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

Abstract

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.

1. Introduction

Earth is defined as a coastal planet [1] because its coastline has an extent of about 1,634,701 km [2]. In other words, if the coastline could be stretched, it would go 402 times around the equator [1]. Moreover, coastal areas are a very valuable resource. In 2016, around 102,108.4 km² of these areas were declared an “exclusive economic zone” [3]. They provide the human population with many benefits (e.g., food, renewable and nonrenewable resources, and services) [4]. The wide range of benefits make them favorite places for permanent living, leisure, recreation, and tourism. Most people are concentrated in coastal cities, and many of them have more than 10 million people [5]. In 2000, Kullenberg [6] highlighted that 27 coastal cities had more than 1 million people, 12 cities had between 1 to 10 million people, 13 had between 10 to 20 million people, and 2 had more than 20 million people. Moreover, these numbers are expected to grow. The Intergovernmental Panel on Climate Change evaluated that 680 million people lived in coastal zones in 2019 and predicted that the number will become more than one billion in 2050 [7].

However, climate change has a great impact on coastal zones [8]. In 2019, Oppenheimer et al. [9] underlined that “coastal ecosystems are already impacted by the combination of sea level rise, other climate-related ocean changes, and adverse effects from human activities on ocean and land (high confidence)”. Moreover, the continuous interaction between humans and the land, sea, rivers, and atmosphere make coastal waters very fragile [10]. Crain et al. [11] classified human threats into four categories: effects of contaminants, eutrophication, habitat loss, and overexploitation of fishery resources.

Since 1971, remote sensing has been used to map and monitor coastal zone phenomena and parameters [12,13]. To further demonstrate how remote sensing is very useful for mapping and monitoring this valuable but vulnerable area, more than 500 reviews on remote sensing application in coastal zones were published from 1971 to June 2023. A total of 35 reviews published between 2021 and June 2023 were identified using the Web of Science (WoS) and Scopus search engines (

Table 1. Reviews on remote sensing for mapping and monitoring coastal phenomena and parameters.

Paper	Publication Year	Publication Title	Number of References Cited in the Review	Citations in WoS 1	Citations in Scopus 1
Adade et al. [14]	2021	Unmanned aerial vehicle (UAV) applications in coastal zone management—A review	41	30	36
Adebisi et al. [12]	2021	Advances in estimating sea level rise: A review of tide gauge, satellite altimetry and spatial data science approaches	126	22	26
Apostolopoulos and Nikolakopoulos [15]	2021	A review and meta-analysis of remote sensing data, GIS methods, materials and indices used for monitoring the coastline evolution over the last twenty years	171	35	42
Ashphaq et al. [16]	2021	Review of near-shore satellite derived bathymetry: Classification and account of five decades of coastal bathymetry research	99	39	46
Bagheri-Gavkosh et al. [17]	2021	Land subsidence: A global challenge	91	66	74
Chaturvedi [18]	2021	Disaster management: Tsunami and remote sensing technology	31	0	0
Datta et al. [19]	2021	Monitoring the spread of water hyacinth (Pontederia crassipes): Challenges and future developments	74	23	33
Gijsman et al. [20]	2021	Nature-based engineering: A review on reducing coastal flood risk with mangroves	215	31	36
Gupana et al. [21]	2021	Remote sensing of sun-induced chlorophyll-a fluorescence in inland and coastal waters: Current state and future prospects	129	23	26
Kieu and Law [22]	2021	Remote sensing of coastal hydro-environment with portable unmanned aerial vehicles (pUAVs) a state-of-the-art review	111	9	10
Murthy et al. [23]	2021	Three decades of Indian remote sensing in coastal research	52	2	2
Parthasarathy and Deka [24]	2021	Remote sensing and GIS application in assessment of coastal vulnerability and shoreline changes: A review	112	-	31
Rossi et al. [25]	2021	Measurement of sea waves	220	15	15
Thamaga et al. [26]	2021	Advances in satellite remote sensing of the wetland ecosystems in Sub-Saharan Africa	152	19	18
Topouzelis et al. [27]	2021	Floating marine litter detection algorithms and techniques using optical remote sensing data: A review	46	33	38
Wen et al. [28]	2021	A review of quantifying pCO2 in inland waters with a global perspective: Challenges and prospects of implementing remote sensing technology	111	4	8
Al-Shehhi and Abdul [29]	2022	Identifying algal bloom ‘hotspots’ in marginal productive seas: A review and geospatial analysis	125	0	2
Asif et al. [30]	2022	Environmental impacts and challenges associated with oil spills on shorelines	111	31	38
Gonçalves et al. [31]	2022	Beach litter survey by drones: Mini-review and discussion of a potential standardization	71	7	15
Cazenave and Moreira [32]	2022	Contemporary sea-level changes from global to local scales: a review	185	11	15
Morgan et al. [33]	2022	Unmanned aerial remote sensing of coastal vegetation: A review	47	13	9
Tran et al. [34]	2022	A review of spectral indices for mangrove remote sensing	282	17	17
Veettil et al. [35]	2022	Coastal and marine plastic litter monitoring using remote sensing: A review	108	13	14
Vigouroux and Destouni [36]	2022	Gap identification in coastal eutrophication research—Scoping review for the Baltic system case	82	5	5
Adjovu et al. [37]	2023	Overview of the application of remote sensing in effective monitoring of water quality parameters	213	9	11
Ankrah et al. [38]	2023	Shoreline change and coastal erosion in West Africa: A systematic review of research progress and policy recommendation	102	6	6
Boukhennaf and Mezouar [39]	2023	Long and short-term evolution of the Algerian coastline using remote sensing and GIS technology	108	0	0
Hauser et al. [40]	2023	Satellite remote sensing of surface winds, waves, and currents: Where are we now?	381	3	4
Hu et al. [41]	2023	Mapping Ulva prolifera green tides from space: A revisit on algorithm design and data products	81	13	14
Kim et al. [42]	2023	Remote sensing of sea surface salinity: Challenges and research directions	143	3	4
Rolim et al. [43]	2023	Remote sensing for mapping algal blooms in freshwater lakes: A review	112	7	7
Schwartz-Belkin and Portman [44]	2023	A review of geospatial technologies for improving Marine spatial planning: Challenges and opportunities	285	4	5
Tsiakos and Chalkias [45]	2023	Use of machine learning and remote sensing techniques for shoreline monitoring: A review of recent literature	111	7	8
Villalobos Perna et al. [46]	2023	Remote sensing and invasive plants in coastal ecosystems: What we know so far and future prospects	95	2	2
Yuan et al. [47]	2023	Marine environmental monitoring with unmanned vehicle platforms: Present applications and future prospects	176	10	11

¹ accessed on 8 January 2024.

).

Most reviews focused on a phenomenon and/or parameter, providing an overview of papers that examined or monitored them (89%). Among these reviews, some selected papers that analyzed only one study area (14%), whereas others selected papers that only employed one type of sensor or methodology (18%) (

Table 2. The analyzed reviews according to the phenomena and/or parameters examined, the study area analyzed, and type of sensor or methodology employed.

One Phenomenon and/or Parameter Examined	One Study Area Analyzed	One Type of Sensor or Methodology Employed	Number of Reviews
Algal blooms [43], bathymetry [16], carbon dioxide [28], chlorophyll-a [21], coastlines [15,24], floating marine litter [27], invasive alien plants [46], mangroves [20], oil spills [30], sea level [12,32], sea surface salinity [42], subsidence [17], surface wave [25], wind and current [40], tsunami [18], Ulva (green algae) [41], water hyacinth [19], water quality [37]	No	No	20
Algal blooms [29], coastlines [38,39], eutrophication research [36], vegetation [26]	Algerian coast [39], Arabian gulf and sea [29], Baltic sea [36], Sea of Oman [29], Sub-Saharan Africa [26], Red Sea [29], West Africa [38]	No	5
Coastlines [45], mangroves [34], marine spatial planning [44], floating marine litter [31,35], vegetation [33]	No	Geospatial technology [44], high spatial resolution images [35], machine learning [45], spectral indices [34], unmanned aerial vehicles [31,33]	6
No	No	Indian remote sensing satellites [23], unmanned aerial vehicles [14,22,47]	4

).

The other authors only considered coastal zone phenomena and parameters that were mapped using Indian remote sensing satellites [23] or unmanned aerial vehicles (UAVs) [14,22,47]. It is interesting to note that the authors who provided a comprehensive overview of the phenomena and parameters mapped using UAVs pointed out their limited use and advocated greater integration between satellite data and data collected by UAVs [14,22,47].

Objectvives

In conclusion, the most recent reviews provide a limited overview of coastal zone phenomena and parameters mapped and monitored using remote data. In other words, these reviews do not provide an exhaustive overview of remote data, methods, and/or approaches that might more effectively map and monitor these phenomena and parameters. As a matter of fact, for more accurate mapping and monitoring, it is important to remember that coastal areas are characterized by small-scale mosaics of different habitats and mainly affected by small-scale natural or man-made phenomena and that coastal waters are characterized by high spatial and temporal variability of biochemical and physical parameters [48].

Therefore, this paper provides an updated systematic review that aims to (a) identify the coastal zone phenomena and parameters that can be mapped and monitored using remote data, (b) examine how authors have addressed the required spatial, temporal, and thematic requirements to more effectively analyze the coastal zone phenomena and parameters, (c) and provide readers with recommendations for meeting them. The systematic review was carried out in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) statement [49]. The methodological approach employed in this systematic literature review is explained in Section 2, whereas the results, discussion, and conclusions are presented in Section 3, Section 4 and Section 5, respectively.

2. Materials and Methods

2.1. Identification Criteria

This systematic literature review aims to provide readers with an updated overview of research that has exploited remote sensing data to map and monitor the phenomena and parameters of coastal zones. Because remote sensing is defined as a technique that retrieves information without physical contact with the target under examination [50], this study considered all kinds of remote sensing data acquired with any kind of platform (from satellite to aircraft and from UAV to fixed platform). Therefore, the search string used to initially identify papers was generated from all possible combinations between two groups of keywords: the first group identified the coastal area from a geographical point of view (i.e., “coastal waters”, “coasts”, “delta”, “estuarine”, “gulf”, and “lagoons”), while the second group included remote data acquired by all types of platforms (i.e., “remote sensing”, “remote sensed”, “satellite”, “drone”, “unmanned aerial vehicle”, “airborne”, and “aircraft”). For this purpose, the WoS and Scopus search engines were used. It is important to note that this systematic review provides an up-to-date overview that does not claim to be exhaustive given around 100,000 papers have studied coastal zones since 1971. Papers that were published from January 2021 to June 2023 were analyzed.

2.2. Screening and Eligible Criteria

As the search engines identified 15,141 papers published in this time (blue rectangle in Figure 1), the other criterion selected to screen the eligible papers was their number of citations.

Because the number of citations increase over time, no single citation threshold was fixed to papers that were published in different years. However, the number of papers that have to be analyzed for each year was fixed to around 600 papers. This large number was chosen to provide a meaningful sample of papers published during the period under review. The green rectangle in Figure 1 shows the citation threshold and last search engine access for each year of publication and the resulting number of papers screened.

Therefore, the abstracts of these articles were analyzed to identify research that exploited remote data to map and/or monitor coastal phenomena and parameters. A total of 502 eligible papers were identified after excluding duplicates (yellow rectangle in Figure 1).

3. Results

3.1. Remote Data

The analysis of 502 eligible papers highlighted that authors had used 1287 remote data and 396 products that were available online to map and monitor coastal phenomena and parameters. Among the remote data, 79%, 7%, 10%, and 4% of remote data were acquired from satellite, airborne, UAV, and fix platforms, respectively. In addition, 10% of the satellite data were characterized by high spatial resolutions (less than 10 m, e.g., WorldView images). Therefore, 29% of all remote data were characterized by spatial resolutions less than 10 m, and only 7% of the remote data were characterized by spatial resolutions greater than 100 m (e.g., MODIS images). Passive sensors acquired 85% of the remote data, and hyperspectral sensors acquired 5% of the passive data. Regarding active satellite data, Sentinel-1 sensors were the first to be employed. Regarding passive satellite data, Sentinel-2 sensors were the first to be employed. Therefore, most authors preferred to utilize sensor data with high temporal and spatial resolution, followed by sensors with high spectral resolution.

Atmospheric Correction

Because atmospheric correction is one of the major challenges in studying coastal waters with remote data [51,52], most authors (76%) paid special attention to atmospheric correction of passive data (e.g., Tavares et al. [52] and Vanhellemont and Ruddick [53] compared the performance of the main atmospheric correction algorithms). Some authors atmospherically corrected remote images that were acquired from different sensors and/or in different times to compare their spectra and/or products (e.g., [54,55]). Others proposed atmospheric correction algorithms (e.g., Luo et al. [56] developed a new algorithm to atmospherically correct the HY-1C/D data and compared the corrected data with the simultaneous Landsat data). Moreover, some authors also corrected the images by sun glint contamination (e.g., [52,57,58]).

3.2. Available Products

In order to explain how the authors exploited the 396 products, a careful analysis was performed. The following is an explanatory overview of them. Regarding chlorophyll-a products, Nadhairi et al. [59], for example, employed the daily mean chlorophyll-a satellite data (spatial resolution of 4 × 4 km) that were provided by the Copernicus Marine Environmental Monitoring Center (CMEMS). Regarding the digital elevation model, Xu et al. [60], for example, compared some products that play a critical role in flood simulation. Regarding dissolved iron products, Zanaty et al. [61], for example, compared land use and land cover maps with some water quality products that were provided by CMEMS to assess the anthropogenic impacts on environmental sustainability. Regarding hydrodynamic products, Brempong et al. [62], for example, utilized data produced by the Laboratory of Geophysical and Oceanographic Spatial Studies of Toulouse to assess the major factors contributing to coastal flooding. Regarding sea level altimetry products, Passaro et al. [63] and Pujol et al. [64], for example, compared some products. 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.

In conclusion, some authors compared available products with each other to minimize errors or with retrieved data to validate them, whereas others utilized available products to provide a complete view of all variables. The overview not only highlighted how the authors of the eligible papers utilized them but also showed that there are many coastal parameters available online, therefore highlighting the increased need for remote data for monitoring coastal areas.

Developed Tools

This increased need was also highlighted by many tools for extracting and analyzing some coastal parameters, such as the Coastal Analyst System from Space Imagery Engine (CASSIE), and (Digital Shoreline Analysis System (DSAS). CASSIE was developed to map and analyze shorelines and is freely available from Google Earth Engine [70], while DSAS was developed by the Woods Hole Coastal and Marine Science Center “to calculate rate-of-change statistics from multiple historical shoreline positions” [71]. These tools, along with others that were developed to atmospherically correct multispectral data (e.g., ACOLITE, POLYMER, and SeaWiFS Data Analysis System), were widely exploited by the authors of the eligible papers (e.g., [53]).

3.3. Coastal Parameters Mapped and Monitored

A study of the eligible papers highlighted that the authors mapped or monitored 39 parameters. Their analysis also showed that some authors mapped only one parameter, whereas others mapped multiple parameters. These parameters are listed in alphabetical order in

Table 3. The parameters mapped or monitored by the authors of eligible papers.

Parameter	Number of Papers that Analyzed the Parameter	Number of Papers that Mapped Only the Parameter	Number of Papers that Mapped the Parameter Together with Other Parameters
Algae and macroalgae	40	13	27
Aquaculture systems	22	3	19
Aquatic vegetation and coral	18	0	18
Bathymetry, seabed, and tidal creeks	84	27	57
Chlorophyll-a	71	12	59
Colored dissolved organic matter	14	0	14
Current data	20	0	20
Depths of Secchi disk and euphotic layer	9	1	8
Diffuse attenuation coefficient at 490 nm	14	0	14
Digital surface model	84	18	66
Dissolved organic carbon	5	0	5
Dissolved iron and dissolved oxygen	4	0	4
Flood extent	10	1	9
Ice	7	1	6
Land surface temperature	11	1	10
Land use and land cover	152	8	144
Leaf area index	5	0	5
Mangroves	35	4	31
Marine litter	14	6	8
Nightlight and nighttime light intensity	5	0	5
Methane and oil	10	4	6
Particulate organic carbon	5	0	5
Photosynthetically active radiation	6	0	6
Phycocyanin	1	0	1
Plumes	9	1	8
Primary production	9	0	9
Salt marshes	9	0	9
Sea level anomaly and sea level rise	46	3	43
Sea surface salinity	17	1	16
Sea surface temperature	59	4	55
Shoreline	113	24	89
Soil salinization and soil moisture	10	1	9
Suspended sediments	30	1	29
Tidal data	28	0	28
Vegetation cover	98	0	98
Vegetation species	19	0	19
Water turbidity	17	0	17
Wave data	9	0	9
Wind data	39	4	35

(first column). The second column shows the number of papers that mapped or monitored the parameter. Among these papers, the number of papers that mapped only this parameter and the number of those that also mapped other parameters are listed in the third and fourth columns, respectively.

These parameters were mapped or monitored 1158 times in the 502 eligible papers: the authors of 138 papers mapped or monitored only one parameter, whereas the authors of the remaining 365 papers mapped or monitored several parameters together. Therefore, the authors of the remaining 365 papers analyzed the 39 parameters 1019 times. In other words, they analyzed an average of about three parameters in each research.

