Spatiotemporal Variability of Chlorophyll-a and Sea Surface Temperature, and Their Relationship with Bathymetry over the Coasts of UAE

Hussein, Khalid; Abdouli, Khameis Al; Ghebreyesus, Dawit; Petchprayoon, Pakorn; Hosani, Naeema Al; Sharif, Hatim O.

doi:10.3390/rs13132447

Cited by 9 publications

(6 citation statements)

References 45 publications

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“…Produk konsentrasi klorofil-a aqua-MODIS Level 3 dihitung dengan algoritma MODIS OC3M (O'reilly et al, 1998). Algoritma OC3M merupakan fungsi polinomial yang menghubungkan reflektansi penginderaan jauh pada panjang gelombang 443, 488, dan 547 nm terhadap konsentrasi klorofil-a (Hussein et al, 2021).…”

Section: Sentinel-2 216unclassified

“…Produk konsentrasi 320 klorofil-a aqua-MODIS Level 3 dihitung dengan algoritma MODIS OC3M (O'reilly et al, 1998). 321 Algoritma OC3M merupakan fungsi polinomial yang menghubungkan reflektansi penginderaan jauh 322 pada panjang gelombang 443, 488, dan 547 nm terhadap konsentrasi klorofil-a (Hussein et al, 2021). 323 Spasio-temporal konsentrasi klorofil-a di wilayah perairan Tanjung Kelayang pada periode 324 perekaman tahun 2020 disajikan pada Gambar 7.…”

Section: Sentinel-2 216unclassified

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Pemetaan cepat batimetri perairan dangkal menggunakan citra Sentinel-2 dan Google Earth Engine di Perairan Tanjung Kelayang – Pulau Belitung

Munawaroh,

Wicaksono,

Rudiastuti

2023

MGI

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Abstrak Peta batimetri perairan dangkal dapat membantu pengelolaan, pemantauan, dan perlindungan bagi ekosistem di dalamnya. Namun, ketersediaan peta batimetri di wilayah perairan dangkal di Indonesia masih terbatas. Untuk itu, diperlukan sebuah metode pemetaan cepat estimasi batimetri di perairan dangkal di Indonesia. Penelitian ini mencoba mengaplikasikan metode otomatisasi pemetaan batimetri perairan dangkal di Tanjung Kelayang menggunakan band biru dan hijau dari mosaik clean-coastal-water citra Sentinel-2, klorofil-a dari aqua-MODIS pada platform Google Earth Engine (GEE). Tujuan dari penelitian ini adalah menguji keandalan metode pemetaan cepat batimetri dengan menggunakan data mosaik citra satelit Sentinel-2, klorofil-a dan algoritma band-ratio pada platform GEE di wilayah penelitian. Hasil penelitian menunjukkan model SDB (Satellite Derived Bathymetry) memiliki R2 sebesar 0,62, mean absolute erorr (MAE) sebesar 1,77 meter dan RMSE sebesar 2,02 meter dengan estimasi nilai kedalaman optimum 5 meter dan maksimum 10 mter. Dengan demikian, model SDB yang dihasilkan dapat diandalkan sebagai alternatif untuk pemetaan cepat batimetri di perairan dangkal, meskipun harus berkompromi dengan akurasi peta batimetri yang dihasilkan. Abstract Bathymetric maps of shallow waters can aid in managing, monitoring, and protecting ecosystems within them. However, the availability of bathymetric maps for shallow water regions in Indonesia still needs to be improved. Therefore, a method of rapid mapping for bathymetry in shallow waters in Indonesia is needed. This study attempts to apply an automated method for mapping shallow water bathymetry in Tanjung Kelayang using the blue and green bands of the clean-coastal-water mosaic of Sentinel-2 images, chlorophyll-a data from aqua-MODIS, and the Google Earth Engine (GEE) platform. This research aims to test the reliability of the rapid bathymetry mapping method using Sentinel-2 image mosaics, chlorophyll-a data, and band-ratio algorithms on the GEE platform in the study area. The results of the study show that the Satellite Derived Bathymetry (SDB) model has an R2 value of 0,62, a mean absolute error (MAE) of 1,77 meters, and a root mean square error (RMSE) of 2,02 meters, with an estimated optimal depth value of 5 meters and a maximum depth value of 10 meters. Thus, the generated SDB model can be considered a reliable alternative for rapid bathymetry mapping in shallow waters, although it may compromise the accuracy of the resulting bathymetric maps.

