reference, the temporal consistency of HY-1B COCTS was significantly improved for each band after radiometric adjustment. Cloud-free imageries between 2007 and 2013 showed relatively high spatial consistency. The bias of TOA reflectance was ~5% in visible bands and ~10% in near-infrared bands after degradation correction. These improvements confirm the application potentials of HY-1B COCTS in ocean remote sensing.

The SAtellite VAlidation Navy Tool (SAVANT) was developed by the Navy to help facilitate the assessment of the stability and accuracy of ocean color satellites using ground truth (insitu) platform and buoy stations positioned around the globe and support methods for match-up protocols. This automated, continuous monitoring system for satellite ocean color sensors employs a website interface to extract and graph coincident satellite and insitu data in near-real-time. Available satellite sensors include MODerate resolution Imaging Spectrometer (MODIS) onboard the Aqua satellite, Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi National Polar-orbitting Partnership (SNPP) & Joint Polar Satellite Sensor (JPSS), Ocean and Land Colour Instrument (OLCI) onboard the Sentinel 3A and Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean and Meteorological Satellite (COMS). SAVANT houses an extensive match-up data set covering nineteen plus years (2000- 2019) of coincident global satellite and ground truth spectral Normalized Water Leaving Radiance (nLw) data allowing users to evaluate the accuracy of ocean color sensors spectral water leaving radiance at specific ground truth sites that provide continuous data. The tool permits changing different match-up constraints and evaluating the effects on the match-up uncertainty. Results include: a) the effects of spatial selection (using single satellite pixel versus 3x3 and 5x5 boxes, all centered around the insitu location), b) time difference between satellite overpass and ground truth observations, c) and satellite and solar zenith angles. Match-up uncertainty analyses was performed on VIIRS SNPP at the AErosol RObotic NETwork Ocean Color (AeroNET-OC) Wave Current surge Information System (WavCIS) site, maintained by NRL and the Louisiana State University (LSU) in the North Central Gulf of Mexico onboard the Chevron platform CSI-06. The VIIRS SNPP and AeroNET-OC assessment determined optimal satellite ocean color cal/val match-up protocols that reduced uncertainty in the derived satellite products.

The one of the most important technique of satellite ocean color remote sensing is the radiance measurement accuracy because the water leaving radiance is only about 5% to 10% of the total radiance arrived at sensor at the satellite altitude. It is necessary to guarantee the accuracy of water leaving radiance measurement of about 5%(relative error) to meet the reversed accuracy of ocean color factors (such as chlorophyll suspended material and so on) within relative error about 30 in open sea (Case I water). When sensor has been in the orbit it is important to take orbit calibration to make up some deficits of the pre-launch calibration in the laboratory. Two kinds of data could be used for orbit calibration one is in-situ measurement date so-called in-situ field calibration and another is other satellite data with higher radiance measurement accuracy so called crossing-calibration. China has launched the third spaceship SZ-3 in March 2002. The main remote sensing sensor is the Chinese Moderate Imaging Spectroradiometer (CMODIS) which has total 34 channels (30 channels of 2Onm interval in the spectral range of 0.403-1.O43μm and four infrared channels with 2.15-2.25um, 8.4-8.5um, 10.3-11.3um and 11.5-12.5um. Following SZ-3 China had the first ocean color satellite HY-1A in May of the same year whose main sensor is for ocean color measurement, called as Chinese Ocean Color and Temperature Scanner, COCTS, providing 8 visible and near-infrared channels similar to SeaWiFS. In this paper, first, the methodology and procedure of satellite cross-calibration are discussed in detail, with taking an example of CMODIS. Then the results of this orbit cross-calibrated by American ocean sensor, Sea Wide Field-of-View Sensor (SeaWiFS), for CMODIS and COCTS are presented with comparing the normarization from pre-lunched Lab-calibration and in-situ measurement.

The societal benefits of satellite ocean colour include aiding the management of the marine ecosystem, helping understand the role of the ocean ecosystem in climate change, aquaculture, fisheries, coastal zone water quality, and the mapping and monitoring of harmful algal blooms. Ocean colour is also designated as an essential climate variable by the Global Climate Observing System (GCOS). However, in order to have confidence in earth observation data, measurements made at the surface of the Earth, with the intention of providing verification or validation of satellite mounted sensor measurements, should be trustworthy and of the same high quality as those taken with the satellite sensors themselves. In order to be trustworthy, in situ validation measurements should include an unbroken chain of SI traceable calibrations and comparisons and full uncertainty budgets for each of the in situ sensors used. This metrological traceability is beginning to be demanded by the space agencies for satellite validation measurements and, for ocean colour, should follow the guidelines and protocols of the ESA Fiducial Reference Measurements for Satellite Ocean Colour (FRM4SOC) project (www.frm4soc.org). Until now, this has not been the case for most measurements used for validation, including those taken in the Aegean and Eastern Mediterranean. Subsequently, the Hellenic Centre for Marine Research (HCMR), in cooperation with the Laboratory of Optical Metrology (LOM), has started to follow the FRM direction by ensuring that the radiometers of its optical suite underwent SI-traceable absolute radiometric calibration. This included an estimate of the radiometry calibration uncertainty budget and was performed at the marine optical laboratory of the European Commission’s Joint Research Centre prior to their deployment on the recent PERLE-2 oceanographic cruise in the Eastern Mediterranean (Feb-Mar 2019). As well as irradiance and radiance sensors, the HCMR optical suite also houses instruments for measuring inherent optical properties (IOP) of the water column. Therefore, this paper presents the in-water radiometry matchups from PERLE-2 with Sentinel-3 Ocean and Land Colour Instrument (OLCI) measurements, and investigates their validation potential. It also presents the PERLE-2 cruise profile chlorophyll and backscatter measurements that aid this effort through characterizing the light scattering and absorbing constituents that contribute to the signal detected by satellite ocean colour sensors during validation matchups.