3.3.1. Algae and Macroalgae

The characteristics of the 40 eligible papers whose authors mapped algae or macroalgae are summarized in

Table A1. The eligible papers that mapped and/or monitored algae and macroalgae.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Algae	Algae distribution	Sentinel-2 (10–20–60 m)	No	-	-	[179]
Algae (1)	Algal blooms	Landsat (15–30 m)	No	[72]	-	-
Algae (1)	Cyanobacterial pigment concentrations	HICO™ (Hyperspectral Imager for the Coastal Ocean, ~90 m) (2)	No	-	[180]	-
Algae (1)	Cyanobacterial pigment concentrations	Landsat (15–30 m)	No	-	-	[89]
Algae	Cyanobacterial pigment concentrations	MERIS (300 m)	No	-	-	[89,181]
Algae (1)	Cyanobacterial pigment concentrations	MODIS (0.5–1 km)	No	-	-	[182]
Algae (1)	Cyanobacterial pigment concentrations	Sentinel-2 (10–20–60 m)	No	-	-	[89,183]
Algae	Cyanobacterial pigment concentrations	Sentinel-3 (300 m)	No	-	-	[89,181]
Algae (1)	Coastal aquaculture ponds	Sentinel-2 (10–20–60 m)	No	-	[77,79]	-
Algae (1)	Harmful algae	MODIS (0.5–1 km)	No	[184]	-	[76]
Algae (1)	Harmful algae	Sentinel-3 (300 m)	No	-	[185]
Algae	Red tide bloom	GaoFen-1 WFV (16 m)	No	-	[186]	[187]
Algae	Red tide bloom	HY-1D (50 m)	No	-	[186]	[187]
Algae (1)	Red tide bloom	Landsat (15–30 m)	No	-	[188]	-
Algae	Red tide bloom	MODIS (0.5–1 km)	No	-	[186]	-
Algae	Red tide bloom	Sentinel-2 (10–20–60 m)	No	[57]	[186]	-
Algae	Red tide bloom	Sentinel-3 (300 m)	No	[57]	-	-
Algae (1)	Red tide bloom	TechDemoSat-1 (TDS-1) GNSS-R	No	-	[188]	-
Algae	Sea snots	DESIS (30 m) (2)	No	-	[189]	-
Algae	Sea snots	MODIS (0.5–1 km)	No	-	[189]	-
Algae (1)	Sea snots	Sentinel-2 (10–20–60 m)	No	-	[190]	-
Algae	Sea snots	Sentinel-3 (300 m)	No	-	[189]	-
Algae (1)	Water quality estimation	Landsat (15–30 m)	No	-	[191]	-
Algae (1)	Water quality estimation	Sentinel-2 (10–20–60 m)	No	-	[191]	[192]
Algae (1)	Wetland biodiversity estimation	HSI ZiYuan1-02D (30 m) (2)	No	-	-	[193]
Macroalgae (1)	Green tides	Aerial photos	No	-	-	[194]
Macroalgae	Green tides	GaoFen-1 (2–8 m)	No	-	-	[74,195]
Macroalgae	Green tides	Geostationary Ocean Color Imager -GOCI (500 m)	No	-	-	[196]
Macroalgae	Green tides	Huanjing-1A (30–100 m) (2)	No	-	-	[74,195,197]
Macroalgae	Green tides	Huanjing-1B (150–300 m)	No	-	-	[74,195,197]
Macroalgae	Green tides	Landsat (15–30 m)	No	-	-	[195,197,198]
Macroalgae (1)	Green tides	Landsat (15–30 m)	No	-	[199]	[194,200]
Macroalgae	Green tides	MODIS (0.5–1 km)	No	[41]	-	[74,195,200]
Macroalgae (1)	Green tides	Sentinel-2 (10–20–60 m)	No	-	[199]	[74]
Macroalgae	Green tides	Sentinel-2 (10–20–60 m)	No	-	-	[74,195]
Macroalgae	Macroalgae	Portable photo camera	No	-	-	[201]
Macroalgae	Macroalgae	MODIS (0.5–1 km)	No	[73]	-	-
Macroalgae (1)	Macroalgae	MODIS (0.5–1 km)	No	[202]	[203]	[204]
Macroalgae (1)	Macroalgae	Sentinel-1 (~10 m)	No	-	[203]	-
Macroalgae	Macroalgae	Sentinel-2 (10–20–60 m)	No	-	[205]	-
Macroalgae (1)	Macroalgae	Sentinel-3 (300 m)	No	-	[206]	-
Macroalgae (1)	Macroalgae	UAV	No	-	[207]	-
Macroalgae (1)	Microphytobenthos	Sentinel-2 (10–20–60 m)	No	-	[208]	-
Macroalgae	Phytoplankton blooms	Sentinel-3 (300 m)	No	[73]	-	-
Macroalgae	Phytoplankton blooms	Visible Infrared Imaging Radiometer Suite (VIIRS)	Yes	[73]	-	-
Macroalgae (1)	Seagrass	UAV	No	-	[207]	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters; ⁽²⁾ hyperspectral data.

. The authors exploited 69 remote data and 4 products that were available online. Among the remote data, 65, 1, 2, and 1 were acquired from satellite, aircraft, UAV, and fixed platforms, respectively. Among them, 1 was obtained from an active sensor (Sentinel-1), and 65 were acquired from passive sensors. Among these remote data, 62 were retrieved from multispectral sensors (the first sensor utilized was Sentinel-2, followed by MODIS and Landsat sensors) and 6 were obtained from hyperspectral sensors (the first sensor utilized was Huanjing-1A).

Most of the papers (88%) monitored algae or macroalgae over time (e.g., [72]), about half of the papers (63%) compared different methods in order to minimize errors (e.g., [73]), and 58% retrieved the algae or macroalgae from several remote data (e.g., [74]). Moreover, four studies merged remote sensing techniques with models (e.g., Fernandes-Salvador et al. [75] used satellite data and the results of oceanographic modeling for forecasting toxic harmful algae for the Northeast Atlantic shellfish aquaculture industry) or Geographic information system (GIS) techniques and (e.g., Izadi et al. [76] employed GIS technique).

3.3.2. Aquaculture Systems

The characteristics of the 21 eligible papers whose authors mapped aquaculture systems are summarized in

Table A2. The eligible papers that mapped and/or monitored aquaculture systems.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Aquaculture (1)	Coastal aquaculture ponds	Google Earth images	No	-	[78]	-
Aquaculture	Coastal aquaculture ponds	Landsat (15–30 m)	No	-	[209]	[210]
Aquaculture (1)	Coastal aquaculture ponds	Landsat (15–30 m)	No	-	[78,79]	-
Aquaculture	Coastal aquaculture ponds	Sentinel-1 (~10 m)	No	-	-	[211]
Aquaculture	Coastal aquaculture ponds	Sentinel-2 (10–20–60 m)	No	-	-	[211]
Aquaculture (1)	Coastal aquaculture ponds	Sentinel-2 (10–20–60 m)	No	[212]	[77,78,79]	-
Aquaculture (1)	Coastline change	Landsat (15–30 m)	No	-	[213]	[117,214]
Aquaculture (1)	Habitat	Sentinel-1 (~10 m)	No	[165,215]	-	-
Aquaculture (1)	Habitat	Sentinel-2 (10–20–60 m)	No	[215]	-	-
Aquaculture (1)	Ecosystem services value	Landsat (15–30 m)	No	-	-	[216]
Aquaculture (1)	LU/LC change	Landsat (15–30 m)		-	-	[217]
Aquaculture (1)	LU/LC change	Aerial photos	No		[218]	-
Aquaculture (1)	LU/LC change	GaoFen5-HIS (30 m) (2)	No	-	[219]	-
Aquaculture (1)	LU/LC change	Landsat (15–30 m)	No	-	[218,220]	[217]
Aquaculture (1)	LU/LC change	Sentinel-2 (10–20–60 m)	No	-	[219,220]	-
Aquaculture (1)	LU/LC change	SPOT (~10–20 m)		-	-	[217]
Aquaculture	Marine aquaculture	GaoFen-1 WFV (16 m)	No	-	-	[221]
Aquaculture	Marine aquaculture	Landsat (15–30 m)	No	-	-	[221]
Aquaculture	Marine aquaculture	Sentinel-2 (10–20–60 m)	No	-	-	[221]
Aquaculture	Marine aquaculture	ZY1-02D-HIS (30 m) (2)	No	-	[222]	-
Aquaculture (1)	Seaweed aquaculture	HY-1C (50 m)	No	-	[80]	-
Aquaculture (1)	Seaweed aquaculture	Sentinel-1 (~10 m)	No	-	[223]	-
Aquaculture (1)	Seaweed aquaculture	Sentinel-2 (10–20–60 m)	No	-	[80,223]	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters; ⁽²⁾ hyperspectral data.

. The authors exploited 36 satellite data and did not use available products. Among the remote data, 33 were acquired from passive sensors and 3 were acquired from active sensors (Sentinel-1). Among the passive data, 30 were acquired from multispectral sensors (Sentinel-2 sensors were the first to be employed, followed by Landsat sensors) and 3 were retrieved from hyperspectral sensors (GaoFen5-HIS and ZY1-02D-HIS sensors).

Most of the papers (90%) monitored aquaculture systems over time (e.g., [77]), about half of the papers (48%) retrieved aquaculture systems from several remote data (e.g., Luo et al. [78] retrieved coastal aquaculture ponds from Google Earth, Landsat, and Sentinel-2 images), and 38% compared different methods (e.g., [79]). Moreover, three studies combined remote sensing techniques with models or GIS techniques (e.g., Cheng et al. [80] matched the retrieved maps using the GIS technique).

3.3.3. Aquatic Vegetation and Coral

The characteristics of the 19 eligible papers whose authors mapped the aquatic vegetation and coral are summarized in

Table A3. The eligible papers that mapped and/or monitored aquatic vegetation and coral.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Aquatic vegetation (1)	Aquatic vegetation	HyMap airborne (2)	No	-	[81]	-
Aquatic vegetation (1)	Aquatic vegetation	Sentinel-2 (10–20–60 m)	No	-	[81,160,224]	-
Aquatic vegetation (1)	Water hyacinth	Sentinel-2 (10–20–60 m)	No	-	[160]	-
Aquatic vegetation (1)	Water quality estimation	Sentinel-2 (10–20–60 m)	No	-	[160]	-
Coral (1)	Coral reef	Airborne data (2 m)	No		[225]	-
Coral (1)	Intertidal polychaete reefs	UAV-MSI	No	-	[82]	-
Giant kelp (1)	Bull and giant kelp	Landsat (15–30 m)	No	-	-	[226]
Giant kelp (1)	Giant kelp	Landsat (15–30 m)	No	-	[227]	-
Giant kelp (1)	Giant kelp	Sentinel-2 (10–20–60 m)	No	-	-	[228]
Giant kelp (1)	Giant kelp	UAV	No	-	-	[229]
Seagrass (1)	Habitat	Remotely piloted aircraft (RPAs)	No	-	-	[230]
Seagrass (1)	Macroalgae	UAV	No	-	[207]	-
Seagrass (1)	Seabed classification	Airborne lidar	No	-	[231]	-
Seagrass (1)	Seagrass	Landsat (15–30 m)	No	-	[232,233]	-
Seagrass (1)	Seagrass	Sentinel-2 (10–20–60 m)	No	-	[234]	-
Seagrass (1)	Seagrass	UAV	No	-	[207]	-
Seagrass (1)	Seagrass	WorldView 2–3 (~0.5–4 m)	No	[235]	[236]	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters; ⁽²⁾ hyperspectral data.

. The authors exploited 20 remote data, which were acquired from passive sensors and did not use products. Among the remote data, 11, 4, and 5 were acquired from satellite, aircraft, and UAV platforms, respectively. One was obtained from an active sensor (airborne LIDAR) and 19 data were acquired from passive sensors. Among these remote data, 18 were retrieved from multispectral sensors (Sentinel-2 and UAV sensors were the first to be employed, followed by Landsat sensors) and 1 was obtained from a hyperspectral airborne sensor (HyMap).

Most of the papers (84%) monitored aquatic vegetation or coral over time (e.g., [81]), about half of the papers (58%) compared different methods (e.g., [82]), and 32% retrieved these parameters from several remote data (e.g., Ade et al. [81] retrieved aquatic vegetation from HyMap and Sentinel-2 images). Moreover, one study combined the remote sensing technique with GIS techniques [82].

3.3.4. Bathymetry, Seabed, and Tidal Creeks

The characteristics of the 84 eligible papers whose authors mapped the bathymetry, seabed, and tidal creeks are summarized in

Table A4. The eligible papers that mapped and/or monitored the bathymetry, seabed, and tidal creeks.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Bathymetry (1)	Aquatic vegetation	Sentinel-2 (10–20–60 m)	No	-	[224]	-
Bathymetry (1)	Bathymetry	Airborne lidar	No	[237]	[238,239]	-
Bathymetry	Bathymetry	Airborne lidar	No	[84]	[83,240,241]	[242,243]
Bathymetry	Bathymetry	Aerial photos	No	-	-	[244]
Bathymetry	Bathymetry	ASTER (15 m)	No	-	[245]	-
Bathymetry	Bathymetry	Multispectral camera—UAV	No	-	[246]	-
Bathymetry	Bathymetry	Jilin-1	No	-	[158]	-
Bathymetry	Bathymetry	Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) lidar	No	-	[83,247]	[242,248,249,250,251,252,253]
Bathymetry	Bathymetry	Landsat-based global surface water dataset (GSWD)	Yes	-	[247]	-
Bathymetry	Bathymetry	Landsat (15–30 m)	No	[254]	[83,245,255]	[250]
Bathymetry	Bathymetry	MODIS (0.5–1 km)	Yes	-	-	[252]
Bathymetry	Bathymetry	National Centers for Environmental Prediction (NCEP) datasets	Yes	-	-	[252]
Bathymetry	Bathymetry	Orthophotos	No	[256]	-	-
Bathymetry	Bathymetry	PlanetScope images	No	-	[257]	-
Bathymetry (1)	Bathymetry	Sentinel-2 (10–20–60 m)	No	[237]	-	[258]
Bathymetry	Bathymetry	Sentinel-2 (10–20–60 m)	No	[84,256]	[83,239,245,255,259,260,261,262]	[85,243,248,249,250,251,253]
Bathymetry (1)	Bathymetry	UAV	No	-	-	[263]
Bathymetry	Bathymetry	UAV	No	[256]	[240,264]	-
Bathymetry (1)	Bathymetry	WorldView-2/3 (~0.5–4 m)	No	-	[265]	-
Bathymetry	Bathymetry	WorldView-2/3 (~0.5–4 m)	No	-	[266]	-
Bathymetry	Bathymetry	Zhuhai-1 (10 m) (2)	No	-	[261]	-
Bathymetry (1)	Biosiliceous sedimentation flux	Landsat (15–30 m)	No	-	[267]	-
Bathymetry (1)	Bottom friction coefficients	-	Yes	-	-	[268]
Bathymetry (1)	Coastline change	-	Yes	[150,269]	[270]	[271]
Bathymetry (1)	Coastal structures	-	Yes	-	-	[272]
Bathymetry (1)	Coastal aquaculture ponds	-	Yes	-	[209]	[210]
Bathymetry (1)	Coastal vulnerability assessment	-	Yes	[273]	-
Bathymetry (1)	Coastal vulnerability assessment	Changjiang Estuary Waterway Administration Bureau datasets	Yes	[55]	-	-
Bathymetry (1)	Coastal vulnerability assessment	BathySwath1 ITER System (interferometric sonar)	No	[91]	-	-
Bathymetry (1)	Coastal vulnerability assessment	Vegetation and Environment monitoring on a New Micro-Satellite (VENμS)	No	-	-	[274]
Bathymetry (1)	Coastal vulnerability assessment	General Bathymetric Chart of the Oceans (GEBCO) datasets	Yes	-	[275]	-
Bathymetry (1)	Coral reef	-	Yes	-	[225]	-
Bathymetry (1)	Distribution of heavy metals	MODIS (0.5–1 km)	No	[276]	-	-
Bathymetry (1)	Green tide	-	Yes	-	-	[200]
Bathymetry (1)	Habitat	Airborne lidar	No	-	[277]	-
Bathymetry (1)	Habitat	UAV	No	-	[277]	[278]
Bathymetry (1)	Habitat	UAV	No	-	-	[279]
Bathymetry (1)	Hydrographic structure	-	Yes	[280]	-	-
Bathymetry (1)	Intertidal polychaete reefs	-	Yes	-	[82]	-
Bathymetry (1)	Landfast sea ice	MODIS (0.5–1 km)	Yes	-	-	[281]
Bathymetry (1)	Marine heatwaves	-	Yes	-	[282]	-
Bathymetry (1)	Morphological evolution	-	Yes	[283]	-	-
Bathymetry (1)	Morphological evolution	COSMO-SkyMed	No	-	[284]	-
Bathymetry (1)	Morphological evolution	CSK-SA, UAV	No	-	[284]	-
Bathymetry (1)	Morphological evolution	Landsat (15–30 m)	No	-	[285]	-
Bathymetry (1)	Morphological evolution	Multibeam acquisitions (vessels)	No	-	[285,286]	-
Bathymetry (1)	Morphological evolution	Pleiades tri-stereo images (~0.5–2 m)	No	-	[284]	-
Bathymetry (1)	Morphological evolution	Bathymetry Reson Seabat	No	-	[284]	-
Bathymetry (1)	Morphological evolution	UAV	No	-	[285,287,288]	-
Bathymetry (1)	Morphological evolution	Airborne lidar	No	-	[285]	-
Bathymetry (1)	Primary production	-	Yes	-	[289]	-
Bathymetry (1)	Seabed classification	Airborne lidar	No	-	[231]	-
Bathymetry (1)	SLA	-	Yes	-	[290]	-
Bathymetry (1)	Sea snots	-	Yes	-	[190]	-
Bathymetry (1)	SST	-	Yes	-	[291]	-
Bathymetry (1)	Suspended sediments	Landsat (15–30 m)	No	-	[292]	-
Sandbar (1)	Coastline change	Landsat (15–30 m)	No	[293]	-	-
Sandbar (1)	Coastline change	RapidEye (5 m)	No	[293]	-	-
Sandbar (1)	Coastline change	Planetscope (3 m)	No	[293]	-	-
Sand ridge line (1)	Morphological evolution	Huanjing-1B (150–300 m)	No	-	[294]	-
Sand ridge line (1)	Morphological evolution	Landsat (15–30 m)	No	-	[294]	-
Seabed (1)	Coastal vulnerability assessment	Edgetech 4200 SP (side scan sonar)	No	[91]	-	-
Seabed (1)	Habitat	Airborne lidar	No	-	[277]	-
Seabed (1)	Habitat	UAV	No	-	[277]	[278]
Seabed (1)	Seabed classification	Airborne lidar	No	-	[231]	-
Tidal creeks	Morphological evolution	GaoFen-1 WFV (16 m)	No	-	[295]	-
Tidal creeks	Morphological evolution	Huanjing-1B (150–300 m)	No	-	[295]	-
Tidal creeks	Morphological evolution	Landsat (15–30 m)	No	-	[295]	-
Tidal creeks	Morphological Eevolution	Sentinel-2 (10–20–60 m)	No	-	[295]	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters; ⁽²⁾ hyperspectral data.

. The authors exploited 96 remote data and 29 products that were available online. Among the remote data, 56, 17, 14, and 9 were acquired from satellite, airborne, UAV, and fix platforms, respectively. Among these data, 64 were acquired from passive sensors and 32 were acquired from active sensors (9, 15, and 8 were acquired from fixed positions, airborne LIDAR, and ICESat-2 sensors, respectively). Among the 64 data that were retrieved from passive sensors, 63 were obtained from multispectral sensors (Sentinel-2 sensors were the first to be employed, followed by MODIS and Landsat sensors) and 1 was obtained from a hyperspectral sensor (Zhuhai-1).