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Section: Sentinel-2 216unclassified

Pemetaan cepat batimetri perairan dangkal menggunakan citra Sentinel-2 dan Google Earth Engine di Perairan Tanjung Kelayang – Pulau Belitung

Munawaroh,

Wicaksono,

Rudiastuti

2023

MGI

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“…Mohamed et al [12] also studied the change in SST in the Barents Sea using the EOF method, and concluded that the Barents Sea has the greatest changes in SST in the Arctic. Hussein et al [13] studied the correlations between chlorophyll-a and SST, and their trends in the Arabian Gulf. Zhao et al [14] analyzed the spatial and temporal characteristics of SST in the North Pacific Ocean and the correlations between SST and other sea factors.…”

Section: Introductionmentioning

confidence: 99%

On Evaluating the Predictability of Sea Surface Temperature Using Entropy

et al. 2023

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Sea surface temperature (SST) has important impacts on the global ecology, and having a good understanding of the predictability, i.e., the possibility of achieving accurate prediction, of SST can help us monitor the marine environment and climate change, and guide the selection and design of SST prediction methods. However, existing studies for analyzing SST mostly measure the rising or falling trends of SST. To address this issue, we introduce a temporal-correlated entropy to quantify the predictability of SST series from both global coarse-grained and local fine-grained aspects, and make SST prediction with multiple deep learning models to prove the effectiveness of such predictability evaluation method. In addition, we explore the dynamics of SST predictability by dividing the time range of interest into consecutive time periods, evaluating the corresponding predictability of SST for each time period, and analyzing the stability of the predictability of SST over time. According to the experiments, the SST predictability values near the poles and equator are really high. The average SST predictability values of the East China Sea, Bohai Sea, and Antarctic Ocean are 0.719, 0.706, and 0.886, respectively, and the size relationship of the SST predictability in the three local sea areas is consistent with our prediction results using multiple representative SST prediction methods, which corroborates the reliability of the predictability evaluation method. In addition, we found that the SST predictability in the Antarctic Ocean changes more dramatically over time than in the East China Sea and the Bohai Sea. The results of SST predictability and its dynamic analysis indicate that global warming, ocean currents, and human activities all have significant impacts on the predictability of SST.

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“…Besides the above-mentioned applications in ocean observation, this Special Issue presents various advanced ocean remote sensing technologies and their applications, including the use of artificial intelligence (AI) technology to explore ocean information [61][62][63][64] and reconstruct missing values [65,66]. The applications of ocean remote sensing detailed in this Special Issue include methods for the observation of changes in the ocean environment [67][68][69][70][71][72][73][74][75][76][77] and fishing ground [78], as well as the dynamics of the ocean, such as internal tides [79], internal waves [80,81], eddies and wakes [82,83], upwelling [84,85], ocean current [86][87][88][89] and even bibliometric analysis applied to oil detection and mapping [90]. A brief overview of the articles collected in this Special Issue is given below.…”

mentioning

confidence: 99%

“…The Chl-a and SST products from MODIS Aqua were used to study the events of harmful algae bloom (HAB) in the Arabian Gulf [71]. The results of the study show that the highest Chl-a concentration was in the Strait of Hormuz, with an average of 2.8 mg m −3 , which was 1.1 mg m −3 higher than the average of the entire study area.…”

mentioning

confidence: 99%