A Review of Remote Sensing for Water Quality Retrieval: Progress and Challenges

Yang, Haibo; Kong, Jialin; Hu, Huihui; Du, Yao; Gao, Meiyan; Chen, Fei

doi:10.3390/rs14081770

Cited by 134 publications

(115 citation statements)

References 140 publications

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“…Polu-empirijska metoda razmatra optičke karakteristike parametara kakvoće vode i ima bolju prenosivost od empirijske metode. Ovi modeli se koriste većinom za mjerenje vodenih konstituenata kao što su: jasnoće vode, klorofila, cijanobakterija, suspendiranih tvari, CDOM (obojena otopljena organska tvar) (Yang, 2022. ).…”

Section: Modelunclassified

Evaluacija kakvoće mora luka metodom daljinskog istraživanja

Matetić

Luttenberger

2022

Politehnika (Online)

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Luke imaju velik gospodarski značaj, ali također predstavljaju značajan izvor onečišćenja, bilo od brodskog tereta ili otpada koji nastaje na brodu. Koncept zelene luke primjenjuje se kao nova paradigma koja nastoji uskladiti lučke aktivnosti s okolišnim i socijalnim pitanjima bez ugrožavanja gospodarskog rasta, čime postaje sinonim za održivu luku. Uz nužnost uvođenja redovitog uzorkovanja mora u luci, potrebno je koristiti i nove dostupne tehnologije kao što je daljinsko istraživanje s ciljem unaprjeđenja nadzora nad kvalitetom vode i efikasnije kontrole onečišćenja morskih prostora, čime se pospješuje održivost luka. U radu se daje prikaz dostupnih aktualnih alata za analizu kvalitete mora te se analiziraju mogućnosti daljinskog istraživanja stanja kvalitete morskog okoliša na primjeru područja luke Rijeka, najveće luke u Republici Hrvatskoj.

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Section: Modelunclassified

Evaluacija kakvoće mora luka metodom daljinskog istraživanja

Matetić

Luttenberger

2022

Politehnika (Online)

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“…The MODIS and OLCI images used in this study can provide the data source for monitoring PC concentration in water; however, there is still a lack of an ideal atmospheric correction algorithm for inland water (Miao et al, 2018;Yang et al, 2022). In this study, various atmospheric correction algorithms have been attempted to correct the water images.…”

Section: Construction Of Pc Concentration Retrieval Models For Olci I...mentioning

confidence: 99%

“…Furthermore, for MODIS images, there is currently no ideal atmospheric correction algorithm to obtain high-precision Rrs values for inland water bodies (Zhou et al, 2009;Vanhellemont and Ruddic, 2018;Yang et al, 2022). The MOD09GA reflectance product used in our paper includes the reflected radiation from the water surface, and the accuracy of Rrs values is not high, resulting in the lower PC concentration retrieval in water based on the MODIS image, which caused certain errors were expected to arise in the retrieved PC concentrations from MODIS and OLCI images acquired on the same day.…”

Section: Spatial Differences In Retrieved Pc Concentrationsmentioning

confidence: 99%

Comparison of phycocyanin concentrations in Chaohu Lake, China, retrieved using MODIS and OLCI images

Wang

Cui

et al. 2022

Front. Environ. Sci.

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Phycocyanin (PC) concentration is used as an indicator to characterize cyanobacteria biomass while monitoring eutrophication in inland water. Remote sensing provides useful methods for quantifying PC concentration; however, there is a shortage of datasets for the long-term monitoring of PC concentration when only a single remote sensing data is used. Therefore, PC concentrations obtained from multisource remote sensing images should be compared before integrating them for long-term monitoring. In this study, machine learning (ML) regression algorithms are used to develop PC concentration retrieval models suitable for Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-3 Ocean and Land Colour Instrument (OLCI) images, and their accuracies are compared. The two optimal retrieval models are applied to satellite images acquired on the same days to compare the spatial consistency of the two PC concentration retrieval results. The results show that the sensitive spectral range of PC concentration is 560–680 nm. Among the ML regression algorithms, gradient boosted tree (GBT) regression exhibits the highest PC retrieval accuracy for both the MODIS images (R2 = 0.82, RMSE = 61.9 μg/L) and OLCI images (R2 = 0.86, RMSE = 45.44 μg/L). The PC concentrations retrieved from the MODIS and OLCI images acquired in bloom and no-bloom periods have a high spatial consistency in most areas of Chaohu Lake. Their correlation coefficient also exceeds 0.7, and the average relative error reaches 0.293 μg/L. However, a large difference exists in areas with high PC concentrations, which may cause by the poor applicability of atmospheric correction algorithms and PC retrieval models in these areas. The proposed PC concentration retrieval models developed using GBT regression in this paper can expend the idea for the quantitative retrieval of other inland water quality parameters in inland water, and the conclusions should enable the effective integration of MODIS and OLCI images for the time series monitoring of PC concentrations in reservoirs and lakes.

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“…Empirical models can be subdivided into fitting and machine learning algorithms. For multi-spectral data and specific research areas, the empirical method is simple and fast, and the estimation accuracy meets the needs of remote sensing monitoring, and is more suitable for the processing and application of large time scales and big data [10,32,37].…”

Section: Introductionmentioning

confidence: 99%

Characteristics of the Total Suspended Matter Concentration in the Hongze Lake during 1984–2019 Based on Landsat Data

Lyu

et al. 2022

Remote Sensing

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The Hongze Lake is the fourth largest freshwater lake in China and an important lake for the East Route of the South-to-North Water Diversion Project. The water quality of the lake affects social development and the lives of residents. To assess the impacts of environmental changes and human activities on the distribution of the total suspended matter (TSM) in the Hongze Lake, we developed an algorithm that utilizes the near-infrared (NIR) band to estimate TSM based on in situ measurements. The algorithm was applied to Landsat images to derive TSM distribution maps from 1984 to 2019, revealing significant inter-annual, seasonal, and spatial variability. The relationship between TSM, precipitation, and wind speed was analyzed, and we found that: (1) The estimation model of TSM concentration in the Hongze Lake constructed for TM and OLI has a high accuracy, and it can be used to jointly monitor TSM concentration in the Hongze Lake for long-term series; (2) From 1984 to 2019, the concentration of TSM in the Hongze Lake showed a trend of first rising and then falling, with the maximum value in 2010 at 100.18 mg/L mainly caused by sand mining activities. Precipitation and wind speed weakly influence the inter-annual variation of TSM concentration; (3) The concentration of TSM in the Hongze Lake in summer is easily affected by flooding in the Huai River, and the concentration of TSM in other seasons is significantly negatively correlated with precipitation; (4) TSM is highest in the Huaihe Bay, followed by the Lihe Bay and Chengzi Bay. The main reason for this is that the input of the Huaihe Bay flows directly into this lake area and is also the main navigation channel. The results of this study are of great significance for the protection and management of the water environment of the Hongze Lake.

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A Review of Remote Sensing for Water Quality Retrieval: Progress and Challenges

Cited by 134 publications

References 140 publications

Evaluacija kakvoće mora luka metodom daljinskog istraživanja

Evaluacija kakvoće mora luka metodom daljinskog istraživanja

Comparison of phycocyanin concentrations in Chaohu Lake, China, retrieved using MODIS and OLCI images

Characteristics of the Total Suspended Matter Concentration in the Hongze Lake during 1984–2019 Based on Landsat Data

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