About half of the papers (58%) retrieved the bathymetry or seabed from several remote data (e.g., Zhang et al. [83] retrieved bathymetry not only from ICESat-2 and airborne LiDAR data but also from GaoFen-2, LandSat-8, and Sentinel-2 images), and 48% compared different methods to obtain results with the lowest error (e.g., [84]). Moreover, 18 papers merged the remote sensing technique with models or GIS techniques (e.g., Lebrec et al. [85] matched the retrieved maps using the GIS technique).

3.3.5. Chlorophyll-a

The characteristics of the 71 eligible papers whose authors mapped chlorophyll-a (Chl-a), which is the primary pigment of all types of phytoplankton [86], are summarized in

Table A5. The eligible papers that mapped and/or monitored Chl-a.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Chl-a	Atmospheric correction	Sentinel-2 (10–20–60 m)	No	-	-	[52]
Chl-a (1)	Atmospheric correction	Sentinel-3 (300 m)	No	-	-	[53]
Chl-a (1)	Algal blooms	MODIS (0.5–1 km)	No	[72]	-	-
Chl-a (1)	Bathymetry	WorldView-2-3 (~0.5–4 m)	No	-	[265]	-
Chl-a (1)	Biosiliceous sedimentation flux	Landsat (15–30 m)	No	-	[267]	-
Chl-a (1)	Biosiliceous sedimentation flux	MODIS (0.5–1 km)	No	-	[267]	-
Chl-a (1)	Biotoxin risk	-	Yes	-	-	[296]
Chl-a (1)	Coastal aquaculture ponds	Sentinel-2 (10–20–60 m)	No	-	[77,79]	-
Chl-a (1)	Cyanobacterial pigment concentrations	HICO™ (~90 m) (2)	No	-	[180]	-
Chl-a (1)	Cyanobacterial pigment concentrations	Landsat (15–30 m)	No	-	-	[89]
Chl-a (1)	Cyanobacterial pigment concentrations	MERIS (300 m)	No	-	-	[89]
Chl-a (1)	Cyanobacterial pigment concentrations	Sentinel-2 (10–20–60 m)	No	-	-	[89]
Chl-a (1)	Cyanobacterial pigment concentrations	Sentinel-3 (300 m)	No	-	-	[89]
Chl-a (1)	Dissolved organic carbon	MODIS (0.5–1 km)	Yes	-	[297]	-
Chl-a (1)	Effects of COVID-19 lockdown	Sentinel-3 (300 m)	No	-	[169]	[298]
Chl-a	Effects of extreme events	-	Yes	[59]	-	-
Chl-a (1)	Eutrophication	DJI M600Pro-UAV (2)	No	[299]	-	-
Chl-a (1)	Eutrophication	hyperspectral imager Pika L (2)	No	[299]	-	-
Chl-a	Eutrophication	MODIS (0.5–1 km)	Yes	[300]	-	[301]
Chl-a	Eutrophication	Sentinel-2 (10–20–60 m)	No	-	-	[302]
Chl-a (1)	Fishing zones	MODIS (0.5–1 km)	Yes	[303]	-	-
Chl-a (1)	Giant kelp	MODIS (0.5–1 km)	Yes	-	-	[228]
Chl-a (1)	Harmful algal bloom	MODIS (0.5–1 km)	Yes	[304]	[305]	[76]
Chl-a (1)	Harmful algal bloom	Sentinel-2 (10–20–60 m)	No	-	-	[183]
Chl-a (1)	Harmful algal bloom	Sentinel-3 (300 m)	No	-	[185]	-
Chl-a (1)	Harmful algal risk	-	Yes	-	-	[296]
Chl-a (1)	LU/LC change	-	Yes	[61]	-	-
Chl-a (1)	Marine aquaculture	-	Yes	-	-	[75]
Chl-a (1)	Macroalgae	MODIS (0.5–1 km)	No	-	-	[204]
Chl-a (1)	Marine heatwaves	-	Yes	-	[282]	-
Chl-a (1)	Microbenthic invertebrate distribution	-	Yes	-	[306]	-
Chl-a (1)	Oil spill	-	Yes	-	[307,308]	-
Chl-a (1)	Particulate organic carbon	MODIS (0.5–1 km)	Yes	-	[309]	-
Chl-a (1)	Phenology and niche ecology of Harmful species	METEOSAT	Yes	-	[159]
Chl-a (1)	Phycocyanin	HICO™ (~90 m) (2)	No	-	-	[134]
Chl-a (1)	Phycocyanin	PRISMA (30 m) (2)	No	-	-	[134]
Chl-a	Phytoplankton	-	Yes	-	[310]	-
Chl-a (1)	Phytoplankton	-	Yes	-	[311]	[312]
Chl-a	Phytoplankton	CZCS (~1 km)	No	-	[313]	-
Chl-a	Phytoplankton	HICO™ (~90 m) (2)	No	-	-	[87]
Chl-a	Phytoplankton	GER 1500 Portable	No	-	[314]	-
Chl-a	Phytoplankton	MERIS (300 m)	No	-	-	[315]
Chl-a	Phytoplankton	MODIS (0.5–1 km)	No	-	[313,316,317]	-
Chl-a	Phytoplankton	SeaWiFS (1.1–4.5 km)	No	-	[313]	-
Chl-a (1)	Phytoplankton	SeaWiFS (1.1–4.5 km)	Yes	-	[318]	-
Chl-a	Phytoplankton	Sentinel-2 (10–20–60 m)	No	[319]	-	-
Chl-a (1)	Phytoplankton	Sentinel-2 (10–20–60 m)	No	-	[320]	-
Chl-a	Phytoplankton	Sentinel-3 (300 m)	No	-	-	[315]
Chl-a (1)	Phytoplankton	VIIRS	Yes	-	[318]	-
Chl-a (1)	Primary production	-	Yes	-	-	[289]
Chl-a (1)	Primary production	Landsat (15–30 m)	Yes	[139]	-	-
Chl-a (1)	Primary production	MERIS (300 m)	Yes	-	[157]	-
Chl-a (1)	Primary production	MODIS (0.5–1 km)	Yes	[69,138]	[157]	-
Chl-a (1)	Seagrass	MODIS (0.5–1 km)	Yes	-	-	[321]
Chl-a (1)	Water hyacinth	Sentinel-2 (10–20–60 m)	No	-	[160]	-
Chl-a (1)	Water quality estimation	MODIS (0.5–1 km)	Yes	-	[88]	[322]
Chl-a (1)	Water quality estimation	Landsat (15–30 m)	No	-	[86,88,323,324]	[325]
Chl-a (1)	Water quality estimation	Sentinel-2 (10–20–60 m)	No	[326]	[86,88,323,324,327,328]	[168,192,329]
Chl-a (1)	Water quality estimation	Sentinel-3 (300 m)	No	-	[86]	[90,168]
Chl-a (1)	Water quality estimation	UAV	No	[326]	-	-
Chl-a (1)	Water quality estimation	-	Yes	-	[306]	-
Chl-a (1)	Water quality estimation	Sentinel-2 (10–20–60 m)	No	-	[160]	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters; ⁽²⁾ hyperspectral data.

. The authors exploited 76 remote data and 30 available products. All remote data were obtained from passive sensors (of them, 72, 1, 2, and 1 were acquired from satellite, aircraft, platforms, UAV, and fixed platforms, respectively). Among these remote data, 70 were acquired from multispectral sensors (Sentinel-2 sensors were the first to be employed, followed by MODIS, Sentinel-3, Landsat, MERIS, and WorldView sensors) and 6 were acquired from hyperspectral sensors (the HICO sensor was the first to be employed, followed by the PRISMA sensor).

About half of the papers (65%) monitored Chl-a concentrations over time (e.g., [69]), 49% compared different methods to obtain results with the lowest error (e.g., [87]), and 39% retrieved the Chl-a concentrations from several remote data (e.g., Masoud et al. [88] retrieved Chl-a concentrations from Landsat, Sentinel-2, and Sentinel-3 images and compared them with CMEMS products). Moreover, 9 papers merged remote sensing techniques with models or GIS techniques (e.g., Vaičiūtė et al. [89] also employed some data retrieved from the SHYFEM model and Izadi et al. [76] also employed the GIS technique).

3.3.6. Colored Dissolved Organic Matter

The characteristics of the 14 eligible papers whose authors mapped colored dissolved organic matter (CDOM), also called gelbstoffe, gilvin, or yellow matter [48], are summarized in

Table A6. The eligible papers that mapped and/or monitored CDOM.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
CDOM (1)	Coastal aquaculture ponds	Sentinel-2 (10–20–60 m)	No	-	[79]	-
CDOM (1)	Dissolved organic carbon	Landsat (15–30 m)	No	-	-	[103]
CDOM (1)	Dissolved organic carbon	MODIS (0.5–1 km)	Yes	-	[104,297]	-
CDOM (1)	Dissolved organic carbon	Sentinel-2 (10–20–60 m)	No	-	-	[103]
CDOM (1)	Dissolved organic carbon	Sentinel-3 (300 m)	No	-	[330]	-
CDOM (1)	Effects of extreme events	Landsat	No	[105]	-	-
CDOM (1)	Effects of extreme events	Sentinel-2 (10–20–60 m)	No	[105]	-	-
CDOM (1)	Effects of extreme events	Sentinel-3 (300 m)	No	[105]	-	-
CDOM (1)	Marine aquaculture	-	Yes	-	-	[75]
CDOM (1)	Phytoplankton	Sentinel-2 (10–20–60 m)	No	-	-	[331]
CDOM (1)	Water quality estimation	MODIS (0.5–1 km)	Yes	-	[88]	-
CDOM (1)	Water quality estimation	Landsat (15–30 m)	No	-	[86,88]	-
CDOM (1)	Water quality estimation	Sentinel-2 (10–20–60 m)	No	-	[86,88]	-
CDOM (1)	Water quality estimation	Sentinel-3 (300 m)	No	-	[86]	[90]

⁽¹⁾ This parameter was mapped and monitored together with other parameters.

. The authors exploited 26 remote data and 3 products that were available online. All remote data were obtained from multispectral satellite sensors (Sentinel-2 sensors were the first to be employed, followed by Sentinel-3, Landsat, and MODIS sensors).

Most of the papers (93%) monitored CDOM over time (e.g., [90]), 64% compared different methods to obtain results with the lowest error (e.g., [88]), and 57% retrieved CDOM from several remote data (e.g., [88]). Moreover, two papers merged remote sensing techniques with models or GIS techniques [75,86].

3.3.7. Current Data

The characteristics of the 20 eligible papers whose authors analyzed current data are summarized in

Table A7. The eligible papers that mapped and/or monitored the current data.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Current (1)	Biotoxin risk	-	Yes	-	-	[296]
Current (1)	Coastline change	-	Yes	-	-	[332]
Current (1)	Coastal vulnerability assessment	-	No	-	[333]	-
Current (1)	Coastal vulnerability assessment	-	Yes	[91]	-	-
Current (1)	Effects of COVID-19 lockdown	-	Yes	-	[169]	-
Current (1)	Green tide	-	Yes	-	-	[200]
Current (1)	Distribution of heavy metals	-	Yes	[276]	-	-
Current (1)	Harmful algal bloom	-	Yes	-	-	[296]
Current (1)	Marine aquaculture	-	Yes	-	-	[75]
Current (1)	Oil spill	-	Yes	-	[334,335]	[92]
Current (1)	Phytoplankton	-	Yes	-	[311]	-
Current (1)	Sea level anomaly	OMNI buoys	No	-	[290]	-
Current (1)	Sea snots	-	Yes	-	[190]	-
Current (1)	Suspended sediments	-	Yes	-	[336]	[337]
Current (1)	Velocity products	Sentinel-1 (~10 m)	No		[338]	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters.

. The authors exploited 2 remote data and 18 products that were available online. The remote data were obtained from active sensors: one was acquired from a satellite (Sentinel-1) and one was obtained from a fixed platform (OMNI buoys).

Most of the papers (89%) monitored current over time (e.g., [91]), and 10 papers merged remote sensing techniques with models (e.g., [92]) or GIS techniques (e.g., [91]).

3.3.8. Depths of Secchi Disk and Euphotic Layer

The characteristics of the nine eligible papers whose authors mapped depths of Secchi disk (Zsd) and euphotic layer (Zeu) [48] are summarized in

Table A8. The eligible papers that mapped and/or monitored Zsd and Zeu.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Zsd (1)	Harmful algal bloom	MODIS (0.5–1 km)	No	-	-	[76]
Zsd (1)	Seagrass	Sentinel-2 (10–20–60 m)	No	-	[234]	-
Zsd	Water quality estimation	GOCI	No	-	[339]	-
Zsd (1)	Water quality estimation	Sentinel-2 (10–20–60 m)	No	-	-	[94,192]
Zsd	Zsd	Landsat (15–30 m)	No	-	-	[95]
Zsd	Zsd	MERIS (300 m)	No	-	[93]	-
Zsd	Zsd	MODIS (0.5–1 km)	No	-	[93]	-
Zeu (1)	Phytoplankton crops and taxonomic composition	SeaWiFS (1.1–4.5 km)	Yes	-	[96]	-
Zeu (1)	Primary production	MERIS (300 m)	Yes	-	[157]	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters.

. The authors exploited 11 remote data and 2 products that were available online. All remote data were acquired from multispectral satellite sensors (Sentinel-2 sensors were the first to be employed, followed by MERIS and MODIS sensors).

All papers monitored these parameters over time (e.g., [93]), about half of the papers (67%) compared different methods or products in order to minimize errors (e.g., [94]), and 56% retrieved depths of Secchi disk and euphotic layer from several remote data (e.g., Yin et al. [95] retrieved Zeu from Landsat and Sentinel-3 data). Moreover, two papers merged remote sensing techniques with models or GIS techniques [76,96].

3.3.9. Diffuse Attenuation Coefficient at 490 nm

The characteristics of the 14 eligible papers whose authors mapped diffuse attenuation coefficient at 490 nm (Kd 490) [97] are summarized in

Table A9. The eligible papers that mapped and/or monitored Kd (490).

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Kd (490) (1)	Bathymetry	Sentinel-2 (10–20–60 m)	No	[237]	-	-
Kd (490) (1)	Biosiliceous sedimentation flux	Landsat (15–30 m)	No	-	[267]	-
Kd (490) (1)	Biosiliceous sedimentation flux	MODIS (0.5–1 km)	No	-	[267]	-
Kd (490) (1)	Effects of COVID-19 lockdown	Sentinel-3 (300 m)	No	-	-	[298]
Kd (490) (1)	Giant kelp	MODIS (0.5–1 km)	Yes	-	-	[228]
Kd (490) (1)	Harmful algal blooms	MODIS (0.5–1 km)	No	-	-	[76]
Kd (490) (1)	Particulate organic carbon	-	Yes	[98]	-	-
Kd (490) (1)	Phytoplankton	-	Yes	-	-	[312]
Kd (490) (1)	Harmful algal bloom	MODIS (0.5–1 km)	No	-	-	[76]
Kd (490) (1)	Seagrass	Sentinel-2 (10–20–60 m)	No	-	[234]	-
Kd (490) (1)	Water quality estimation	GOCI	No	-	[339]
Kd (490) (1)	Water quality estimation	Sentinel-2 (10–20–60 m)	No	-	-	[94,192]
Kd (490) (1)	Zsd	Landsat (15–30 m)	No	-	-	[95]

⁽¹⁾ This parameter was mapped and monitored together with other parameters.

. The authors exploited 13 remote data and 4 products that were available online. All remote data were acquired from multispectral satellite sensors. MODIS images were the first to be employed, followed by Sentinel-2 and then Sentinel-3 and Landsat data.

Most of these papers (93%) monitored this parameter over time (e.g., [95]), about half of the papers (63%) compared different methods or products in order to minimize errors (e.g., [98]), and 57% retrieved Kd (490) from several remote data (e.g., Joshi et al. [98] retrieved Kd (490) data from MODIS and SeaWiFS images). Moreover, three papers combined remote sensing techniques with models or GIS techniques (e.g., [76]).

3.3.10. Digital Surface Model

The characteristics of the 84 eligible papers whose authors mapped the digital surface model (DSM) are summarized in

Table A10. The eligible papers that mapped and/or monitored DSM.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
DSM (1)	Avulsion sites	TanDEM-X (12 m)	No	-	[340]	-
DSM (1)	Coastal aquaculture ponds	-	Yes	[212]	-	[210]
DSM (1)	Coastal forested wetland	UAV	No	-	[341]	-
DSM (1)	Coastline change	-	Yes	[150,269,342,343]	[344,345,346]	[347]
DSM (1)	Coastline change	UAV	No		[348]	-
DSM (1)	Coastal structures	-	Yes	-	-	[272]
DSM	Coastal structures	Terrestrial Laser Scanner	No	-	[349]	-
DSM	Coastal structures	UAV	No	-	[349]	[350]
DSM (1)	Coastal vulnerability assessment	-	Yes	[116]	-	[166,351]
DSM (1)	Coastal vulnerability assessment	ASTER	Yes	-	-	[102]
DSM (1)	Coastal vulnerability assessment	Pleiades (0.5–2 m)	No	-	-	[352]
DSM (1)	Coastal vulnerability assessment	SRTM DEM, USGS (30 m)	Yes	-	[275]	-
DSM (1)	Coastal vulnerability assessment	VENμS	No	-	-	[274]
DSM (1)	Coastal wetland classification	Airborne lidar	No	-	[353]	-
DSM (1)	Flood extent	-	Yes	-	-	[109]
DSM (1)	Flood extent	Pleiades stereo image (0.5–2 m)	No	[62]	[354]	-
DSM	Flood risk	-	Yes	-	-	[60]
DSM (1)	Habitat	-	Yes	-	-	[355]
DSM (1)	Habitat	Airborne lidar	No	-	[277]	-
DSM (1)	Habitat	NASADEM	Yes	[215]	-	-
DSM (1)	Habitat	Sentinel-1 (~10 m)	No	[165]	-	-
DSM (1)	Habitat	UAV	No	-	[277,356,357,358]	[279,359,360]
DSM (1)	Habitat	UAV-LiDAR	No	-	[142,361]
DSM (1)	Intertidal polychaete reefs	DJI Phantom 4 Multispectral UAV	No	-	[82]	-
DSM (1)	Invasive alien species	UAV	No	[161]	-	-
DSM (1)	Litter	-	Yes	-		[362]
DSM (1)	LU/LC change	ALOS PALSAR (12.5 m)	Yes	-	[363]	-
DSM (1)	LU/LC change	ARCTIC DEM	Yes	[364]
DSM (1)	Mangrove ecosystems	-	Yes	-	[365,366]	[367,368]
DSM (1)	Mangrove ecosystems	NASA Goddard’s Lidar, Hyperspectral, and Thermal (G-LiHT) airborne image	No	-	-	[162]
DSM (1)	Mangrove ecosystems	SAR	No	-	[369]	-
DSM (1)	Marine litter	-	Yes	-	-	[128]
DSM (1)	Marine litter	UAV	No	[370]	-	-
DSM (1)	Microbenthic invertebrate distribution	-	Yes	-	[306]	-
DSM (1)	Morphological evolution	Airborne lidar	No	-	[285,371]	-
DSM (1)	Morphological evolution	HJ-1 CCD (30 m)	No	-	[294]	-
DSM (1)	Morphological evolution	Pleiades (0.5–2 m)	No	-	[284,286,371]	-
DSM (1)	Morphological evolution	UAV	No	-	[101,285,288]	[372]
DSM	Morphological structures	Airborne lidar	No	-	-	[100]
DSM	Morphological structures	Aerial photos	No	-	-	[100]
DSM	Morphological structures	Lidar	No	-	-	[373]
DSM (1)	Morphological structures	UAV	No	-	[120]	[263,374]
DSM	Subsidence	Sentinel-1 (~10 m)	No	-	[375,376,377,378,379]	[99]
DSM	Landslides	Sentinel-1 (~10 m)	No	-	[336]	[380]
DSM (1)	Sea level rise	Airborne lidar	No	-	[381]	-
DSM (1)	Suspended sediments	-	Yes	-	[336]	-
DSM (1)	Water quality estimation	-	Yes	-	[306]	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters.

