Mapping Chlorophyll-a Concentrations in the Kaštela Bay and Brač Channel Using Ridge Regression and Sentinel-2 Satellite Images

Ivanda, Antonia; Šerić, Ljiljana; Bugarić, Marin; Braović, Maja

doi:10.3390/electronics10233004

Cited by 18 publications

(7 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, both studies revealed better results by applying new algorithms. Our study's preliminary results support the findings of Ivanda et al, 2021 41 indicating the inapplicability of the existing algorithms and the necessity for a new algorithm for this specific area.…”

Section: Chlorophyll Asupporting

confidence: 81%

See 1 more Smart Citation

Water quality monitoring in an estuary using UAV hyperspectral imaging and satellite algorithms

Kvesić

Divic

Kišević

et al. 2022

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIV

View full text Add to dashboard Cite

Agricultural runoff and municipal sewage generate excessive nutrient input in estuaries, which disturbs the ecosystem's natural balance. Most monitoring programs require in situ measurements, which are expensive, time-consuming, and lack spatial and temporal resolution. Extensive research focuses on mitigating these costs by minimizing the indicators or using remote sensing tools. One of the currently investigated options is the application of unmanned aerial vehicles (UAVs) data since it can narrow the multi-resolution gap between the in situ and satellite data. As an initial step of such a multi-scaling approach, we focused on testing the applicability of existing algorithms developed for the Sentinel-2 multispectral data (MS) on our hyperspectral (HS) data obtained using UAV. We applied the available algorithms to estimate three water quality (WQ) parameters: Chlorophyll a (Chl a), Colored Dissolved Organic Matter (CDOM), and turbidity (TUR), for the in situ data acquired at the estuary of the River Jadro near the city of Split (Croatia). The higher spectral resolution obtained by HS imaging enabled us to use the specific wavelengths corresponding to the satellite bands for which the initial algorithms were developed. Moreover, we made one synthetic dataset of MS data, obtained by spectral resampling of HS data using spectral response functions for Sentinel 2 sensors given by ESA.By using these corresponding bandwidths, the initial study found medium and poor correlations with the WQ parameters: Chl a (R 2 =0.48), turbidity (R 2 =0.07), and CDOM (R 2 =0.22). Furthermore, all algorithms revealed higher correlations when using HS data compared to synthesized MS data. However, to fortify these results, we need to test more algorithms and compare the results with satellite reflectance data. Moreover, the future goals of this study are to develop new algorithms which could serve as surrogate data for satellite predictions.

show abstract

Section: Chlorophyll Asupporting

confidence: 81%

“…Chl a in Kaštela bay has already been monitored by RS technology 25,41 , where previous studies showed both poor and strong correlations between existing satellite algorithms and in situ Chl a data. Such opposite results could be due to the usage of different ground truth data with various spatial, temporal, and quantitative ranges.…”

Section: Chlorophyll Amentioning

confidence: 99%

Water quality monitoring in an estuary using UAV hyperspectral imaging and satellite algorithms

Kvesić

Divic

Kišević

et al. 2022

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIV

View full text Add to dashboard Cite

show abstract

“…RR ( Ivanda et al., 2021 ) serves as a technique for estimating the coefficients of a multiple regression model in scenarios where the independent variables exhibit high correlation. This is achieved by incorporating an L2 regularization term into the OLS loss function.…”

Section: Methodsmentioning

confidence: 99%

Estimation of the rice aboveground biomass based on the first derivative spectrum and Boruta algorithm

