Spatial downscaling of MODIS Chlorophyll-a using Landsat 8 images for complex coastal water monitoring

2018 ◽  
Vol 209 ◽  
pp. 149-159 ◽  
Author(s):  
Yanzhao Fu ◽  
Shiguo Xu ◽  
Chengkun Zhang ◽  
Ya Sun
Keyword(s):  
Chlorophyll A ◽  
Coastal Water ◽  
Water Monitoring ◽  
Landsat 8 ◽  
Spatial Downscaling ◽  
Coastal Water Monitoring

Fusion of MeRIS and ETM images for coastal water monitoring

2007 ◽  
Author(s):  
Audrey Minghelli-Roman ◽  
Laurent Polidori ◽  
Sandrine Mathieu-Blanc ◽  
Lionel Loubersac ◽  
Francois Cauneau
Keyword(s):  
Coastal Water ◽  
Water Monitoring ◽  
Coastal Water Monitoring

scholarly journals Spatial Resolution Improvement by Merging MERIS–ETM Images for Coastal Water Monitoring

2006 ◽  
Vol 3 (2) ◽  
pp. 227-231 ◽  
Author(s):  
A. Minghelli-Roman ◽  
L. Polidori ◽  
S. Mathieu-Blanc ◽  
L. Loubersac ◽  
F. Cauneau
Keyword(s):  
Spatial Resolution ◽  
Coastal Water ◽  
Water Monitoring ◽  
Resolution Improvement ◽  
Coastal Water Monitoring

scholarly journals CHLOROPHYLL ESTIMATION OF LAKE WATER AND COASTAL WATER USING LANDSAT-8 AND SENTINEL-2A SATELLITE

2019 ◽  
Vol XLII-3/W7 ◽  
pp. 77-82 ◽  
Author(s):  
S. Yadav ◽  
Y. Yamashiki ◽  
J. Susaki ◽  
Y. Yamashita ◽  
K. Ishikawa
Keyword(s):  
Chlorophyll A ◽  
Coastal Water ◽  
Lake Water ◽  
Satellite Images ◽  
Lake Biwa ◽  
Measurement Data ◽  
Landsat 8 ◽  
Chlorophyll A Concentration ◽  
Wakasa Bay ◽  
Sentinel 2A

<p><strong>Abstract.</strong> Chlorophyll-a is an optically active compound (OAC) commonly used as a proxy for phytoplankton biomass in an aquatic environment. Retrieving the concentration of chlorophyll-a remains a challenge due to the presence of several OAC particularly in water bodies which are in proximity to the land-based activities. In this study, an effort has been made to estimate the chlorophyll-a concentration of both the freshwater Lake Biwa and the coastal water of Wakasa Bay in Japan. A spectral decomposition algorithm was used to determine the chlorophyll-a using the satellite images. The algorithm was applied to the satellite images from two different sensors namely Landsat-8/OLI and Sentinel-2A/MSI. The satellite-derived chlorophyll-a concentration for the lake and coastal water from two different sensors were compared to assess the performance of both the sensors. The accuracy of the chlorophyll-a results derived from the images was evaluated with the in-situ measurement data of the chlorophyll-a for the Lake Biwa and the coastal water of Wakasa Bay. Both satellite sensors appear to give the best results for the coastal water (R<sup>2</sup>&amp;thinsp;&amp;gt;&amp;thinsp;0.80) with an RMSE &amp;lt;&amp;thinsp;0.3&amp;thinsp;&amp;mu;g/L. However, slight underestimation of chlorophyll-a noted for the Landsat-8 image with an increase in chlorophyll-a concentration. For the lake water, Sentinel-2A results were relatively better (R<sup>2</sup>&amp;thinsp;&amp;gt;&amp;thinsp;0.70) than Landsat-8, with an RMSE of &amp;lt;&amp;thinsp;1.0&amp;thinsp;&amp;mu;g/L. The obtained results will be useful to evaluate the primary productivity of both freshwater and coastal water body.</p>

Remote Sensing in Coastal Water Monitoring: Applications in the Eastern Mediterranean Sea

2016 ◽  
Author(s):  
Manos Dassenakis ◽  
Vasiliki Paraskevopoulou ◽  
Constantinos Cartalis ◽  
Nektaria Adaktilou ◽  
Katerina Katsiabani
Keyword(s):  
Remote Sensing ◽  
Mediterranean Sea ◽  
Coastal Water ◽  
Eastern Mediterranean ◽  
Water Monitoring ◽  
Eastern Mediterranean Sea ◽  
Monitoring Applications ◽  
Coastal Water Monitoring