. The authors analyzed 60 remote data and 35 products that were available online. Among the remote data, 19, 13, 25, and 3 were acquired from satellite, airborne, UAV, and fixed platforms, respectively. Among these data, 50 were acquired from active sensors (airborne LIDAR sensors were the first to be employed, followed by Sentinel-1 sensors), whereas 10 were obtained from passive sensors (PLEIADES tri-stereo images were the first to be employed).

About half of the papers (58%) monitored DSM over time (e.g., [99]), 56% acquired data from several platforms (e.g., Grottoli et al. [100] retrieved DSM from fixed position, UAV, and airborne platforms), and 51% compared different methods to minimize errors (e.g., [101]) and different data and products (e.g., [99]). Moreover, 15 papers merged remote sensing techniques with models (e.g., [60]) or GIS techniques (e.g., [102]).

3.3.11. Dissolved Organic Carbon

The characteristics of the five eligible papers whose authors mapped dissolved organic carbon (DOC) [48] are summarized in

Table A11. The eligible papers that mapped and/or monitored DOC.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
DOC (1)	Dissolved organic carbon	Landsat (15–30 m)	No	-	-	[103]
DOC (1)	Dissolved organic carbon	MODIS (0.5–1 km)	No	-	[104,297]	-
DOC (1)	Dissolved organic carbon	Sentinel-2 (10–20–60 m)	No	-	-	[103]
DOC (1)	Dissolved organic carbon	Sentinel-3 (300 m)	No	-	[330]	-
DOC (1)	Effects of extreme events	Landsat (15–30 m)	No	[105]	-	-
DOC (1)	Effects of extreme events	Sentinel-2 (10–20–60 m)	No	[105]	-	-
DOC (1)	Effects of extreme events	Sentinel-3 (300 m)	No	[105]	-	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters.

. The authors exploited eight multispectral satellite data and did not use available products. The sensors utilized were MODIS, Landsat, Sentinel-2, and Sentinel-3.

Most of these papers (80%) monitored DOC over time (e.g., [103]), whereas 40% of the papers compared different methods to minimize errors (e.g., [104]) and exploited data that were acquired from several platforms (e.g., Liu et al. [105] retrieved DOC from Landsat, Sentinel-2, and Sentinel-3 images).

3.3.12. Dissolved Iron and Dissolved Oxygen

The characteristics of the four eligible papers whose authors mapped dissolved iron and dissolved oxygen (DO) [48] are summarized in

Table A12. The eligible papers that mapped and/or monitored dissolved iron and DO.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Dissolved Iron (1)	LU/LC change	-	Yes	[61]	-	-
DO (1)	Fishing zones	-	Yes	[382]	-	-
DO (1)	LU/LC change	-	Yes	[61]	-	-
DO (1)	Water quality estimation	Sentinel-2 (10–20–60 m)	No	-	[328]	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters.

. The analysis of the papers highlighted that they exploited three available products and one remote image (Sentinel-2 data [61]). All authors monitored dissolved iron and oxygen over time [61].

3.3.13. Flood Extent

The characteristics of the 10 eligible papers whose authors mapped flood extent are summarized in

Table A13. The eligible papers that mapped and/or monitored the flood extent.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Flood	Effects of extreme events	Sentinel-1 (~10 m)	No	-	[383]	-
Flood (1)	Flood extent	Airborne lidar	Yes	-	-	[109]
Flood (1)	Flood extent	Landsat (15–30 m)	No	[107]	[354]	-
Flood	Flood extent	Sentinel-1 (~10 m)	No	-	[106]	-
Flood (1)	Flood extent	Sentinel-1 (~10 m)	No	[107]	[120]	-
Flood (1)	Flood extent	Sentinel-2 (10–20–60 m)	No	[107]	-	-
Flood (1)	Flood extent	Pleiades stereo image (0.5–2 m)	No	[62]	-	-
Flood (1)	LU/LC change	ASAR	Yes	-	[108]	-
Flood (1)	LU/LC change	MODIS (0.5–1 km)	No	-	[108]	-
Flood (1)	LU/LC change	TerraSAR-X	Yes	-	[108]	-
Flood (1)	Sea level rise	-	Yes	[384]	-	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters.

. The authors exploited nine remote data and five available products. The remote data were obtained from satellite sensors: four were acquired from active sensors (Sentinel-1), and five were acquired from passive sensors (Landsat sensors were the first to be employed).

Most of these papers (90%) monitored flood extent over time (e.g., [106]), about half of the papers (50%) compared different methods to obtain results with the lowest error (e.g., [107]), and 50% retrieved this parameter from several remote data (e.g., Vu et al. [108] retrieved the flood extent from ASAR, MODIS, and TerraSAR-X). Moreover, Munoz et al. [109] utilized the retrieved maps to calibrate hydrodynamic models, and Nguyen et al. [110] computed flood risk combining hazard, exposure, and vulnerability using hydrodynamic modeling.

3.3.14. Ice

The characteristics of the seven eligible papers whose authors mapped ice extent are summarized in

Table A14. The eligible papers that mapped and/or monitored the presence of ice.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Ice (1)	Blue ice	MODIS (0.5–1 km)	No	-	[385]	-
Ice (1)	Blue ice	Sentinel-2 (10–20–60 m)	No	-	[385]	-
Ice (1)	Landfast sea ice	MODIS (0.5–1 km)	Yes	-	-	[281]
Sea Ice (1)	Sea ice	-	Yes	-	[386]	-
Sea Ice (1)	Sea ice	-	Yes	-	-	[111]
Sea Ice	Sea ice	Coastal global navigation satellite system reflectometry (GNSS-R) fixed station	No	-	[387]	-
Sea Ice (1)	Sea level anomaly	-	Yes	[64]	-	[63]

⁽¹⁾ This parameter was mapped and monitored together with other parameters.

. The authors utilized three remote data and five products that were available online. Among the remote data, two were acquired from passive satellite sensors (MODIS and Sentinel-2 sensors) and one was acquired from fixed platforms (coastal global navigation satellite system reflectometry).

Most of these papers (86%) monitored this parameter over time (e.g., [111]), whereas one paper combined the retrieved maps with models [63].

3.3.15. Land Surface Temperature

The characteristics of the 11 eligible papers whose authors mapped land surface temperature (LST) are summarized in

Table A15. The eligible papers that mapped and/or monitored LST.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
LST (1)	Coastline change	Landsat (15–30 m)	No	[163]	-	-
LST (1)	Coastal salt marshes	MODIS (0.5–1 km)	Yes	[141]	-	[388]
LST	Habitat	-	Yes	-	-	[178]
LST (1)	LU/LC change	Landsat (15–30 m)	No	[389]	-	[390]
LST (1)	LU/LC change	MODIS (0.5–1 km)	No	[391]	-	-
LST (1)	LST	Landsat (15–30 m)	No	-	[112]	-
LST (1)	LST	MODIS (0.5–1 km)	No	-	[112]	-
LST (1)	Urban sprawl	Landsat (15–30 m)	No	-	-	[392]
LST (1)	Urban sprawl	MODIS (0.5–1 km)	No	[113]	-	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters.

. The authors exploited 13 remote data and 2 available products. All remote data were acquired from multispectral satellite sensors (Landsat and MODIS sensors).

Most of these papers (91%) monitored LST over time (e.g., [112]), and 45% of the papers retrieved this parameter from several remote data (e.g., [112]). Moreover, six papers combined remote data with models (e.g., [113]).

3.3.16. Land Use and Land Cover

The characteristics of the 152 eligible papers whose authors mapped land use and land cover (LU/LC) are summarized in

Table A16. The eligible papers that mapped and/or monitored LU/LC.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
LU/LC (1)	Aboveground biomass	Landsat (15–30 m)	No	-	-	[393]
LU/LC (1)	Aboveground biomass	Sentinel-2 (10–20–60 m)	No	-	[140]	-
LU/LC (1)	Aboveground biomass	UAV	No	-	-	[393]
LU/LC (1)	Agricultural non-point source pollution	Sentinel-2 (10–20–60 m)	No	-	[394]	-
LU/LC (1)	Aquatic vegetation	HyMap airborne (2)	No	-	[81]	-
LU/LC (1)	Aquatic vegetation	Sentinel-2 (10–20–60 m)	No	-	[81,224]	-
LU/LC (1)	Blue ice	MODIS (0.5–1 km)	No	-	[385]	-
LU/LC (1)	Blue ice	Sentinel-2 (10–20–60 m)	No	-	[385]	-
LU/LC	Carbon storage change	Landsat (15–30 m)	No	-	[395]	-
LU/LC (1)	Climate change	-	Yes	-	-	[396]
LU/LC (1)	Climate change	Google Earth image	No	-	-	[147]
LU/LC (1)	Climate change	Landsat (15–30 m)	Yes	-	[177]	[397]
LU/LC (1)	Coastal aquaculture ponds	-	Yes	[212]	-	-
LU/LC (1)	Coastal aquaculture ponds	Landsat (15–30 m)	Yes	-	[209]	[210]
LU/LC (1)	Coastline change	Google Earth image	No	-	-	[398]
LU/LC (1)	Coastline change	Landsat (15–30 m)	No	[163,399,400,401,402]	[213,403,404,405]	[117,171,214]
LU/LC (1)	Coastline change	Sentinel-2 (10–20–60 m)	No	[399,401,406]	[346,407,408,409]	[171]
LU/LC (1)	Coastline change	RapidEye (5 m)	No	-	[410]	-
LU/LC (1)	Coastline change	Planetscope (3 m)	No	-	[410]	-
LU/LC (1)	Coastal vulnerability assessment	-	Yes	-	-	[145]
LU/LC (1)	Coastal vulnerability assessment	Google Earth images	No	-	-	[351]
LU/LC (1)	Coastal vulnerability assessment	Landsat (15–30 m)	No	[55,411]	[275]	[102,166,412,413]
LU/LC (1)	Coastal vulnerability assessment	Sentinel-2 (10–20–60 m)	No	-	-	[166]
LU/LC (1)	Coastal vulnerability assessment	Video camera systems	No	[273]	-	-
LU/LC (1)	Coastal wetland classification	Sentinel-2 (10–20–60 m)	No	-	[353]	-
LU/LC (1)	Deltaic estuarine transformations	Landsat (15–30 m)	No	[116]	-	-
LU/LC (1)	Flood extent	-	Yes	[107]	-	-
LU/LC (1)	Flood extent	Google Earth image	No	-	[120]	-
LU/LC (1)	Flood extent	Landsat (15–30 m)	No	-	[120]	[109]
LU/LC	Flood risk	Sentinel-2 (10–20–60 m)	No	-	-	[110]
LU/LC	Flood risk	SPOT (~10–20 m)	No	-	-	[110]
LU/LC (1)	Giant kelp	Landsat (15–30 m)	No		[227]
LU/LC (1)	Ecosystem services value	Landsat (15–30 m)	No	-	-	[216]
LU/LC (1)	Effects of extreme events	-	Yes	-	[383]	-
LU/LC (1)	Effects of extreme events	Sentinel-2 (10–20–60 m)	No	-	-	[414]
LU/LC	Habitat	GaoFen2 (0.8–3.2 m)	No	-	-	[415]
LU/LC (1)	Habitat	GaoFen3-SAR (4.5–5 m)	No	-	-	[416]
LU/LC	Habitat	GaoFen5-HIS (30 m) (2)	No	-	-	[417,418]
LU/LC (1)	Habitat	HSI ZiYuan1-02D (30 m) (2)	No	-	[419]	-
LU/LC (1)	Habitat	Landsat (15–30 m)	No	[165]	[119,420,421,422]	[423,424]
LU/LC	Habitat	Landsat (15–30 m)	No	-	[119,422]	[415]
LU/LC	Habitat	Landsat (15–30 m)	Yes	-	-	[425]
LU/LC (1)	Habitat	Remotely piloted aircraft (RPAs)	No	-	-	[230]
LU/LC (1)	Habitat	Sentinel-1 (~10 m)	No	[165,215]	-	-
LU/LC	Habitat	Sentinel-2 (10–20–60 m)	No	-	-	[418,426]
LU/LC (1)	Habitat	Sentinel-2 (10–20–60 m)	No	[215]	[420]	[427,428]
LU/LC (1)	Habitat	UAV-Lidar	No	-	[429]
LU/LC (1)	Habitat	UAV	No	-	[277,356,429]	[360]
LU/LC	Habitat	WorldView-2 (~0.5–2 m)	No	-	[430]	-
LU/LC (1)	Invasive alien species	Landsat (15–30 m)	No	-	[431,432]	-
LU/LC	Invasive alien species	UAV	No	-	[431]	-
LU/LC (1)	LST	Landsat (15–30 m)	No	-	[112]	-
LU/LC (1)	LST	Sentinel-2 (10–20–60 m)	No	-	[112]	-
LU/LC (1)	LU/LC change	-	Yes	-	-	[433,434]
LU/LC (1)	LU/LC change	Aerial photos	No	[364]	[435,436]	-
LU/LC (1)	LU/LC change	GaoFen-5-HIS (30 m) (2)	No	-	[219]	-
LU/LC (1)	LU/LC change	Google Earth Image	No	-	-	[437]
LU/LC (1)	LU/LC change	Landsat (15–30 m)	No	[61,129,389,391,438,439,440]	[114,218,220,358,363,441,442]	[217,390,437,443,444]
LU/LC	LU/LC change	Landsat (15–30 m)	No	-	[445]	-
LU/LC (1)	LU/LC change	MODIS (0.5–1 km)	No	-	[108]	-
LU/LC (1)	LU/LC change	Quickbird (0.6–2.4 m)		-	-	[217]
LU/LC (1)	LU/LC change	Sentinel-2 (10–20–60 m)	No	[440]	[114,219,220,441]
LU/LC (1)	LU/LC change	SPOT (~10–20 m)		-	-	[217]
LU/LC (1)	LU/LC change	Pleiades (0.5–2 m)	No	[364]	-	-
LU/LC (1)	Macroalgae	MODIS (0.5–1 km)	No	-	[203]	-
LU/LC (1)	Macroalgae	Sentinel-1 (~10 m)	No	-	[203]	-
LU/LC (1)	Macroalgae	UAV	No	-	[207]	-
LU/LC (1)	Mangrove ecosystems	-	Yes	-	-	[446]
LU/LC (1)	Mangrove ecosystems	Corona	No	[121]	-	-
LU/LC (1)	Mangrove ecosystems	Google Earth images		[121]	-	-
LU/LC (1)	Mangrove ecosystems	Landsat (15–30 m)	No	[121,124]	[369,447,448,449]	[162,450,451]
LU/LC (1)	Mangrove ecosystems	Sentinel-1 (~10 m)	No	-	[452]	-
LU/LC (1)	Mangrove ecosystems	Sentinel-2 (10–20–60 m)	No	-	[369,447,452,453]	[450,454]
LU/LC (1)	Mangrove ecosystems	Pléiades-1 (0.5–2 m)	No	-	[369]	-
LU/LC (1)	Mangrove ecosystems	UAV	No	-	[369]	-
LU/LC (1)	Microbenthic invertebrate distribution	Landsat (15–30 m)	No	-	[306]	-
LU/LC (1)	Microphytobenthos	Sentinel-2 (10–20–60 m)	No	-	[208]	-
LU/LC (1)	Morphological evolution	HJ-1 CCD (30 m)	No	-	[294]	-
LU/LC (1)	Morphological evolution	Pléiades (0.5–2 m)	No	-	[371]	-
LU/LC (1)	Oil spill	-	Yes	[308]	-	-
LU/LC (1)	Plastic litter	-	Yes	-	-	[127]
LU/LC (1)	Plastic litter	PRISMA (30 m) (2)	No	-	[455]	-
LU/LC (1)	Plastic litter	Sentinel-2 (10–20–60 m)	No	-	[126]	-
LU/LC (1)	Primary production	Landsat (15–30 m)	No	[139]	-	-
LU/LC (1)	Sea level rise	-	Yes	[384,456]	-	-
LU/LC (1)	Seagrass	UAV	No	-	[207]	-
LU/LC (1)	Seagrass	WorldView 2–3 (~0.5–4 m)	No	[235]	[236]
LU/LC (1)	Soil salinization	Landsat (15–30 m)	No	[457]	[458]	-
LU/LC (1)	Soil salinization	MODIS (0.5–1 km)	No	[459]	-	-
LU/LC (1)	Urban sprawl	ASD Portable (2)	No	-	-	[460]
LU/LC (1)	Urban sprawl	Hyperion (30 m) (2)	No	[115]	-	-
LU/LC (1)	Urban sprawl	Landsat (15–30 m)	No	[461,462]	[463,464,465]	[392]
LU/LC (1)	Urban sprawl	MIVIS airborne (2)	No	[466]	-
LU/LC (1)	Urban sprawl	PRISMA (30 m) (2)	No	[115]	-
LU/LC (1)	Water quality estimation	Landsat (15–30 m)	No	-	[323,324]	-
LU/LC (1)	Water quality estimation	Sentinel-2 (10–20–60 m)	No	-	[323,324]	-
LU/LC (1)	Water quality estimation	Landsat (15–30 m)	No	-	[306]	-
LU/LC	Wetland biodiversity estimation	ZiYhis1-02D-HSI (30 m) (2)	No	-	[467,468]	[193]
LU/LC	Wetland biodiversity estimation	ZiYuan1-02D-MSI (10 m)	No	-	[468]	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters; ⁽²⁾ hyperspectral data.

