Comparison of Split Window Algorithms for Retrieving Measurements of Sea Surface Temperature from MODIS Data in Near-Land Coastal Waters

Cavalli, Rosa Maria

doi:10.3390/ijgi7010030

Cited by 11 publications

(4 citation statements)

References 53 publications

(122 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, when developing new methods for retrieving SST from satellites in the nearshore, efforts should be focused on correcting the effect of land contamination in the nearshore pixels. Other factors related to coastal waters not investigated here that might impact satellite SST retrievals (e.g., floating kelp and optically-complex coastal waters) are also worthy of further investigation [65,66].…”

Section: Forward Outlookmentioning

confidence: 99%

Evaluating Operational AVHRR Sea Surface Temperature Data at the Coastline Using Benthic Temperature Loggers

et al. 2018

View full text Add to dashboard Cite

Abstract:The nearshore coastal ocean is one of the most dynamic and biologically productive regions on our planet, supporting a wide range of ecosystem services. It is also one of the most vulnerable regions, increasingly exposed to anthropogenic pressure. In the context of climate change, monitoring changes in nearshore coastal waters requires systematic and sustained observations of key essential climate variables (ECV), one of which is sea surface temperature (SST). As temperature influences physical, chemical and biological processes within coastal systems, accurate monitoring is crucial for detecting change. SST is an ECV that can be measured systematically from satellites. Yet, owing to a lack of adequate in situ data, the accuracy and precision of satellite SST at the coastline are not well known. In a prior study, we attempted to address this by taking advantage of in situ SST measurements collected by a group of surfers. Here, we make use of a three year time-series (2014-2017) of in situ water temperature measurements collected using a temperature logger (recording every 30 min) deployed within a kelp forest (∼3 m below chart datum) at a subtidal rocky reef site near Plymouth, UK. We compared the temperature measurements with three other independent in situ SST datasets in the region, from two autonomous buoys located ∼7 km and ∼33 km from the coastline, and from a group of surfers at two beaches near the kelp site. The three datasets showed good agreement, with discrepancies consistent with the spatial separation of the sites. The in situ SST measurements collected from the kelp site and the two autonomous buoys were matched with operational Advanced Very High Resolution Radiometer (AVHRR) EO SST passes, all within 1 h of the in situ data. By extracting data from the closest satellite pixel to the three sites, we observed a significant reduction in the performance of AVHRR at retrieving SST at the coastline, with root mean square differences at the kelp site over twice that observed at the two offshore buoys. Comparing the in situ water temperature data with pixels surrounding the kelp site revealed the performance of the satellite data improves when moving two to three pixels offshore and that this improvement was better when using an SST algorithm that treats each pixel independently in the retrieval process. At the three sites, we related differences between satellite and in situ SST data with a suite of atmospheric variables, collected from a nearby atmospheric observatory, and a high temporal resolution land surface temperature (LST) dataset. We found that differences between satellite and in situ SST at the coastline (kelp site) were well correlated with LST and solar zenith angle; implying contamination of the pixel by land is the principal cause of these larger differences at the coastline, as opposed to issues with atmospheric correction. This contamination could be either from land directly within the pixel, potentially impacted by errors in geo-location, or possibly through thermal adjacenc...

show abstract

Section: Forward Outlookmentioning

confidence: 99%

Evaluating Operational AVHRR Sea Surface Temperature Data at the Coastline Using Benthic Temperature Loggers

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Regarding sea surface salinity products, Vazquez-Cuervo et al [65], for example, analyzed the RSSSMAP (Remote Sensing Systems 70 km Soil Moisture Active/Passive Derived Sea Surface Salinity L3) and JPLSMAP (Jet Propulsion Laboratory Soil Moisture Active/Passive Derived Sea Surface Salinity) products to identify sea surface temperature and sea surface salinity fronts along the California coast. Regarding sea surface temperature products, Cavalli [66,67] and Kartal [68], for example, exploited several products to analyze the prediction capabilities of different models. Regarding primary production products, Tilstone et al [69], for example, analyzed some products of CMEMS.…”