Nian,

Su,

Yue

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

Aboveground biomass (AGB) is regarded as a critical variable in monitoring crop growth and yield. The use of hyperspectral remote sensing has emerged as a viable method for the rapid and precise monitoring of AGB. Due to the extensive dimensionality and volume of hyperspectral data, it is crucial to effectively reduce data dimensionality and select sensitive spectral features to enhance the accuracy of rice AGB estimation models. At present, derivative transform and feature selection algorithms have become important means to solve this problem. However, few studies have systematically evaluated the impact of derivative spectrum combined with feature selection algorithm on rice AGB estimation. To this end, at the Xiaogang Village (Chuzhou City, China) Experimental Base in 2020, this study used an ASD FieldSpec handheld 2 ground spectrometer (Analytical Spectroscopy Devices, Boulder, Colorado, USA) to obtain canopy spectral data at the critical growth stage (tillering, jointing, booting, heading, and maturity stages) of rice, and evaluated the performance of the recursive feature elimination (RFE) and Boruta feature selection algorithm through partial least squares regression (PLSR), principal component regression (PCR), support vector machine (SVM) and ridge regression (RR). Moreover, we analyzed the importance of the optimal derivative spectrum. The findings indicate that (1) as the growth stage progresses, the correlation between rice canopy spectrum and AGB shows a trend from high to low, among which the first derivative spectrum (FD) has the strongest correlation with AGB. (2) The number of feature bands selected by the Boruta algorithm is 19~35, which has a good dimensionality reduction effect. (3) The combination of FD-Boruta-PCR (FB-PCR) demonstrated the best performance in estimating rice AGB, with an increase in R² of approximately 10% ~ 20% and a decrease in RMSE of approximately 0.08% ~ 14%. (4) The best estimation stage is the booting stage, with R2 values between 0.60 and 0.74 and RMSE values between 1288.23 and 1554.82 kg/hm2. This study confirms the accuracy of hyperspectral remote sensing in estimating vegetation biomass and further explores the theoretical foundation and future direction for monitoring rice growth dynamics.

show abstract

“…Various datasets from lakes and reservoirs have been compiled, leading to the development and application of Chl-a estimation models using multispectral satellites through different techniques such as semi-empirical models, multiple linear regression (MLR), and machine learning [17][18][19]. Of these, MLR is characterized by its relative simplicity and ease of interpretation.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Sentinel-2 Based Chlorophyll-a Estimation in a Small-Scale Reservoir: Assessing Accuracy and Availability

Jang,

Kim,

Kim

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Small-scale reservoirs located in river estuaries are a significant water resource supporting agricultural and industrial activities; however, they face annual challenges of eutrophication and algal bloom occurrences due to excessive nutrient accumulation and watershed characteristics. Efficient management of algal blooms necessitates a comprehensive analysis of their spatiotemporal distribution characteristics. Therefore, this study aims to develop a chlorophyll-a (Chl-a) estimation model based on high-resolution satellite remote sensing data from Sentinel-2 multispectral sensors and multiple linear regression. The multiple linear regression (MLR) models were constructed using multiple reflectance-based variables that were collected over 2 years (2021–2022) in an estuarine reservoir. A total of 21 significant input variables were selected by backward elimination from the 2–4 band algorithms as employed in previous Chl-a estimation studies, along with the Sentinel-2 B1-B8A wavelength ratio. The developed algorithm exhibited a coefficient of determination of 0.65. Spatiotemporal variations in Chl-a concentration generated by the algorithm reflected the movement of high Chl-a concentration zones within the body of water. Through this analysis, it turned out that Sentinel-2-based spectral images were applicable to a small-scale reservoir which is relatively long and narrow, and the algorithm estimated changes in concentration levels over the seasons, revealing the dynamic nature of Chl-a distributions. The model developed in this study is expected to support effective algal bloom management and water quality improvement in a small-scale reservoir or similar complex water quality water bodies.

show abstract

Mapping Chlorophyll-a Concentrations in the Kaštela Bay and Brač Channel Using Ridge Regression and Sentinel-2 Satellite Images

Cited by 18 publications

References 40 publications

Water quality monitoring in an estuary using UAV hyperspectral imaging and satellite algorithms

Water quality monitoring in an estuary using UAV hyperspectral imaging and satellite algorithms

Estimation of the rice aboveground biomass based on the first derivative spectrum and Boruta algorithm

Evaluation of Sentinel-2 Based Chlorophyll-a Estimation in a Small-Scale Reservoir: Assessing Accuracy and Availability

Contact Info

Product

Resources

About