. The authors analyzed 190 remote data and 16 available products. Among the remote data, 166, 10, 9, and 5 were acquired from satellite, airborne, UAV, and fixed platforms, respectively. Among these data, 6 were obtained from active sensors (GaoFen-3, Sentinel-1, and UVA sensors), whereas 190 were acquired from passive sensors. Among these data, 158 were acquired from multispectral sensors (Landsat sensors were the first to be employed, followed by Sentinel-2 sensors) and 17 were acquired from hyperspectral sensors (GaoFen-5-HIS sensor was the first to be employed).

Most of these papers (77%) monitored LU/LC over time using different remote data (e.g., [114]) and 54% of the papers retrieved this parameter from several remote data (e.g., [115]). Moreover, 23 papers combined remote data with models (e.g., Acharyya et al. [116] coupled SWAT and DSAS models for assessment of deltaic estuarine transformations of rivers and also simulated SWAT models using LU/LC and shoreline maps retrieved from remote data) or GIS (e.g., [117]).

3.3.17. Leaf Area Index

The characteristics of the five eligible papers whose authors mapped the leaf area index (LAI) [118] are summarized in

Table A17. The eligible papers that mapped and/or monitored LAI.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
LAI (1)	Habitat	Sentinel-2 (10–20–60 m)	No	-	[469]	-
LAI (1)	Mangrove ecosystems	MODIS	Yes	-	-	[470]
LAI (1)	Mangrove ecosystems	Landsat (15–30 m)	No	-	[365]	-
LAI (1)	Mangrove ecosystems	Sentinel-2 (10–20–60 m)	No	-	[365]	[123]
LAI (1)	Mangrove ecosystems	SPOT (~10–20 m)	No	-	[365]	-
LAI (1)	Mangrove ecosystems	WorldView-2 (~0.5–2 m)	No	-	-	[123]
LAI (1)	Mangrove ecosystems	UAV	No	-	-	[123]
LAI (1)	Seagrass	Landsat (15–30 m)	No	-	[233]	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters.

. The authors exploited nine remote data and did not use available remote products. All data were acquired from passive sensors: eight were obtained from satellite sensors (Sentinel-2 sensors were the first to be employed, followed by Landsat sensors and then SPOT and WorldView sensors) and one was acquired from UAV multispectral sensors.

Most of these papers (80%) monitored LAI over time (e.g., [119]), whereas about half of the papers (60%) compared different methods to minimize errors and retrieved this parameter from several remote data (e.g., [120]).

3.3.18. Mangroves

The characteristics of the 35 eligible papers whose authors mapped mangroves are summarized in

Table A18. The eligible papers that mapped and/or monitored mangroves.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Mangroves	Aboveground biomass	Sentinel-1 (~10 m)	No	-	-	[471]
Mangroves (1)	Coastline change	Landsat (15–30 m)	No	-	-	[214]
Mangroves (1)	Coastal vulnerability assessment	Landsat (15–30 m)	No	[411]	-	-
Mangroves (1)	Influence of El-Nino	Landsat	No	-	-	[472]
Mangroves (1)	Invasive alien species	Sentinel-2 (10–20–60 m)	No	-	-	[473]
Mangroves (1)	LU/LC change	Landsat (15–30 m)	No	[440]	-	-
Mangroves (1)	LU/LC change	Sentinel-2 (10–20–60 m)	No	[440]	-	-
Mangroves (1)	Mangrove ecosystems	-	Yes	[474]	-	[475]
Mangroves (1)	Mangrove ecosystems	ALOS-2	No	-	[365]	-
Mangroves (1)	Mangrove ecosystems	Corona	No	[121]	-	-
Mangroves (1)	Mangrove ecosystems	GaoFen-1 (2–8 m)	No	-	-	[367]
Mangroves (1)	Mangrove ecosystems	GaoFen-5-HIS (2)	No	-	[122]	-
Mangroves (1)	Mangrove ecosystems	Google Earth Image	No	[121,476]	-	-
Mangroves (1)	Mangrove ecosystems	Hyperion (30 m) (2)	No	-	[122]	-
Mangroves (1)	Mangrove ecosystems	Landsat (15–30 m)	No	[121,124]	[365,369,447,448,449]	[446,450,451,470,477,478,479]
Mangroves (1)	Mangrove ecosystems	MODIS (0.5–1 km)	No	-	[480]	[470]
Mangroves (1)	Mangrove ecosystems	SPOT (~10–20 m)	No	-	[365]	-
Mangroves (1)	Mangrove ecosystems	PRISMA (30 m) (2)		-	[122]	-
Mangroves	Mangrove ecosystems	Sentinel-1 (~10 m)	No	[481]	-	-
Mangroves (1)	Mangrove ecosystems	Sentinel-1 (~10 m)	No	-	[452]	[368,446]
Mangroves	Mangrove ecosystems	Sentinel-2 (10–20–60 m)	No		[482]	-
Mangroves (1)	Mangrove ecosystems	Sentinel-2 (10–20–60 m)	No	[476]	[122,365,366,369]	[123,368,446,454,470]
Mangroves	Mangrove ecosystems	Sentinel-3 (300 m)	No	-	[482]	-
Mangroves (1)	Mangrove ecosystems	HSI ZiYuan1-02D (30 m) (2)	No	-	[122]	-
Mangroves (1)	Mangrove ecosystems	MSI ZiYuan1–3 (2.1–8 m)	No	-	-	[367]
Mangroves (1)	Mangrove ecosystems	WorldView-2 (~0.5–2 m)	No	-	-	[123]
Mangroves (1)	Mangrove ecosystems	UAV	No	-	-	[123]
Mangroves (1)	Tidal flat	Sentinel-2 (10–20–60 m)	No	-	[453]	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters; ⁽²⁾ hyperspectral data.

. The authors analyzed 55 remote data and 6 products that were available online. Among the remote data, 52, 2, and 1 were acquired from satellite, airborne, and UAV platforms, respectively. Among these data, 4 were obtained from active sensors (ALOS-2 and Sentinel-1 data), whereas 48 were acquired from passive sensors. Among them, 44 were acquired from multispectral sensors (Landsat sensors were the first to be employed, followed by Sentinel-2 sensors) and 4 were acquired from hyperspectral sensors (GaoFen-5-HIS, HSI ZiYuan1-02D, Hyperion, and PRISMA sensors).

Most of these papers (77%) monitored mangroves over time (e.g., [121]), whereas 30% of the papers compared different data (e.g., [122]) and methods (e.g., [123]). Moreover, four papers combined remote data with models (e.g., Gitau et al. [124] estimated the flood extent in the delta using the hydrological model, and these data were compared with mangrove and vegetation cover maps) and GIS (e.g., [124]).

3.3.19. Marine Litter

The characteristics of the 14 eligible papers whose authors mapped marine litter (i.e., “items that have been made or used by people and deliberately discarded, unintentionally lost, or transported by winds and rivers, into the sea and on beaches” [125]) are summarized in

Table A19. The eligible papers that mapped and/or monitored marine litter.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Marine litter (1)	Litter	UAV	No	-	-	[362]
Marine litter (1)	Marine litter	Orthophoto	No	[370]	-	-
Marine litter (1)	Marine litter	UAV	No	-	-	[128]
Marine litter	Plastic litter	GNSS-R systems (lab)	No	[483]	-	-
Marine litter (1)	Plastic litter	PRISMA (30 m) (2)	No	-	[455]	-
Marine litter	Plastic litter	Sentinel-2 (10–20–60 m)	No	-	-	[54]
Marine litter (1)	Plastic litter	Sentinel-2 (10–20–60 m)	No	-	[126,484]	-
Marine litter	Plastic litter	UAV	No	-	[485]	[486,487,488]
Marine litter	Plastic litter	UAV Hyperspectral (2)	No	-	-	[489]
Marine litter (1)	Plastic litter	-	Yes	-	-	[127,486]

⁽¹⁾ This parameter was mapped and monitored together with other parameters; ⁽²⁾ hyperspectral data.

. The authors analyzed 14 remote data and 1 available product. Among the remote data, 4, 2, 7, and 1 were acquired from satellite, airborne, UAV, and fixed platforms, respectively. Among these data, one was obtained from an active sensor (GNSS-R systems) in the laboratory, whereas 13 were acquired from passive sensors. Among these data, 11 were acquired from multispectral sensors (UAV multispectral sensors were the first to be employed) and 2 were acquired from hyperspectral sensors (UAV hyperspectral and PRISMA sensors).

About half of the papers (43%) compared different methods, 36% monitored marine litter over time (e.g., [54]), two papers also used GIS [126,127], and one paper compared different data [128].

3.3.20. “Fires and Thermal Anomalies”, Nightlight, and Nighttime Light Intensity

The characteristics of the five eligible papers whose authors analyzed “fires and thermal anomalies”, nightlight, and nighttime light intensity are summarized in

Table A20. The eligible papers that monitored “fires and thermal anomalies”, nightlight, and nighttime light intensity.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Fires and thermal anomalies (1)	Methane plume	Visible Infrared Imaging Radiometer Suite (VIIRS)	Yes	-	[130]	-
Nightlight (1)	LU/LC change	-	Yes	[129]	-	-
Nightlight (1)	LU/LC change	Visible Infrared Imaging Radiometer Suite (VIIRS)	Yes	-	-	[444]
Nightlight (1)	Plastic litter	Visible Infrared Imaging Radiometer Suite (VIIRS)	Yes	-	-	[127]
Nightlight (1)	Urban sprawl	-	Yes	[113]	-	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters.

. The authors did not retrieve maps from remote data and exploited five available products that were provided by the Visible Infrared Imaging Radiometer Suite.

All papers monitored these parameters over time and combined remote data with models (e.g., [129]) and GIS (e.g., [127]).

3.3.21. Methane and Oil

The characteristics of the 10 eligible papers whose authors mapped methane and oil are summarized in

Table A21. The eligible papers that mapped and/or monitored methane and oil.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Methane (1)	Methane plume	Landsat (15–30 m)	No	-	[130]	-
Methane (1)	Methane plume	Sentinel-2 (10–20–60 m)	No	-	[130]	-
Methane (1)	Methane plume	WorldView-3 (~0.5–4 m)	No	-	[130]	-
Oil (1)	Oil spill	-	No	[490]	-	-
Oil	Oil spill	Sentinel-1 (~10 m)	No	-	[491,492,493]	[131]
Oil (1)	Oil spill	Sentinel-1 (~10 m)	No	[308]	[307,334,335,494]	[92]
Oil	Oil spill	Sentinel-2 (10–20–60 m)	No	-	-	[131]
Oil (1)	Oil spill	Sentinel-2 (10–20–60 m)	No	-	[307,335]	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters.

. The authors analyzed 17 remote data and did not use available products. Among the remote data, 14, 1, and 2 were acquired from satellite, UAV, and fixed platforms, respectively. Among these data, 11 were obtained from active sensors (Sentinel-1 sensors were the first to be employed), whereas 5 were acquired from multispectral sensors (Landsat, Sentinel-2, WorldView sensors).

Most of these papers (80%) monitored these parameters over time (e.g., [130]), whereas about half of the papers (30%) retrieved these parameters from several remote data (e.g., [131]). Moreover, one paper also used models (i.e., [92]).

3.3.22. Particulate Organic Carbon

The characteristics of the five eligible papers whose authors mapped particulate organic carbon (POC) [48] are summarized in

Table A22. The eligible papers that mapped and/or monitored POC.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
POC (1)	Macroalgae	MODIS (0.5–1 km)	Yes	-	-	[204]
POC (1)	Oil spill	-	Yes	-	[307]	-
POC (1)	Particulate organic carbon	MODIS (0.5–1 km)	Yes	[98]	[309]	-
POC (1)	Particulate organic carbon	SeaWiFS (1.1–4.5 km)	Yes	[98]	-	-
POC (1)	Particulate organic carbon	VIIRS-SNPP	Yes	[98]	-	-
POC (1)	Primary production	MODIS (0.5–1 km)	Yes	[139]	-	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters.

. The authors analyzed seven available remote products and did not retrieve the maps from remote data.

Four papers monitored POC over time, compared different methods or products in order to minimize errors, and combined remote data with models or GIS (e.g., [98]).

3.3.23. Photosynthetically Active Radiation

The characteristics of the six eligible papers whose authors mapped photosynthetically active radiation (PAR) (i.e., the flux density of photons in the 400–700 nm waveband incident per unit time on a unit surface [132]) are summarized in

Table A23. The eligible papers that mapped and/or monitored PAR.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
PAR (1)	Green tide	MODIS (1 km)	Yes	-	[199]	-
PAR (1)	Mangrove ecosystems	MODIS (1 km)	Yes	-	[133]	-
PAR (1)	Mangrove ecosystems	Sentinel-2 (10–20–60 m)	Yes	-	[133]	-
PAR (1)	Phytoplankton crops and taxonomic composition	SeaWiFS (1.1–4.5 km)	Yes	-	[96]	-
PAR (1)	Primary production	MODIS (0.5–1 km)	Yes	-	[157]	-
PAR (1)	Primary production	MODIS (0.5–1 km)	Yes	[139]	[289]	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters.

. The authors analyzed six available products and did not retrieve maps from remote data.

All papers monitored PAR over time, and one study compared different methods and different products [133].

3.3.24. Phycocyanin

The characteristics of the one eligible paper whose authors mapped phycocyanin [134] are summarized in

Table A24. The eligible papers that mapped and/or monitored phycocyanin.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Phycocyanin (1)	Phycocyanin	HICO™ (~90 m) (2)	No	-	-	[134]
Phycocyanin (1)	Phycocyanin	PRISMA (30 m) (2)	No	-	-	[134]

⁽¹⁾ This parameter was mapped and monitored together with other parameters; ⁽²⁾ hyperspectral data.

. The authors compared two maps retrieved from two hyperspectral satellite data (HICO and PRISMA) using different methods and monitored the parameter over time.

3.3.25. Plumes

The characteristics of the nine eligible papers whose authors mapped plumes are summarized in

Table A25. The eligible papers that mapped and/or monitored plumes.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Plumes (1)	Anthropogenic activities	Landsat (15–30 m)	No	-	[495]	-
Plumes (1)	Anthropogenic activities	MERIS (300 m)	No	-	[495]	-
Plumes (1)	Anthropogenic activities	MODIS (0.5–1 km)	No	-	[495]	-
Plumes (1)	Anthropogenic activities	SeaWIFs (1.1–4.5 km)	No	-	[495]	-
Plumes	Sediment plumes	UAV	No	-	[496]	-
Plumes (1)	Sediment plumes	MODIS (0.5–1 km)	No	-	[497]	-
Plumes (1)	Sediment plumes	Sentinel-2 (10–20–60 m)	No	-	[135]	-
Plumes (1)	Suspended sediments	Geostationary Ocean Color Imager (GOCI)	No	-	-	[337]
Plumes (1)	Suspended sediments	Landsat (15–30 m)	No	-	-	[337]
Plumes (1)	Suspended sediments	Sentinel-2 (10–20–60 m)	No	-	-	[337]
Plumes (1)	Suspended sediments	Landsat (15–30 m)	No	-	[292]	-
Plumes (1)	Suspended sediments	Sentinel-2 (10–20–60 m)	No	-	[292]	-
Plumes (1)	Suspended sediments	-	Yes	-	[336]	-
Plumes (1)	Water quality estimation	Sentinel-2 (10–20–60 m)	No	-	[498]	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters.

. The authors analyzed 17 remote data and 1 available product. Among the remote data, 16 and 1 were acquired from satellite and fixed platforms, respectively. All remote data were acquired from multispectral sensors (Landsat sensors were the first to be employed, followed by Sentinel-3 and MODIS and then MERIS and SeaWiFS sensors). All authors compared different remote data and monitored plumes over time (e.g., [135]).

3.3.26. Primary Production

The characteristics of the nine eligible papers whose authors mapped primary production (PP) [136,137] are summarized in

Table A26. The eligible papers that mapped and/or monitored PP.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
PP (1)	LU/LC change	-	Yes	[61]	-	-
PP (1)	LU/LC change	MODIS (0.5–1 km)	Yes	[389]	-	-
PP (1)	Macroalgae	MODIS (0.5–1 km)	Yes	-	-	[204]
PP (1)	Mangrove ecosystems	MODIS (0.5–1 km)	Yes	-	-	[470]
PP (1)	Primary production	-	Yes	[69]	[289]	-
PP (1)	Primary production	Landsat (15–30 m)	No	[139]	-	-
PP (1)	Primary production	MODIS (0.5–1 km)	No	[138,139]	-	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters.

. The authors analyzed two remote data and six available products. All remote data were acquired from multispectral satellite sensors (MODIS and Landsat).

Most of these papers (89%) monitored PP over time (e.g., [138]), whereas about half of the papers (44%) exploited different remote data (e.g., [139]) and compared the results obtained with different methods (e.g., [138]).

3.3.27. Salt Marshes

The characteristics of the nine eligible papers whose authors mapped salt marshes are summarized in

Table A27. The eligible papers that mapped and/or monitored salt marshes.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Salt marshes (1)	Aboveground biomass	ASD Portable (2)	No	-	[140]	-
Salt marshes (1)	Aboveground biomass	Sentinel-2 (10–20–60 m)	No	-	[140]	-
Salt marshes (1)	Coastal salt marshes	Sentinel-1 (~10 m)	No	[141]	-	[388]
Salt marshes (1)	Coastal salt marshes	Sentinel-2 (10–20–60 m)	No	[141]	-	-
Salt marshes (1)	Habitat	Landsat (15–30 m)	No	-	[119,422]	-
Salt marshes (1)	Habitat	Sentinel-2 (10–20–60 m)	No	-	-	[427,428]
Salt marshes (1)	Habitat	UAV-Lidar	No	-	[142]	-
Salt marshes (1)	Habitat	UAV-MSI	No	-	[142]	-
Salt marshes (1)	Leaf area index	Sentinel-2 (10–20–60 m)	No	-	[469]	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters; ⁽²⁾ hyperspectral data.

