Evaluation of Sentinel-2 and Landsat 8 Images for Estimating Chlorophyll-a Concentrations in Lake Chad, Africa

Buma, Willibroad Gabila; S, Lee

doi:10.3390/rs12152437

Cited by 76 publications

(48 citation statements)

References 64 publications

(56 reference statements)

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“…The spectral signature of Chl-a is characterized by strong absorption in the blue (443 nm) and red wavelengths (near 675 nm), and by high reflectance in the green (550-555 nm) and red-edge spectrum regions (685-710 nm) [42][43][44][45]. Existing studies have shown that the most suitable spectral range for monitoring suspended solids in water is 700-800nm [46].…”

Section: Spectral Analysismentioning

confidence: 99%

Retrieval of Water Quality from UAV-Borne Hyperspectral Imagery: A Comparative Study of Machine Learning Algorithms

Wei

et al. 2021

Remote Sensing

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The rapidly increasing world population and human activities accelerate the crisis of the limited freshwater resources. Water quality must be monitored for the sustainability of freshwater resources. Unmanned aerial vehicle (UAV)-borne hyperspectral data can capture fine features of water bodies, which have been widely used for monitoring water quality. In this study, nine machine learning algorithms are systematically evaluated for the inversion of water quality parameters including chlorophyll-a (Chl-a) and suspended solids (SS) with UAV-borne hyperspectral data. In comparing the experimental results of the machine learning model on the water quality parameters, we can observe that the prediction performance of the Catboost regression (CBR) model is the best. However, the prediction performances of the Multi-layer Perceptron regression (MLPR) and Elastic net (EN) models are very unsatisfactory, indicating that the MLPR and EN models are not suitable for the inversion of water quality parameters. In addition, the water quality distribution map is generated, which can be used to identify polluted areas of water bodies.

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Section: Spectral Analysismentioning

confidence: 99%

Retrieval of Water Quality from UAV-Borne Hyperspectral Imagery: A Comparative Study of Machine Learning Algorithms

Wei

et al. 2021

Remote Sensing

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“…Sai số được tính toán dựa trên độ chênh lệch bình phương giữa giá trị thực tế (của các điểm đối chiếu) và 271 giá trị mô phỏng (trong mô hình hồi quy) theo công 272 thức (6). (7).…”

Section: U N C O R R E C T I O N P R O O Funclassified

“…nở hoa giữa ảnh radar và ảnh viễn thám vệ tinh5 ; nghiên cứu so sánh tính hiệu quả của các thuật toán của các ảnh vệ tinh WorldView, Sentinel 2, Landsat 8 và MERIS/OLCI6 hay nghiên cứu so sánh hiệu quả thuật toán 2BDA, 3BDA và NDCI giữa ảnh Landsat 8 và Sentinel 2…7 Các kết quả nghiên cứu chỉ ra rằng, Có hai mùa rõ rệt, mùa khô từ tháng12 đến hết tháng 04, mùa mưa từ tháng 05 đến hết tháng 11. Lượng mưa trung bình năm là 1.800-2.200 mm.…”

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Monitoring techniques for algae blooms from space

Hoang

Xuan

Thi

2021

Sci. Tech. Dev. J. - Nat. Sci.

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Microalgae blooms are one of the most serious environmental problems that greatly affect the living environment of aquatic organisms. They deplete the oxygen source in the water, seriously depleting the respiratory oxygen source for organisms living in the water body. Some species of microalgae are also capable of producing toxins, typically blue-green algae, poisoning aquatic organisms and causing death, health effects on human life when ingested. This paper presents the results of research on mapping algae concentration distribution to support water quality assessment for Dau Tieng reservoir. Landsat satellite imagery was used for qualitative and quantitative assessment of algae presence in the lake area. Calculation results showed that algae concentration had a linear correlation with the band ratio of the 3-band algorithm (3BDA (2), and was a combination of spectral reflective bands in the green wavelength bands GREEN, RED and near infrared NIR. We simulated the spatial distribution of algae concentrations on the entire lake area on 3 points of time when satellite images had been collected. Result of this study showed that remote sensing technology can be used for monitoring the appearance and change of algae concentration in water, which is an essential measurement for timely providing warnings as well as assisting in monitoring and managing water environment quality to minimize damages to people and the ecosystem.

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“…The ESA's aim is that several Sentinel satellites [6] will act as Europe's eyes on Earth. At present, the following satellites are in orbit: Sentinel-1 (S1), which has a radar sensor installed, the Sentinel-2 (S2) satellite pair (Sentinel-2A and Sentinel-2B), which have optical sensors to study and monitor agriculture [7,8], and Sentinel-3, which has visual (VI) and near-infrared (NIR) sensors, for monitoring marine environments.…”

Section: Introductionmentioning

confidence: 99%

Sentinel-2 Images and Machine Learning as Tool for Monitoring of the Common Agricultural Policy: Calasparra Rice as a Case Study

López-Andreu¹,

Erena²,

Domínguez-Gómez³

et al. 2021

Agronomy

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The European Commission introduces the Control by Monitoring through new technologies to manage Common Agricultural Policy funds through the Regulation 2018/746. The advances in remote sensing have been considered one of these new technologies, mainly since the European Space Agency designed the Copernicus Programme. The Sentinel-1 (radar range) and Sentinel-2 (optical range) satellites have been designed for monitoring agricultural problems based on the characteristics they provide. The data provided by the Sentinel 2 missions, together with the emergence of different scientific disciplines in artificial intelligence —especially machine learning— offer the perfect basis for identifying and classifying any crop and its phenological state. Our research is based on developing and evaluating a pixel-based supervised classification scheme to produce accurate rice crop mapping in a smallholder agricultural zone in Calasparra, Murcia, Spain. Several models are considered to obtain the most suitable model for each element of the time series used; pixel-based classification is performed and finished with a statistical treatment. The highly accurate results obtained, especially across the most significant vegetative development dates, indicate the benefits of using Sentinel-2 data combined with Machine Learning techniques to identify rice crops. It should be noted that it was possible to locate rice crop areas with an overall accuracy of 94% and standard deviation of 1%, which could be increased to 96% (±1%) if we focus on the months of the crop’s highest development state. Thanks to the proposed methodology, the on-site inspections carried out, 5% of the files, have been replaced by remote sensing evaluations of 100% of the analyzed season files. Besides, by adjusting the model input data, it is possible to detect unproductive or abandoned plots.

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Evaluation of Sentinel-2 and Landsat 8 Images for Estimating Chlorophyll-a Concentrations in Lake Chad, Africa

Cited by 76 publications

References 64 publications

Retrieval of Water Quality from UAV-Borne Hyperspectral Imagery: A Comparative Study of Machine Learning Algorithms

Retrieval of Water Quality from UAV-Borne Hyperspectral Imagery: A Comparative Study of Machine Learning Algorithms

Monitoring techniques for algae blooms from space

Sentinel-2 Images and Machine Learning as Tool for Monitoring of the Common Agricultural Policy: Calasparra Rice as a Case Study

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