APLIKASI CITRA LANDSAT 8 OLI UNTUK IDENTIFIKASI STATUS TROFIK WADUK GAJAH MUNGKUR WONOGIRI, JAWA TENGAH

2017 ◽  
Vol 19 (2) ◽  
pp. 113
Author(s):  
Kusuma Wardani Laksitaningrum ◽  
Wirastuti Widyatmanti
Keyword(s):  
Chlorophyll A ◽  
Total Phosphorus ◽  
Trophic State ◽  
Water Clarity ◽  
Coefficient Of Determination ◽  
Physical Factors ◽  
Landsat 8 ◽  
Trophic State Index ◽  
Landsat 8 Oli ◽  
State Index

<p align="center"><strong>ABSTRAK</strong></p><p class="abstrak">Waduk Gajah Mungkur (WGM) adalah bendungan buatan yang memiliki luas genangan maksimum 8800 ha, terletak di Desa Pokoh Kidul, Kecamatan Wonogiri, Kabupaten Wonogiri. Kondisi perairan WGM dipengaruhi oleh faktor klimatologis, fisik, dan aktivitas manusia yang dapat menyumbang nutrisi sehingga mempengaruhi status trofiknya. Tujuan dari penelitian ini adalah mengkaji kemampuan citra Landsat 8 OLI untuk memperoleh parameter-parameter yang digunakan untuk menilai status trofik, menentukan dan memetakan status trofik yang diperoleh dari citra Landsat 8 OLI, dan mengevaluasi hasil pemetaan dan manfaat citra penginderaan jauh untuk identifikasi status trofik WGM. Identifikasi status trofik dilakukan berdasarkan metode <em>Trophic State Index</em> (TSI) Carlson (1997) menggunakan tiga parameter yaitu kejernihan air, total fosfor, dan klorofil-a. Model yang diperoleh berdasar pada rumus empiris dari hasil uji regresi antara pengukuran di lapangan dan nilai piksel di citra Landsat 8 OLI. Model dipilih berdasarkan nilai koefisien determinasi (R<sup>2</sup>) tertinggi. Hasil penelitian merepresentasikan bahwa nilai R<sup>2</sup> kejernihan air sebesar 0,813, total fosfor sebesar 0,268, dan klorofil-a sebesar 0,584. Apabila nilai R<sup>2 </sup>mendekati 1, maka semakin baik model regresi dapat menjelaskan suatu parameter status trofik. Berdasarkan hasil kalkulasi diperoleh distribusi yang terdiri dari kelas eutrofik ringan, eutrofik sedang, dan eutrofik berat yaitu pada rentang nilai indeks 50,051 – 80,180. Distribusi terbesar adalah eutrofik sedang. Hal tersebut menunjukkan tingkat kesuburan perairan yang tinggi dan dapat membahayakan makhluk hidup lain.</p><p><strong>Kata kunci: </strong>Waduk Gajah Mungkur, citra Landsat 8 OLI, regresi, TSI, status trofik</p><p class="judulABS"><strong>ABSTRACT</strong></p><p class="Abstrakeng">Gajah Mungkur Reservoir is an artificial dam that has a maximum inundated areas of 8800 ha, located in Pokoh Kidul Village, Wonogiri Regency. The reservoir’s water conditions are affected by climatological and physical factors, as well as human activities that can contribute to nutrients that affect its trophic state. This study aimed to assess the Landsat 8 OLI capabilities to obtain parameters that are used to determine its trophic state, identifying and mapping the trophic state based on parameters derived from Landsat 8 OLI, and evaluating the results of the mapping and the benefits of remote sensing imagery for identification of its trophic state. Identification of trophic state is based on Trophic State Index (TSI) Carlson (1997), which uses three parameters there are water clarity, total phosphorus, and chlorophyll-a. The model is based on an empirical formula of regression between measurements in the field and the pixel values in Landsat 8 OLI. Model is selected on the highest value towards coefficient of determination (R<sup>2</sup>). The results represented that R<sup>2</sup> of water clarity is 0.813, total phosphorus is 0.268, and chlorophyll-a is 0.584. If R<sup>2</sup> close to 1, regression model will describe the parameters of the trophic state better. Based on the calculation the distribution consists of mild eutrophic, moderate eutrophic, and heavy eutrophic that has index values from 50.051 to 80.18. The most distribution is moderate eutrophication, and it showed the high level of trophic state and may harm other living beings.</p><p><strong><em>Keywords: </em></strong><em>Gajah Mungkur Reservoir, </em><em>L</em><em>andsat 8 OLI satellite imagery, regression, TSI, trophic state</em></p>

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