. The authors analyzed 13 remote data and did not use available products. Among the remote data, 9, 2, and 2 were acquired from satellite, UAV, and fixed platforms, respectively. Among these data, 4 were obtained from active sensors (Sentinel-1 and UAV LIDAR), whereas 9 were acquired from passive sensors. Among them, 8 were acquired from multispectral sensors (Sentinel-2 sensors were the first to be employed) and 1 was acquired from a hyperspectral sensor (ASD portable).

All papers monitored salt marshes over time (e.g., [140]), whereas about half of the papers (56%) compared different methods to minimize errors (e.g., [141]) and 56% retrieved this parameter from several remote data (e.g., [142]). Moreover, Zhang et al. [55] evaluated the invasion process, ecological impact, and coastal protection function of Spartina alterniflora using the saltmarsh classification and hydrodynamic modeling.

3.3.28. Sea Level Anomaly and Sea Level Rise

The characteristics of the 46 eligible papers whose authors mapped sea level anomaly (SLA) and sea level rise (SLR) are summarized in

Table A28. The eligible papers that mapped and/or monitored SLA and SLR.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
SLA (1)	Bottom friction coefficients	-	Yes	-	-	[268]
SLA (1)	Climate change	-	Yes	-	-	[147]
SLA (1)	Climate change	NOAA	Yes	-	-	[396,397]
SLA (1)	Coastline change	-	Yes	[150,269]	[499,500]	-
SLA (1)	Coastal vulnerability assessment	-	Yes	[273]	-	-
SLA (1)	Coastal vulnerability assessment	ENVISAT	Yes	-	-	[145]
SLA (1)	Coastal vulnerability assessment	ERS	Yes	-	-	[145]
SLA (1)	Coastal vulnerability assessment	RADARSAT	Yes	-	-	[145]
SLA (1)	Coastal vulnerability assessment	Global Sea Level Observing (GLOSS)	Yes	-	[275]	-
SLA (1)	Effects of extreme events	-	Yes	[153]	-	[170,501]
SLA	Effects of extreme events	Global Navigation Satellite Systems interferometric reflectometry (GNSS-R) fixed station	No	-	-	[143]
SLA (1)	Fishing zones	-	Yes	[303]	-	-
SLA (1)	Flood extent	-	Yes	[62]	[354]	-
SLA (1)	Global tide	-	Yes	-	-	[502]
SLA (1)	Global tide	FES2014	Yes	-	-	[503]
SLA (1)	LU/LC change	-	Yes	-	-	[443]
SLA (1)	Influence of El-Nino	-	Yes	-	-	[472]
SLA (1)	Plastic litter	-	Yes	-	-	[146]
SLA (1)	Sea level anomaly	-	Yes	[64]	-	[63,144,504,505,506,507]
SLA (1)	Sea level anomaly	Jason-3 altimeter	No	-	[290]	-
SLA	Sea level anomaly	GNSS-R fixed station	Yes	-	-	[508]
SLA	Sea level anomaly	GNSS-R Geo fixed station	No	-	[509]
SLA (1)	Sea level anomaly	X-TRACK multisatellite	Yes	-	-	[144]
SLA (1)	SST		Yes	-	-	[510]
SLA (1)	Subsidence	Sentinel-1 (~10 m)	No	-	[379]	-
SLA (1)	Tidal evolution	X-TRACK multisatellite	Yes	-	[511]	-
SLA (1)	Tidal flat	-	Yes	-	[375]	-
SLR (1)	Sea level rise	-	Yes	[384,456]	[381]	[512]
SLR (1)	Sea level rise	Global LiDAR lowland DTM (GLL_DTM)	Yes	-	-	[513]

⁽¹⁾ This parameter was mapped and monitored together with other parameters.

. The authors analyzed 7 remote data and 53 available products. Among the remote data, 3 and 1 were acquired from satellite and fixed platforms, respectively. All these data were obtained from active sensors (global navigation satellite systems were the first to be employed, followed by Jason-3 altimeter).

Most of these papers (70%) monitored SLA or SLR over time (e.g., [143]), whereas 26% of the papers compared different methods (e.g., [144]) and 33% retrieved these parameters from several remote data or used several available products (e.g., [145]). Moreover, eight papers combined remote data with models (e.g., Tsiaras et al. [146] assimilated SLA and SST data in the hydrodynamic model) or GIS (e.g., [147]).

3.3.29. Sea Surface Salinity

The characteristics of the 17 eligible papers whose authors mapped sea surface salinity (SSS) are summarized in

Table A29. The eligible papers that mapped and/or monitored SSS.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
SSS (1)	Coastal salt marshes	-	Yes	-	-	[388]
SSS (1)	Effects of extreme events	-	Yes	[59]	-	-
SSS (1)	Harmful algal bloom	-	Yes	[304]	-	-
SSS (1)	Hydrographic structure	-	Yes	[280]	-	-
SSS (1)	Mangrove ecosystems	-	Yes	-	[133,480]	-
SSS (1)	Marine aquaculture	-	Yes	-	[514]	-
SSS (1)	Phytoplankton	-	Yes	-	[311,318]	-
SSS (1)	Red tide bloom	GNSS-R	No	-	[188]	-
SSS (1)	Seagrass	-	Yes	-	-	[321]
SSS (1)	SSS	-	Yes	-	-	[148]
SSS	SSS plumes	-	Yes	-	-	[515]
SSS (1)	SST and SSS fronts	-	Yes	[65]	-	-
SSS (1)	Water quality estimation	-	Yes	-	[327]	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters.

. The authors analyzed 1 remote data and 16 available products. The remote data were acquired from an active satellite sensor (GNSS-R). Most of these papers (88%) monitored SSS over time (e.g., [148]).

3.3.30. Sea Surface Temperature

The characteristics of the 59 eligible papers whose authors mapped sea surface temperature (SST) are summarized in

Table A30. The eligible papers that mapped and/or monitored SST.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
SST (1)	Anthropogenic activities	Landsat (15–30 m)	No	-	[167]	-
SST (1)	Biotoxin risk	-	Yes	-	-	[296]
SST (1)	Algal blooms	MODIS (0.5–1 km)	No	[72]	-	-
SST (1)	Algae distribution	-	Yes	-	[206]	-
SST (1)	Biosiliceous sedimentation flux	Landsat (15–30 m)	No	-	[267]	-
SST (1)	Cyanobacterial pigment concentrations	-	Yes	-	-	[89,182]
SST (1)	Discharge water temperature	Landsat (15–30 m)	No	-	[516]	-
SST (1)	Effects of COVID-19 lockdown	-	Yes	-	[169]	-
SST	Escherichia coli	UAV TIR	No	-	[517]	-
SST (1)	Effects of extreme events	-	Yes	[59,153]	-	-
SST (1)	Fishing zones	MODIS (0.5–1 km)	Yes	[303,382]	-	-
SST (1)	Giant kelp	NOAA	Yes	-	-	[228]
SST (1)	Green tide	AVHRR (5 km)	Yes	-	[199]	-
SST (1)	Green tide	Landsat (15–30 m)	No	-	-	[200]
SST (1)	Green tide	MODIS (0.5–1 km)	No	-	-	[200]
SST (1)	Harmful algal bloom	MODIS (0.5–1 km)	Yes	[304]	[305]	[76]
SST (1)	Harmful algal risk	-	Yes	-	-	[296]
SST	Habitat	NOAA	Yes	-	-	[518]
SST (1)	Hydrographic structure	-	Yes	[280]	-	-
SST (1)	Industrial warm drainage	TASI-600, airborne thermal infrared imaging spectral system	No	[519]	-	-
SST (1)	Influence of El-Nino	-	Yes	-	-	[472]
SST (1)	Invasive alien species	-	Yes	-	-	[520]
SST (1)	Macroalgae	-	Yes	-	[206]	-
SST (1)	Mangrove ecosystems	MODIS (0.5–1 km)	Yes	-	[133,480]	-
SST (1)	Marine aquaculture	-	Yes	-	[514]	[75]
SST	Marine heatwaves	-	Yes	-	-	[149]
SST (1)	Marine heatwaves	-	Yes	-	[282]
SST (1)	Marine heatwaves	NOAA	Yes	-	[521]	-
SST (1)	Oil spill	-	Yes	-	[307]	-
SST (1)	Phytoplankton	-	Yes	-	[311,318]	-
SST (1)	Primary production	-	Yes	-	[289]	-
SST (1)	Primary production	Landsat (15–30 m)	Yes	[139]
SST (1)	Primary production	MERIS (300 m)	Yes	-	[157]	-
SST (1)	Primary production	MODIS (0.5–1 km)	Yes	[69,138]	[157]	-
SST (1)	Particulate organic carbon	MODIS (0.5–1 km)	Yes	-	[309]	-
SST (1)	Phytoplankton blooms	MODIS (0.5–1 km)	No	[184]	-
SST (1)	Plastic litter	-	Yes	-	-	[146]
SST (1)	Red tide bloom	GNSS-R	No	-	[188]	-
SST (1)	Seagrass	MODIS (0.5–1 km)	Yes	-	-	[321]
SST (1)	Sea ice	-	Yes	-	-	[111]
SST (1)	SSS	-	Yes	-	-	[148]
SST (1)	SSS and SST fronts	-	Yes	[65]	-	-
SST (1)	SST front	MODIS (0.5–1 km)	Yes	-	-	[522,523]
SST	SST prediction capability	AATSR	Yes	[68]	-	-
SST	SST prediction capability	AVHRR	Yes	[68]	-	-
SST	SST prediction capability	MODIS (0.5–1 km)	Yes	[68]	-	-
SST	SST prediction capability	SEVIRI	Yes	[68]	-	-
SST (1)	SST	-	Yes	-	[291]	[510]
SST (1)	Water quality estimation	AVHRR	Yes	-	-	[322]

⁽¹⁾ This parameter was mapped and monitored together with other parameters.

. The authors analyzed 32 remote data and 52 available products. Among the remote data, 30, 1, and 1 data were acquired from satellite, airborne, and UAV platforms, respectively. All remote data were acquired from multispectral sensors (MODIS sensors were the first to be employed, followed by Landsat sensors).

Most of these papers (88%) monitored SST over time (e.g., [149]), whereas about half of the papers (54%) retrieved SST from several remote data (e.g., [68]) and 46% compared different methods to obtain results with the lowest error (e.g., [68]). Moreover, 13 papers combined remote data with models (e.g., [146]) or GIS (e.g., [76]).

3.3.31. Shoreline

The characteristics of the 112 eligible papers whose authors mapped shorelines are summarized in

Table A31. The eligible papers that mapped and/or monitored the shoreline.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Shoreline (1)	Anthropogenic activities	Landsat (15–30 m)	No	-	[167]	-
Shoreline (1)	Avulsion sites	Landsat (15–30 m)	No	-	[340]	-
Shoreline (1)	Bathymetry	Sentinel-2 (10–20–60 m)	No	[84]	-	[258]
Shoreline (1)	Climate Change	-	Yes	-	-	[396]
Shoreline	Coastline change	-	Yes	-	-	[524]
Shoreline	Coastline change	Aerial photos	No	[525,526]	[527,528]	[398,529]
Shoreline (1)	Coastline change	Aerial photos	No	[400,406]	[530,531]	[398,532]
Shoreline (1)	Coastline change	ALOS Palsar	Yes	-	-	[117]
Shoreline	Coastline change	ASAR	No	-	-	[151]
Shoreline (1)	Coastline change	ASAR	No	-	[344]	-
Shoreline	Coastline change	Canadian RadarSAT-2 spaceborne	No	-	-	[533]
Shoreline	Coastline change	GaoFen-1 (2–8 m)	No	-	-	[534]
Shoreline	Coastline change	GaoFen3-SAR (4.5–5 m)	No	-	-	[151]
Shoreline	Coastline change	Google Earth image	No	[525,526]	[528,535]	-
Shoreline (1)	Coastline change	Google Earth image	No	[150,269]	[345,404,531]	[398,532]
Shoreline	Coastline change	HSI ZiYuan1-02D (30 m) (2)	No		[409]	-
Shoreline (1)	Coastline change	Landsat (15–30 m)	No	[150,269,293,342,399,400,406]	[213,344,345,401,402,403,404,499,500,530,536,537]	[117,171,214,271,332,347,532]
Shoreline	Coastline change	Landsat (15–30 m)	No	[525,526]	[538,539,540]	[70,151,152,154,541,542,543]
Shoreline	Coastline change	SPOT (~10–20 m)	No	-	[544]	-
Shoreline (1)	Coastline change	Sentinel-1 (~10 m)	No	[343]	[344]	[117]
Shoreline	Coastline change	Sentinel-1 (~10 m)	No	-	[538]	[151,545]
Shoreline (1)	Coastline change	Sentinel-2 (10–20–60 m)	No	[343,406,546]	[270,346,348,401,407,408,409,530,537]	[171,347]
Shoreline	Coastline change	Sentinel-2 (10–20–60 m)	No	[343,406]	[538]	[70,541]
Shoreline (1)	Coastline change	RapidEye (5 m)	No	[293]	[410]	-
Shoreline (1)	Coastline change	Planetscope (3 m)	No	[293]	[410]	-
Shoreline	Coastline change	Pleiades (0.5–2 m)	No	-	[527,547]	-
Shoreline (1)	Coastline change	Portable lidar	No		[410]	-
Shoreline	Coastline change	UAV	no	-	[346,528]	-
Shoreline (1)	Coastline change	UAV	No	-	[270,346,348,531]	[398,529]
Shoreline	Coastline change	WorldView-2 (~0.5–2 m)	No	-	[527]	-
Shoreline (1)	Coastal aquaculture ponds	Sentinel-2 (10–20–60 m)	No	-	[77]	-
Shoreline (1)	Coastal vulnerability assessment	Aerial photos	No	[91]	[361]	-
Shoreline (1)	Coastal vulnerability assessment	Google Earth image	No	-	[361]	-
Shoreline	Coastal vulnerability assessment	Landsat (15–30 m)	No	-	-	[548]
Shoreline (1)	Coastal vulnerability assessment	Landsat (15–30 m)	No	[411]	[275]	[102,166,352,412]
Shoreline (1)	Coastal vulnerability assessment	Sentinel-2 (10–20–60 m)	No	-	-	[352]
Shoreline (1)	Coastal vulnerability assessment	Pleiades (0.5–2 m)	No	-	-	[352]
Shoreline (1)	Coastal vulnerability assessment	UAV	No	[91]	-	-
Shoreline (1)	Coastal vulnerability assessment	Video camera systems	No	[273]	-	-
Shoreline (1)	Coastal vulnerability assessment	WorldView-2 (~0.5–2 m)	No	[91]	-	-
Shoreline (1)	Deltaic estuarine transformations	Landsat (15–30 m)	No	[116]	-	-
Shoreline (1)	Effects of COVID-19 lockdown	GaoFen-1 WFV (16 m)	No	-	-	[549]
Shoreline (1)	Effects of COVID-19 lockdown	Landsat (15–30 m)	No	-	-	[549]
Shoreline	Effects of extreme events	Google Earth image	No	[550]	-	-
Shoreline	Effects of extreme events	Landsat (15–30 m)	No	[550]	-	-
Shoreline (1)	LU/LC change	-	Yes	-	-	[433]
Shoreline (1)	LU/LC change	Aerial photos	No	[364]	-	-
Shoreline (1)	LU/LC change	Landsat (15–30 m)		-	-	[217]
Shoreline (1)	LU/LC change	Quickbird		-	-	[217]
Shoreline (1)	LU/LC change	SPOT (~10–20 m)		-	-	[217]
Shoreline (1)	LU/LC change	Pleiades (0.5–2 m)	No	[364]	-	-
Shoreline (1)	Habitat	GeoEye	No	-	[420]	-
Shoreline (1)	Habitat	Google Earth image	No	-	-	[164]
Shoreline (1)	Habitat	Sentinel-2 (10–20–60 m)	No	-	[551,552]	-
Shoreline (1)	Morphological evolution	Huanjing-1B (150–300 m)	No	-	[294]	-
Shoreline (1)	Mangrove ecosystems	Landsat (15–30 m)	No	-	[122]	[475,477,479]
Shoreline (1)	Morphological evolution	GaoFen-1 WFV (16 m)	No	-	[294]	-
Shoreline	Morphological evolution	Landsat (15–30 m)	No	[283]	-	-
Shoreline (1)	Morphological evolution	Landsat (15–30 m)	No	-	[285,294]	-
Shoreline (1)	Morphological evolution	Sentinel-2 (10–20–60 m)	No	-	[553]	-
Shoreline (1)	Morphological evolution	UAV	No	-	[285,287]	-
Shoreline (1)	Oil spill	-	Yes	[490]	[494]	-
Shoreline (1)	Oil spill	Sentinel-1 (~10 m)	No	-	[334]	-
Shoreline (1)	Sea level rise	Landsat (15–30 m)	No	-	[381]	[506]
Shoreline (1)	Sea level rise	WorldView-2 (~0.5–2 m)	No	-	[381]	[506]
Shoreline (1)	Suspended sediments	Landsat (15–30 m)	No	-	-	[337]
Shoreline (1)	Suspended sediments	Sentinel-2 (10–20–60 m)	No	-	-	[337]
Shoreline (1)	Tidal flat	Sentinel-2 (10–20–60 m)	No	-	-	[554]
Shoreline (1)	Water quality estimation	Landsat (15–30 m)	No	-	[323,324]	-
Shoreline (1)	Water quality estimation	Sentinel-2 (10–20–60 m)	No	-	[323,324]	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters; ⁽²⁾ hyperspectral data.

. The authors analyzed 204 remote data and 6 available products. Among the remote data, 152, 29, 18, and 5 were acquired from satellite, airborne, UAV, and fixed platforms, respectively. Among these data, 17 were obtained from active sensors (Sentinel-1 sensors were the first to be employed, followed by GaoFen-3 and airborne LIDAR sensors), whereas 185 were acquired from passive sensors. Among the remote data, 183 were acquired from multispectral sensors (Landsat sensors were the first to be employed, followed by Sentinel-2 sensors) and 1 was acquired from a hyperspectral sensor (his ZiYuan1-02D sensor).