After the two operational polar-orbiting ocean color satellites HY-1C and HY-1D launched in 2018 and 2020, respectively, China launched the new-generation ocean color observation satellite (named as HY-1E or HY-3A, hereafter called HY-1E) on 16 Nov. 2023. The second-generation Chinese Ocean Color and Temperature Scanner (COCTS2), one of the major payloads onboard the HY-1E, can daily observe global ocean color and sea surface temperature. In this study, we developed operational ocean color retrieval algorithms for the HY-1E/COCTS2 and preliminarily examined their performances. Firstly, the operational atmospheric correction algorithm was established based on look-up tables generated by vector radiative transfer model for coupled ocean-atmosphere system (PCOART), and cross-calibrated by the SNPP/VIIRS-retrieved ocean color products. Then, we proposed a cross-calibration method for correcting the radiance response difference along the viewing angles, and obtained the viewing correction functions for each channel. The results showed that the determination coefficient of the linear relationship between cross-calibration simulated radiance at the top-of-atmosphere and HY-1E/COCTS2 measured radiance was larger than 0.95, indicating the good linear response of the HY-1E/COCTS2. Based on the established atmospheric correction algorithms, the HY-1E/COCTS2 data were processed to generate the daily global products of remote sensing reflectance (Rrs) and chlorophyll-a concentration (Chl) and were validated by the in-situ data and the synchronous products from Aqua/MODIS and SNPP/VIIRS. The mean absolute percentage errors referring to the in-situ data from AERONET-OC were 14.287% (412 nm), 13.341% (443 nm), 8.0623% (490 nm), 9.1882% (520 nm), 17.836% (565 nm), 58.474% (620 nm), 63.229% (665 nm) and 61.552% (681 nm), and the ocean color products derived by HY-1E/COCTS2 were consistent with the products from Aqua/MODIS and SNPP/VIIRS, indicating the good performance of HY-1E/COCTS2 to monitor global ocean color environment.

Remote sensing reflectance obtained from space-borne ocean color sensors is of great importance to carbon cycle and ocean-atmospheric interactions by providing biogeochemical parameters on the global scale using specific algorithms. Vicarious calibration is necessary for obtaining accurate remote sensing reflectance that meets the application demands of atmospheric correction algorithms. For ocean color sensors, vicarious calibration must be done prior to atmospheric correction. The third Chinese Ocean Color and Temperature Scanner (COCTS) aboard the HY1C satellite was launched on September 7, 2018, and it will provide essential ocean color data that will complement those of existing missions. We used field measurements from the Marine Optical Buoy (MOBY) and aerosol information provided by the MODerate Imaging Spectroradiometer (MODIS) aboard the Terra satellite to calculate vicarious calibration coefficients, and we further evaluated the applicability of the established vicarious calibration approach by cross-calibration using MODIS data on the global scale. Finally, the established vicarious calibration coefficients were used to retrieve the aerosol optical depth and remote sensing reflectance, which were compared to Aerosol Robotic Network-Ocean Color (AERONET-OC) data and MODIS-Terra and Ocean and Land Color Instrument (OLCI)-Sentinel-3A operational products. The results show that the vicarious calibration coefficients are relatively stable and reliable for all bands ranging from visible to near-infrared and can be used to obtain accurate high-quality data.

In-orbit cross-calibration of HY-1A satellite sensor COCTS

The Haiyang-1C (HY-1C) satellite is the first operational ocean color satellite of the Chinese HY-1 series satellites. The Chinese Ocean Color and Temperature Scanner (COCTS) onboard the HY-1C satellite has 10 channels for ocean color and sea surface temperature (SST) observations. Cloud detection is one of the key preprocessing steps of SST retrieval. In this paper, we use deep neural network U -Net for detection of clouds in HY-1C COCTS imagery. The ground truth of dataset using to train the U -Net model is constructed by Bayesian cloud detection method and manual mask. The overall accuracy has achieved 0.96 on the COCTS test dataset.

Coastal processes can change on hourly time scales in response to tides, winds and biological activity, which can influence the color of surface waters. These temporal and spatial ocean color changes require satellite validation for applications using bio-optical products to delineate diurnal processes. The diurnal color change and capability for satellite ocean color response were determined with in situ and satellite observations. Hourly variations in satellite ocean color are dependent on several properties which include: a) sensor characterization b) advection of water masses and c) diurnal response of biological and optical water properties. The in situ diurnal changes in ocean color in a dynamic turbid coastal region in the northern Gulf of Mexico were characterized using above water spectral radiometry from an AErosol RObotic NETwork (AERONET -WavCIS CSI-06) site that provides up to 8-10 observations per day (in 15-30 minute increments). These in situ diurnal changes were used to validate and quantify natural bio-optical fluctuations in satellite ocean color measurements. Satellite capability to detect changes in ocean color was characterized by using overlapping afternoon orbits of the VIIRS–NPP ocean color sensor within 100 minutes. Results show the capability of multiple satellite observations to monitor hourly color changes in dynamic coastal regions that are impacted by tides, re-suspension, and river plume dispersion. Hourly changes in satellite ocean color were validated with in situ observation on multiple occurrences during different times of the afternoon. Also, the spatial variability of VIIRS diurnal changes shows the occurrence and displacement of phytoplankton blooms and decay during the afternoon period. Results suggest that determining the temporal and spatial changes in a color / phytoplankton bloom from the morning to afternoon time period will require additional satellite coverage periods in the coastal zone.

Estimation of spatial distribution of coastal ocean primary production in Hiroshima Bay, Japan, with a geostationary ocean color satellite