Section: Available Productsmentioning

confidence: 99%

Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review

Cavalli

2024

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Since 1971, remote sensing techniques have been used to map and monitor phenomena and parameters of the coastal zone. However, updated reviews have only considered one phenomenon, parameter, remote data source, platform, or geographic region. No review has offered an updated overview of coastal phenomena and parameters that can be accurately mapped and monitored with remote data. This systematic review was performed to achieve this purpose. A total of 15,141 papers published from January 2021 to June 2023 were identified. The 1475 most cited papers were screened, and 502 eligible papers were included. The Web of Science and Scopus databases were searched using all possible combinations between two groups of keywords: all geographical names in coastal areas and all remote data and platforms. The systematic review demonstrated that, to date, many coastal phenomena (103) and parameters (39) can be mapped and monitored using remote data (e.g., coastline and land use and land cover changes, climate change, and coastal urban sprawl). Moreover, the authors validated 91% of the retrieved parameters, retrieved from remote data 39 parameters that were mapped or monitored 1158 times (88% of the parameters were combined together with other parameters), monitored 75% of the parameters over time, and retrieved 69% of the parameters from several remote data and compared the results with each other and with available products. They obtained 48% of the parameters using different methods, and their results were compared with each other and with available products. They combined 17% of the parameters that were retrieved with GIS and model techniques. In conclusion, the authors addressed the requirements needed to more effectively analyze coastal phenomena and parameters employing integrated approaches: they retrieved the parameters from different remote data, merged different data and parameters, compared different methods, and combined different techniques.

show abstract

“…With regard to the second approach, the results obtained from hyperspectral data were compared with those obtained from other hyperspectral data (e.g., Hyperion images were compared with CHRIS [ 41 ], Hyperspectral Satellite TianGong-1 [ 42 ], and PRISMA [ 43 ] hyperspectral data), from multispectral data (e.g., CASI and MIVIS hyperspectral images were compared with ATM multispectral data [ 44 ], and PRISMA hyperspectral images were compared with Sentinel-2A multispectral data [ 45 ]), and from other data (e.g., AHSI hyperspectral data were compared with the GlobalLand30 land cover data set [ 46 ]; MIVIS hyperspectral image was merged with DEM [ 47 ]). However, there are many sources of error as the capability evaluated from real image is due to both the characteristics of the sensor and each step of image pre-processing and processing (i.e., calibration [ 7 , 48 ]; atmospheric [ 49 , 50 ] and geometric [ 51 ] corrections; dimension reduction [ 30 ]; selected method [ 52 ]; etc.).…”

Section: Introductionmentioning

confidence: 99%

The Weight of Hyperion and PRISMA Hyperspectral Sensor Characteristics on Image Capability to Retrieve Urban Surface Materials in the City of Venice

Cavalli

2023

Sensors

View full text Add to dashboard Cite

Following the success of the first hyperspectral sensor, the evaluation of hyperspectral image capability became a challenge in research, which mainly focused on improving image pre-processing and processing steps to minimize their errors, whereas in this study, the focus was on the weight of hyperspectral sensor characteristics on image capability in order to distinguish this effect from errors caused by image pre-processing and processing steps and improve our knowledge of errors. For these purposes, two satellite hyperspectral sensors with similar spatial and spectral characteristics (Hyperion and PRISMA) were compared with corresponding synthetic images, and the city of Venice was selected as the study area. After creating the synthetic images, the errors in the simulation of Hyperion and PRISMA images were evaluated (1.6 and 1.1%, respectively). The same spectral unmixing procedure was performed using real and synthetic images, and their accuracies were compared. The spectral accuracies in root mean square error were equal to 0.017 and 0.016, respectively. In addition, 72.3 and 77.4% of these values were related to sensor characteristics. The spatial accuracies in the mean absolute error were equal to 3.93 and 3.68, respectively. A total of 55.6 and 59.0% of these values were related to sensor characteristics, and 22.6 and 22.3% were related to co-localization and spatial resampling errors. The difference between the radiometric precision values of the sensors was 6.81 and 5.91% regarding the spectral and spatial accuracies of Hyperion image. In conclusion, the results of this study showed that the combined use of two or more real hyperspectral images with similar characteristics and their synthetic images quantifies the weight of hyperspectral sensor characteristics on their image capability and improves our knowledge regarding processing errors, and thus image capability.

show abstract

Comparison of Split Window Algorithms for Retrieving Measurements of Sea Surface Temperature from MODIS Data in Near-Land Coastal Waters

Cited by 11 publications

References 53 publications

Evaluating Operational AVHRR Sea Surface Temperature Data at the Coastline Using Benthic Temperature Loggers

Evaluating Operational AVHRR Sea Surface Temperature Data at the Coastline Using Benthic Temperature Loggers

Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review

The Weight of Hyperion and PRISMA Hyperspectral Sensor Characteristics on Image Capability to Retrieve Urban Surface Materials in the City of Venice

Contact Info

Product

Resources

About