Most of these papers (88%) monitored shoreline over time (e.g., [150]), whereas half of the papers retrieved this parameter from several remote data (e.g., [151]) and compared different methods (e.g., [152]). Moreover, 21 papers combined remote data with models (e.g., [153]) or GIS (e.g., [154]).

3.3.32. Soil Salinization and Soil Moisture

The characteristics of the 10 eligible papers whose authors analyzed soil salinization and soil moisture are summarized in

Table A32. The eligible papers that mapped and/or monitored soil salinization and soil moisture.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Soil salinization (1)	LU/LC change	Landsat (15–30 m)	No	-	-	[434]
Soil salinization	Soil salinization	ASD Portable (2)	No	-	-	[555]
Soil salinization (1)	Soil salinization	Landsat (15–30 m)	No	[457,556]	[156,458]	[155]
Soil salinization (1)	Soil salinization	MODIS (0.5–1 km)	No	[459]	-	-
Soil salinization (1)	Soil salinization	Sentinel-2 (10–20–60 m)	No	-	-	[125]
Soil salinization (1)	Soil salinization	Portable SOC710VP	No	-	-	[125]
Soil salinization (1)	Soil salinization	UAV	No	-	-	[125]
Soil moisture (1)	Urban sprawl	MODIS (0.5–1 km)	No	[113]	-	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters; ⁽²⁾ hyperspectral data.

. The authors analyzed 12 remote data and did not use available products. Among the remote data, 9, 1, and 2 were acquired from satellite, UAV, and fixed platforms, respectively. Among the remote data, 11 were acquired from multispectral sensors (Landsat and MODIS sensors were the first to be employed) and 1 was acquired from a hyperspectral sensor (ASD portable).

Most of the papers (70%) monitored soil salinization over time (e.g., [155]), whereas about half of the papers (60%) compared different methods to obtain results with the lowest error (e.g., [156]) and 30% retrieved this parameter from several remote data (e.g., [125]). Moreover, one paper also used models [113].

3.3.33. Suspended Sediments

The characteristics of the 30 eligible papers whose authors mapped suspended sediments are summarized in

Table A33. The eligible papers that mapped and/or monitored SPM, SSCs, and TSM.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
SPM	Global suspended sediments	Visible Infrared Imaging Radiometer Suite (VIIRS)	No	-	-	[557]
SPM (1)	Suspended sediments	Geostationary Ocean Color Imager (GOCI) (500 m)	No	-	-	[337]
SPM	Suspended sediments	HY-1C/D (50 m)	No	[56]	-	-
SPM	Suspended sediments	Landsat (15–30 m)	No	[56]	-	-
SPM (1)	Suspended sediments	Landsat (15–30 m)	No	-	-	[337]
SPM (1)	Suspended sediments	Sentinel-2 (10–20–60 m)	No	-	-	[337]
SSCs (1)	Anthropogenic activities	Landsat (15–30 m)	No	-	[495]	-
SSCs (1)	Anthropogenic activities	MERIS (300 m)	No	-	[495]	-
SSCs (1)	Anthropogenic activities	MODIS (0.5–1 km)	No	-	[495]	-
SSCs (1)	Anthropogenic activities	SeaWIFs (1.1–4.5 km)	No	-	[495]	-
SSCs (1)	Coastal structures	GOCI (500 m)	No	-	-	[272]
SSCs (1)	Coastal structures	Landsat (15–30 m)	No	-	-	[272]
SSCs (1)	Climate change	Landsat (15–30 m)	No	-	[495]	-
SSCs (1)	Climate change	MERIS (300 m)	No	-	[495]	-
SSCs (1)	Climate change	MODIS (0.5–1 km)	No	-	[495]	-
SSCs (1)	Climate change	SeaWIFs (1.1–4.5 km)	No	-	[495]	-
SSCs (1)	Distribution of heavy metals	MODIS (0.5–1 km)	No	[276]	-	-
SSCs (1)	Effects of extreme events	GOCI (500 m)	No	-	-	[170]
SSCs (1)	Seaweed aquaculture	HY-1C (50 m)	No	-	[80]	-
SSCs (1)	Seaweed aquaculture	Sentinel-2 (10–20–60 m)	No	-	[80]	-
SSCs (1)	Sediment plumes	Sentinel-2 (10–20–60 m)	No	-	[135]	-
SSCs (1)	Suspended sediments	ASD Portable (2)	No	-	[292]	[558]
SSCs (1)	Suspended sediments	Landsat (15–30 m)	No	-	[292]	-
SSCs (1)	Suspended sediments	Sentinel-2 (10–20–60 m)	No	-	[292]	-
TSM (1)	Dissolved organic carbon	MODIS (0.5–1 km)	Yes	-	[297]
TSM (1)	Effects of COVID-19 lockdown	GaoFen-1 WFV (16 m)	No	-	-	[549]
TSM (1)	Effects of COVID-19 lockdown	Landsat (15–30 m)	No	-	-	[549]
TSM (1)	Eutrophication	DJI M600Pro UAV (2)	No	[299]	-	-
TSM (1)	Eutrophication	hyperspectral imager Pika L (2)	No	[299]	-	-
TSM (1)	Harmful algal bloom	Sentinel-2 (10–20–60 m)	No	-	-	[183]
TSM (1)	Marine aquaculture	-	Yes	-	-	[75]
TSM (1)	Phenology and niche ecology of harmful species	METEOSAT	Yes	-	[159]	-
TSM (1)	Phytoplankton	Sentinel-2 (10–20–60 m)	No	-	-	[331]
TSM (1)	Seagrass	Landsat (15–30 m)	No	-	[232]	-
TSM (1)	Seagrass	MODIS (0.5–1 km)	Yes	-	-	[321]
TSM (1)	Suspended sediments	-	Yes	-	[336]	-
TSM (1)	Water hyacinth	Sentinel-2 (10–20–60 m)	No	-	[160]	-
TSM (1)	Water quality estimation	MODIS (0.5–1 km)	No	-	[88]	-
TSM (1)	Water quality estimation	Landsat (15–30 m)	No	-	[86,88,191]	-
TSM (1)	Water quality estimation	Sentinel-2 (10–20–60 m)	No	[326]	[86,88,160,191,327]	-
TSM (1)	Water quality estimation	Sentinel-3 (300 m)	No	-	[86,88]	[90]
TSM (1)	Water quality estimation	UAV	No	[326]	-	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters; ⁽²⁾ hyperspectral data.

. In these papers, the concentrations of suspended sediments in the water column were quantified using different analytical methods (e.g., suspended particulate matter (SPM), suspended sediment concentrations (SSCs), and total suspended matter (TSM) [125]). The authors analyzed 55 remote data and 13 available products. Among the remote data, 49, 2, 3, and 1 were acquired from satellite, airborne, UAV, and fixed platforms, respectively. All these data were acquired from passive sensors. Among the remote data, 51 were acquired from multispectral sensors (Landsat and Sentinel-2 sensors were the first to be employed) and 3 were acquired from hyperspectral sensors (airborne hyperspectral sensors and ASD portable).

Most of the papers (87%) monitored suspended sediment over time (e.g., [157]), 70% compared different methods (e.g., [103]), and 57% retrieved this parameter from several remote data (e.g., [158]). Moreover, three papers combined remote data with models [75,86,159].

3.3.34. Tidal Data

The characteristics of the 27 eligible papers whose authors analyzed tidal data are summarized in

Table A34. The eligible papers that mapped and/or monitored tidal data.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Tidal data (1)	Bathymetry	Sentinel-2 (10–20–60 m)	No	[84]	-	-
Tidal data (1)	Bottom friction coefficients	-	Yes	-	-	[268]
Tidal data (1)	Bull and giant kelp	-	Yes	-	-	[226,229]
Tidal data (1)	Coastal structures	-	Yes	-	-	[272]
Tidal data (1)	Coastline change	-	Yes	[150,269,546]	[346,530,536]	[271,347,532]
Tidal data (1)	Coastal vulnerability assessment	-	Yes	-	-	[145]
Tidal data (1)	Coastal vulnerability assessment	WXTide	Yes	-	[275]	-
Tidal data (1)	Discharge water temperature	-	Yes	-	[516]	-
Tidal data (1)	Effects of extreme events	-	Yes	[501]	-	[170]
Tidal data (1)	Flood extent	-	Yes	[62]	-	-
Tidal data (1)	Global tide	-	Yes	-	-	[502]
Tidal data (1)	Global tide	FES2014	Yes	-	-	[503]
Tidal data (1)	Industrial warm drainage	airborne	No	[519]	-	-
Tidal data (1)	Habitat			-	-	[164]
Tidal data (1)	Morphological evolution	-	Yes	-	[295]	-
Tidal data (1)	Oil spill	-	Yes	-	[335]	-
Tidal data (1)	Sea level	-	Yes	-	-	[504]
Tidal data (1)	Sea level anomaly	-	Yes	-	-	[63]
Tidal data (1)	Sea level anomaly	X-TRACK multisatellite	Yes	-	[511]	[144]
Tidal data (1)	Tidal evolution	X-TRACK multisatellite	Yes	-	[511]	-
Tidal data (1)	Tidal flat	-	Yes	-	-	[554]

⁽¹⁾ This parameter was mapped and monitored together with other parameters.

. The authors analyzed 7 remote data and 26 available products. Among the remote data, 5, 1, and 1 were acquired from satellite, airborne, and fixed platforms, respectively. All these remote data were obtained from active sensors.

Most of the papers (62%) monitored tidal data over time (e.g., [138]), whereas three papers combined remote data with models or GIS (e.g., [63]).

3.3.35. Vegetation Cover

The characteristics of the 98 eligible papers whose authors mapped vegetation cover are summarized in

Table A35. The eligible papers that mapped and/or monitored vegetation cover.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Vegetation cover (1)	Aboveground biomass	Landsat (15–30 m)	No	-	-	[393]
Vegetation cover (1)	Aboveground biomass	Sentinel-2 (10–20–60 m)	No	-	[140]	-
Vegetation cover (1)	Aboveground biomass	UAV	No	-	-	[393]
Vegetation cover (1)	Agricultural nonpoint source pollution	Sentinel-2 (10–20–60 m)	No	-	[394]	-
Vegetation cover (1)	Anthropogenic activities	Landsat (15–30 m)	No	-	[167]	-
Vegetation cover (1)	Avulsion sites	Landsat (15–30 m)	No	-	[340]	-
Vegetation cover (1)	Coastal aquaculture ponds	Google Earth images	No	-	[78]	-
Vegetation cover (1)	Coastal aquaculture ponds	Landsat (15–30 m)	No	-	[78,79]	-
Vegetation cover (1)	Coastal aquaculture ponds	Sentinel-2 (10–20–60 m)	No	-	[78,79]	-
Vegetation cover (1)	Coastal forested wetland	UAV	No	-	[341]	-
Vegetation cover (1)	Coastline change	Landsat (15–30 m)	No	[163]	[403]	-
Vegetation cover (1)	Coastal vulnerability assessment	Landsat (15–30 m)	No	[411]	-	[412]
Vegetation cover (1)	Coastal vulnerability assessment	Sentinel-2 (10–20–60 m)	No	-	-	[166]
Vegetation cover (1)	Effects of extreme events	RapidEye (5 m)	No	-	-	[414]
Vegetation cover (1)	Giant kelp	Sentinel-2 (10–20–60 m)	No	-	-	[228]
Vegetation cover (1)	Green tide	Landsat (15–30 m)	No	-	[199]	[194]
Vegetation cover (1)	Green tide	Sentinel-2 (10–20–60 m)	No	-	[199]	-
Vegetation cover (1)	Habitat	GaoFen2 (0.8–3.2 m)	No	-	-	[559]
Vegetation cover (1)	Habitat	GaoFen3	No			[416]
Vegetation cover (1)	Habitat	Landsat (15–30 m)	No	[165,438]	[420,421,551]	[424,559]
Vegetation cover (1)	Habitat	RapidEye (5 m)	No	-	-	[424]
Vegetation cover (1)	Habitat	Remotely piloted aircraft (RPAs)	No	-	-	[230]
Vegetation cover (1)	Habitat	Sentinel-1 (~10 m)	No	-	-	[560]
Vegetation cover (1)	Habitat	Sentinel-2 (10–20–60 m)	No	[215]	[420,552]	[428,560]
Vegetation cover (1)	Habitat	UAV-MSI	No	-	[142]	[279,355]
Vegetation cover (1)	Habitat	UAV- Lidar	No	-	[142]	-
Vegetation cover (1)	Influence of El-Nino	Landsat	No	-	-	[472]
Vegetation cover (1)	Intertidal polychaete reefs	UAV-MSI	No	-	[82]	-
Vegetation cover (1)	Invasive alien species	Landsat (15–30 m)	No	-	-	[520]
Vegetation cover (1)	Invasive alien species	Sentinel-2 (10–20–60 m)	No	-	-	[473,520]
Vegetation cover (1)	Invasive alien species	UAV-MSI	No	[161]	-	-
Vegetation cover (1)	LU/LC change	-	Yes	[129]	-	-
Vegetation cover (1)	LU/LC change	Landsat (15–30 m)	No	[389,439,440]	[114,358,441]	[155,390,434,443]
Vegetation cover (1)	LU/LC change	Sentinel-2 (10–20–60 m)	No	[440]	[114,441]	-
Vegetation cover (1)	LU/LC change	WorldView-2 (~0.5–2 m)	No	-	[430]	-
Vegetation cover (1)	Macroalgae	MODIS (0.5–1 km)	No	-	[203]	-
Vegetation cover (1)	Mangrove ecosystems	G-LiHT airborne image (2)	No	-	-	[162]
Vegetation cover (1)	Mangrove ecosystems	Google Earth images	No	[476]	-	-
Vegetation cover (1)	Mangrove ecosystems	Landsat (15–30 m)	No	[124]	[449]	[162,478,479]
Vegetation cover (1)	Mangrove ecosystems	MODIS (0.5–1 km)	Yes	-	[480]	-
Vegetation cover (1)	Mangrove ecosystems	Sentinel-2 (10–20–60 m)	No	[476]	[366]	[123]
Vegetation cover (1)	Mangrove ecosystems	WorldView-2 (~0.5–2 m)	No	-	-	[123]
Vegetation cover (1)	Mangrove ecosystems	UAV	No	-	-	[123]
Vegetation cover (1)	Microphytobenthos	Sentinel-2 (10–20–60 m)	No	-	[208]	-
Vegetation cover (1)	Morphological evolution	Landsat (15–30 m)	No	-	[285]	-
Vegetation cover (1)	Morphological evolution	Sentinel-2 (10–20–60 m)	No	-	[553]	-
Vegetation cover (1)	Morphological evolution	UAV-MSI	No	-	[285]	-
Vegetation cover (1)	Primary production	Landsat (15–30 m)	No	[139]	-	-
Vegetation cover (1)	Primary production	MODIS (0.5–1 km)	No	[139]	-	-
Vegetation cover (1)	Sea snots	Sentinel-2 (10–20–60 m)	No	-	[190]	-
Vegetation cover (1)	Seaweed aquaculture	HY-1C (50 m)	No	-	[80]	-
Vegetation cover (1)	Seaweed aquaculture	Sentinel-1 (~10 m)	No	-	[223]	-
Vegetation cover (1)	Seaweed aquaculture	Sentinel-2 (10–20–60 m)	No	-	[80]	-
Vegetation cover (1)	Soil salinization	Landsat (15–30 m)	No	[459,556]	[156,458]	[155]
Vegetation cover (1)	Soil salinization	MODIS (0.5–1 km)	No	[459]	-	-
Vegetation cover (1)	Soil salinization	Sentinel-2 (10–20–60 m)	No	-	-	[125]
Vegetation cover (1)	Soil salinization	SOC710VP portable	No	-	-	[125]
Vegetation cover (1)	Soil salinization	UAV	No	-	-	[125]
Vegetation cover (1)	Tidal flat	Sentinel-2 (10–20–60 m)	No	-	[453]	[554]
Vegetation cover (1)	Urban sprawl	Hyperion (30 m) (2)	No	[115]	-	-
Vegetation cover (1)	Urban sprawl	Landsat (15–30 m)	No	[461,462]	[463,464,465]	[392]
Vegetation cover (1)	Urban sprawl	MIVIS airborne (2)	No	[466]	-	-
Vegetation cover (1)	Urban sprawl	MODIS (0.5–1 km)	No	[113]	-	-
Vegetation cover (1)	Urban sprawl	PRISMA (30 m) (2)	No	[115]	-	-
Vegetation cover (1)	Water hyacinth	Sentinel-2 (10–20–60 m)	No	-	[160]	-
Vegetation cover (1)	Water quality estimation	Sentinel-2 (10–20–60 m)	No	-	[160]	-
Vegetation structure (1)	Mangrove ecosystems	G-LiHT airborne image (2)	No	-	-	[162]

⁽¹⁾ This parameter was mapped and monitored together with other parameters; ⁽²⁾ hyperspectral data.

. The authors analyzed 130 remote data and 3 available products. Among the remote data, 103, 7, 19, and 1 were acquired from satellite, airborne, UAV, and fixed platforms, respectively. Among these data, 8 were obtained from active sensors (UAV LIDAR sensors were the first to be employed, followed by Sentinel-1 sensors), whereas 124 were acquired from passive sensors. Among the remote data, 118 were acquired from multispectral sensors (Landsat sensors were the first to be employed, followed by Sentinel-2 and UAV sensors) and 4 were acquired from hyperspectral sensors (airborne, Hyperion, and PRISMA sensors).

Most of these papers (73%) monitored vegetation cover over time (e.g., [160]), whereas about half of the papers (60%) compared different methods (e.g., [161]) and 42% retrieved this parameter from several remote data (e.g., [123]). Moreover, three papers combined remote data with models (e.g., [162]) or GIS (e.g., [163]).

3.3.36. Vegetation Species

The characteristics of the 19 eligible papers whose authors mapped vegetation species are summarized in

Table A36. The eligible papers that mapped and/or monitored vegetation species.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Coastal vegetation (1)	Coastal vulnerability assessment	Sentinel-2 (10–20–60 m)	No	-	-	[166]
Coastal vegetation (1)	Habitat	GaoFen2 (0.8–3.2 m)	No	-	-	[559]
Coastal vegetation (1)	Habitat	Google Earth image	No	-	-	[164]
Coastal vegetation (1)	Habitat	Landsat (15–30 m)	No	-	-	[424,559]
Coastal vegetation (1)	Habitat	RapidEye (5 m)	No	-	-	[424]
Coastal vegetation (1)	Wetland Biodiversity Estimation	ZiYuan1-02D-HIS (30 m) (2)	No	-	[467]	-
Coastal vegetation (1)	Wetland Biodiversity Estimation	ZiYuan1-02D-MSI (10 m)	No	-	[467]	-
Invasive species (1)	Coastal vulnerability assessment	Landsat (15–30 m)	No	[55]	-	[413]
Invasive species (1)	Invasive alien species	Landsat (15–30 m)	No	-	-	[520]
Invasive species (1)	Invasive alien species	Sentinel-2 (10–20–60 m)	No	-	-	[473,520]
Invasive species (1)	Invasive alien species	UAV-MSI	No	[161]	-	-
Invasive species (1)	Invasive alien species	UAV	No	[161]	-	-
Invasive species (1)	LU/LC change	Google Earth image	No	-	-	[437]
Invasive species (1)	LU/LC change	Landsat (15–30 m)	No	-	-	[437]
Invasive species (1)	Mangrove ecosystems	ALOS-2	No	-	[365]	-
Invasive species (1)	Mangrove ecosystems	Landsat (15–30 m)	No	-	[365]	-
Invasive species (1)	Mangrove ecosystems	SPOT (~10–20 m)	No	-	[365]	-
Invasive species (1)	Tidal flat	Sentinel-2 (10–20–60 m)	No	-	[453]	-
Riparian species (1)	Invasive alien species	Landsat (15–30 m)	No	-	[432]	-
Salt marsh species (1)	Habitat	Sentinel-1 (~10 m)	No	-	-	[560]
Salt marsh species (1)	Habitat	Sentinel-2 (10–20–60 m)	No	-	-	[428,560]
Salt marsh species (1)	Habitat	UAV-Lidar	No	-	[142]	-
Salt marsh species (1)	Habitat	UAV-MSI	No	-	[142]	-
Wetland species (1)	Habitat	Sentinel-1 (~10 m)	No	[165,215]	-	-
Wetland species (1)	Habitat	Sentinel-2 (10–20–60 m)	No	[215]	-	-
Wetland species (1)	Habitat	UAV-MSI	No	-	-	[279,355]

⁽¹⁾ This parameter was mapped and monitored together with other parameters; ⁽²⁾ hyperspectral data.

. The authors analyzed 30 remote data and did not use available products. Among the remote data, 24, 1, and 5 were acquired from satellite, airborne, and fixed platforms, respectively. Among these data, 5 were obtained from active sensors (ALOS-2, Sentinel-1, and UAV sensors), whereas 25 were acquired from passive sensors. Among the remote data, 24 images were acquired from multispectral sensors (Landsat and Sentinel-2 sensors were the first to be employed) and 1 image was acquired from a hyperspectral sensor (ZiYuan1-02D-HIS).

Most of these papers (79%) monitored vegetation species over time (e.g., [164]), whereas about half of the papers (58%) compared different methods or products to obtain results with the lowest error (e.g., [165]) and 53% retrieved this parameter from several remote data (e.g., [161]). Moreover, one paper combined GIS and remote sensing techniques [166].

3.3.37. Water Turbidity

The characteristics of the 17 eligible papers whose authors mapped water turbidity [48] are summarized in

Table A37. The eligible papers that mapped and/or monitored the water turbidity.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Water turbidity (1)	Atmospheric correction	Sentinel-3 (300 m)	No	-	-	[53]
Water turbidity (1)	Anthropogenic activities	Landsat (15–30 m)	No	-	[167]	-
Water turbidity (1)	Biotoxin risk	-	Yes	-	-	[296]
Water turbidity (1)	Coastal vulnerability assessment	Landsat (15–30 m)	No	-	-	[412]
Water turbidity (1)	Effects of COVID-19 lockdown	Sentinel-3 (300 m)	No	-	[169]	-
Water turbidity (1)	Harmful algal bloom	MODIS (0.5–1 km)	No	-	-	[76]
Water turbidity (1)	Harmful algal risk	-	Yes	-	-	[296]
Water turbidity (1)	Marine aquaculture	-	Yes	-	[514]	-
Water turbidity (1)	Microbenthic invertebrate distribution	-	Yes	-	[306]	-
Water turbidity (1)	Water quality estimation	-	Yes	-	[306]	-
Water turbidity (1)	Water quality estimation	Landsat (15–30 m)	No	-	[323,324]	-
Water turbidity (1)	Water quality estimation	Sentinel-2 (10–20–60 m)	No	-	[323,324,327,328,498]	[168]
Water turbidity (1)	Water quality estimation	Sentinel-3 (300 m)	No	-	-	[168]
Rrs (645) (1)	Sediment plumes	MODIS (0.5–1 km)	No	-	[497]	-

⁽¹⁾ This parameter was mapped and monitored together with other parameters.

. The authors analyzed 15 remote data and 6 available products. All remote data were acquired from multispectral satellite sensors (Sentinel-2 sensors were the first to be employed, followed by Landsat sensors and then Sentinel-3 and MODIS sensors).

Most of these papers (82%) monitored water turbidity over time (e.g., [167]), whereas about half of the papers (76%) compared different methods or different products to obtain results with the lowest error (e.g., [53]) and 47% retrieved this parameter from several remote data (e.g., [168]). Moreover, five papers combined remote sensing techniques with models (e.g., [169]) or GIS techniques (e.g., [76]).

3.3.38. Wave Data

The characteristics of the nine eligible papers whose authors analyzed wave data are summarized in

Table A38. The eligible papers that mapped and/or monitored the wave data.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Mean Significant Wave Height (1)	Coastal vulnerability assessment	INCOIS Wave Rider	Yes	-	[275]	-
Wave (1)	Coastal vulnerability assessment	-	Yes	[91]	-	-
Wave (1)	Coastline change	-	Yes	[546]	[346,531,537]	[171]
Wave (1)	Coastline change	Radar fix positions	No	-	[270]	-
Wave (1)	Effects of extreme events	-	Yes	-	-	[170]
Wave (1)	Wave heights	Jason	Yes	-	[561]	[562]

⁽¹⁾ This parameter was mapped and monitored together with other parameters.

. The authors of these nine papers analyzed one remote data and eight available products. The remote data was acquired from an active sensor that was installed on a fixed platform.

Most of the papers (89%) monitored wave data over time (e.g., [91]), and four papers combined remote sensing techniques with models (e.g., [170]) or GIS techniques (e.g., [171]).

3.3.39. Wind Data

The characteristics of the 39 eligible papers whose authors analyzed wind data are summarized in

Table A39. The eligible papers that mapped and/or monitored the wind data.

Parameter	Phenomena	Remote Data or Dataset	Available Products	References 2023	References 2022	References 2021
Wind (1)	Biotoxin risk	-	Yes	-	-	[296]
Wind (1)	Blue ice	-	Yes	-	[385]	-
Wind (1)	Coastline change	-	Yes		[537]	[332]
Wind (1)	Coastal vulnerability assessment	-	Yes	-	-	[102,412]
Wind (1)	Coastal vulnerability assessment	Marine X-band radar (MR) system	Yes	-	[333]	-
Wind	Effects of extreme events	-	Yes	[59,153]	-	[170]
Wind (1)	Green tide	-	Yes	-	-	[200]
Wind (1)	Habitat	-	Yes	-	[552]	-
Wind (1)	Methane plume	-	Yes	-	[130]	-
Wind (1)	Morphological Evolution	-	Yes	[283]	-	-
Wind (1)	SST front	-	Yes	-	-	[522,523]
Wind (1)	Harmful algal bloom	WindSat satellite	Yes	-	[305]	-
Wind (1)	Harmful algal risk	-	Yes	-	-	[296]
Wind (1)	Marine aquaculture	-	Yes	-	[514]	[75]
Wind (1)	Marine heatwaves	-	Yes	-	[521]
Wind (1)	Oil spill	-	Yes	-	[335]	[92]
Wind (1)	Phytoplankton	-	Yes	-	[318]	-
Wind (1)	Red tide bloom	Global Navigation Satellite System Reflectometry (GNSS-R)	Yes	-	[188]	-
Wind (1)	Sea ice	-	Yes	-	[386]	[111]
Wind (1)	Sea level anomaly	-	Yes	-	-	[507]
Wind (1)	Sea snots	-	Yes	-	[190]	-
Wind (1)	SST front	ERA	Yes	-	-	[523]
Wind (1)	Suspended sediments	-	Yes	-	[336]	-
Wind (1)	Water quality estimation	-	Yes	-	-	[322]
Wind (1)	Wave heights	Jason	Yes	-	[561]	[562]
Wind	Wind	Global Navigation Satellite System Reflectometry (GNSS-R)	Yes	-	-	[563]
Wind	Wind	-	Yes		[173]	[172]
Wind	Wind	Sentinel-1 (~10 m)	No	-	[173]	[172]
Wind (1)	Wind	Sentinel-1 (~10 m)	No	-	[338]

⁽¹⁾ This parameter was mapped and monitored together with other parameters.

. The authors of these 39 papers analyzed 4 remote data and 37 available products. All these data were obtained from active sensors. Among the remote data, 3, and 1 were acquired from satellite (Sentinel-1) and fixed platforms, respectively.

Most of the papers (79%) monitored wind data over time (e.g., [153]), whereas 41% retrieved this parameter from several remote data (e.g., [172]) and 38% compared different methods or different products to minimize errors (e.g., [173]). Moreover, 13 papers combined remote sensing techniques with models (e.g., [59]) or GIS techniques (e.g., [102]).

3.4. Coastal Phenomena Analized

The analysis of the papers highlighted that the authors mapped or monitored 39 parameters to study 103 coastal phenomena, which are listed in the second column of Table A1, Table A2, Table A3, Table A4, Table A5, Table A6, Table A7, Table A8, Table A9, Table A10, Table A11, Table A12, Table A13, Table A14, Table A15, Table A16, Table A17, Table A18, Table A19, Table A20, Table A21, Table A22, Table A23, Table A24, Table A25, Table A26, Table A27, Table A28, Table A29, Table A30, Table A31, Table A32, Table A33, Table A34, Table A35, Table A36, Table A37, Table A38 and Table A39. It is important to specify that all the “purposes” of eligible papers are grouped generically with the name “phenomenon”, but not all of them can be called as such (e.g., atmospheric correction). As mentioned above, since 1971, the coastal zone has been mapped and monitored using remote data. Therefore, to identify coastal phenomena and parameters to be analyzed, researchers have considered both the state of the art and outstanding research challenges. Moreover, because most of the eligible papers (89%) were funded by international institutions and local governments, the choice of the phenomena analyzed was made not only by the scientific community but also by the policy-making community. Therefore, most of the phenomena affecting coastal areas were taken into account by the eligible papers. In Figure 2, the red cells show each parameter that was mapped or monitored (columns) to study each phenomenon (rows). Table A1, Table A2, Table A3, Table A4, Table A5, Table A6, Table A7, Table A8, Table A9, Table A10, Table A11, Table A12, Table A13, Table A14, Table A15, Table A16, Table A17, Table A18, Table A19, Table A20, Table A21, Table A22, Table A23, Table A24, Table A25, Table A26, Table A27, Table A28, Table A29, Table A30, Table A31, Table A32, Table A33, Table A34, Table A35, Table A36, Table A37, Table A38 and Table A39 and Figure 2 lists the names assigned by the authors to the phenomena analyzed. The phenomena that were mapped or monitored using the same parameters are grouped together. The 47 phenomena or groups of phenomena that were derived are shown in Figure 2.

The analysis of parameters according to the number of phenomena showed that the most mapped parameters were LU/LC, SST, bathymetry, and seabed, whereas the least mapped parameters were phycocyanin, soil salinization, and methane/oil (Figure 3).

On the other hand, the analysis of phenomena according to the number of parameters showed that 36 phenomena were examined by mapping or monitoring more than 10 parameters (Figure 4).

It is very interesting to note that phenomena that mainly affect the coastal land were also analyzed by mapping or monitoring parameters of coastal water. LU/LC change and urban sprawl were also analyzed using algae or macroalgae, Chl-a, PP, SLA, or SLR maps, and water quality was also estimated using LU/LC and vegetation cover maps. Because the number of phenomena analyzed by the authors of eligible papers is very large, analysis of the number of phenomena according to the number of authors who examined them is of little value. However, the most studied phenomenon was coastline change and coastal erosion (15.9%), followed by the group including bathymetry, bottom friction coefficients, and seabed classification (8.6%) and then two other groups (8.2% and 7.3%, respectively): (i) habitat and wetland biodiversity estimation and (ii) LU/LC change and urban sprawl.

3.5. Validation of Retrieved Products

Analysis of the papers revealed another important issue: most authors (91%) validated the parameters that were retrieved from remote data. The term validation is defined as “the process of assessing, by independent means, the quality of the data products derived from the system outputs” by the Working Group on Calibration and Validation of the Committee on Earth Observing Satellites [174]. “Ground truth” or “reference data”, which are provided by independent means, are usually compared with “data products” to assess their “degree of correctness” or accuracy [175].

Therefore, only a few did not validate the parameters that were retrieved from remote data; for example, Barreto et al. [176] exploited a UAV system to monitor the marine megafauna, Bera et al. [177] analyzed socioeconomic vulnerability of Sagar Island (India) and linked it with land loss, and Shimada et al. [178] studied the parameters that determine the habitat of two important green turtle nests in the Red Sea.

4. Discussions

Coastal areas are the most valuable on Earth but also the most vulnerable because of many phenomena or processes looming over them [9]. To date, the main phenomena looming over them are climate change and coastal urban sprawl, which are closely interrelated and trigger many others [7,8]. Therefore, mapping and monitoring coastal phenomena and parameters are the most pressing requirements for ensuring sustainability of these valuable and vulnerable areas [9]. However, coastal areas are characterized by small-scale mosaics of different habitats and are mainly affected by small-scale natural or man-made phenomena, while coastal waters are characterized by high spatial and temporal variability of biochemical and physical parameters [48].

Remote sensing has taken up the challenge of characterizing these differences since 1971 and, to date, has become an indispensable tool for mapping and monitoring some phenomena (e.g., seal level rise and sea surface temperature [12,13]). As many updated reviews have provided a limited overview (e.g., [37,41]), this systematic review provides a comprehensive overview of data, methods, and/or remote approaches that map and monitor coastal zone phenomena and parameters more effectively. For this purpose, 502 eligible papers, which consisted of the most cited papers published from January 2021 to June 2023, were identified, screened, and carefully studied.

The analysis of 502 eligible papers highlighted that 103 phenomena were analyzed using 39 parameters. Therefore, most of the phenomena and parameters of coastal areas were mapped or monitored by the eligible papers. This wide variety of phenomena and parameters is strongly related to the key role of the coastal zone in social, economic, and environmental systems and the key role of the remote sensing technique to know, map, and monitor coastal phenomena and parameters. The eligible papers demonstrated that these key roles are clear not only to the scientific community, which published 15,141 papers in 2.5 years, but also to the policy-making community, which founded 89% of the eligible papers.

The phenomena most analyzed by the authors of the eligible papers were changes in coastline and land use and cover, climate change, coastal erosion, and coastal urban sprawl. However, it is very interesting to note that the phenomena analyzed covered multiple and diverse issues: some phenomena were general phenomena (e.g., anthropogenic activities, climate change, coastal erosion, coastal vulnerability assessment, urban sprawl, and water quality estimation); some were very specific (e.g., aboveground biomass, biotoxin risk, blue ice, hydraulic engineering, and leaf area index monitoring); some were more pertinent to coastal land (e.g., land surface temperature, land use and land cover changes, soil salinization, and urban sprawl), coastal waters (e.g., coral reef, depth of Secchi disk, sea surface salinity, and sea surface temperature), and intertidal zone (e.g., coastline changes, mangrove ecosystems, and tidal flat); and some addressed pollution (e.g., marine litter, methane plume, oil spill, and plastic litter) or the socioeconomic aspect of the coastal zone (e.g., coastal aquaculture pond, ecosystem services value, fishing zones, and marine aquaculture). Although there was variability in the phenomena analyzed, most of the phenomena were analyzed using the same parameters.

In order to more effectively analyze coastal zone phenomena and parameters, the authors validated most of the parameters (91%) that were retrieved or analyzed them by comparing them with reference data in order to assess their degree of correctness [85]. For this purpose, they exploited in situ data, products that were obtained from very high spatial resolution images, or validated products. The authors retrieved from remote data 39 parameters that were mapped or monitored 1158 times in the 502 eligible papers. In other words, the authors combined most of the parameters (88%) together with other parameters in order to analyze coastal phenomena. In addition, phenomena mainly affecting coastal land were analyzed not only by mapping parameters related to coastal land but also using parameters related to coastal waters and vice versa. The authors 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. The authors combined different remote data: they were acquired from active (15% of the remote data) and passive (15% of the remote data) sensors; from satellites (79% of remote data), aircraft (7% of remote data), unmanned aerial vehicles (10% of remote data), and fixed platforms (4% of remote data); and from multispectral (95% of the passive data) and hyperspectral (5% of the passive data) sensors. The authors obtained 48% of the parameters using different methods, and their results were compared with each other and with available products. Moreover, the authors combined 17% of the parameters that were retrieved from remote data with geographic information system and model techniques.

Although this systematic review included 502 eligible papers that were the most cited and up to date, it cannot and does not claim to be totally comprehensive of a very broad topic.

5. Conclusions

This systematic review addressed three important questions, which lack up-to-date and effective answers: (1) Which coastal zone phenomena and parameters can be adequately mapped and monitored using remote data? (2) How have authors addressed the spatial, temporal, and thematic requirements required to more effectively analyze coastal zone phenomena and parameters? (3) What recommendations can be offered to readers to meet spatial, temporal, and thematic requirements?

Regarding the first question, the systematic review demonstrated that most coastal phenomena and parameters, to date, can be mapped and monitored using remote data.

Regarding the second question, 502 eligible papers showed that authors addressed these requirements in six ways and many of them were performed jointly, with authors found to have (i) retrieved parameters from different remote data (69%), (ii) validated the parameters retrieved (91%), (iii) merged different data and parameters (88%), (iv) monitored these different data and parameters over time (75%), (v) compared different methods (48%), and (vi) combined geographic information system, models, and remote sensing techniques (17%). In other words, the authors addressed the spatial, temporal, and thematic requirements needed to more effectively analyze coastal phenomena and parameters using and implementing the integrated approaches.

Therefore, regarding the third question, the systematic review recommends employing and implementing new and creative integrated approaches.