Water Column Correction Research Articles

Towards Nationwide Mapping of Seagrasses in the Philippines: Empowering Students through Remote Sensing Education

Abstract. Seagrasses are critical marine ecosystems facing numerous threats in the Philippines. This study aimed to develop a robust methodology for nationwide seagrass mapping using open-access satellite imagery and empower students to contribute through remote sensing education. We achieved this through a two-pronged approach: (1) developing a satellite-based mapping methodology and (2) training students to apply this method across the archipelago. Utilizing Sentinel-2 satellite images, we processed the data using SNAP and Google Earth Engine to identify seagrass beds. Before classification, the images underwent preprocessing steps including land masking, water column correction, and extraction of shallow water bathymetry. These processed layers were then stacked with Sentinel-2 bands 2, 3, 4, 8, and 11. Two classification methods, support vector machine, and random forest, were employed, and their results were compared with existing seagrass maps. Students achieved overall accuracies ranging from 70 to 90 percent. Prior to mapping activities, students received training on remote sensing techniques and seagrass ecology. This project demonstrates the potential of remote sensing for large-scale seagrass mapping and highlights the value of student engagement in marine conservation.

Benthic habitat mapping for estimating seagrass carbon stock across Takabonerate Islands, Indonesia

Data on benthic habitat cover are essential for monitoring and managing coastal areas, providing valuable ecosystem services for local communities. Moreover, seagrass extent is crucial for estimating carbon stock using Tier-1 calculations. On the other side, remote sensing technology has proven effective in mapping benthic habitat cover, including seagrass, over large local areas. Therefore, this research aims to map specific benthic habitats in a local area to obtain high-accuracy estimates of seagrass extent for carbon stock estimation. The study was conducted in the Takabonerate Islands, Indonesia, using Sentinel-2A imagery. The study involved field data processing, which enabled the mapping of eight benthic habitat classes using Sentinel-2A images, along with the steps for Sentinel-2A image processing for benthic habitat mapping. The benthic habitat map was produced following image correction steps, including atmospheric, sunglint, and water column corrections, followed by image classification using a Neural Net Classifier (NNC). Seagrass carbon stock estimation was performed using the Tier-1 equation from IPCC 2013. The study revealed a total benthic habitat area of 57,538.04 ha, with a seagrass area of 3127.42 ha for carbon stock estimation. The potential seagrass carbon stock in the study area was estimated to range from 337.76 GgC/ha to 31.27 GgC/ha using the Tier-1 equation. The overall accuracy of the benthic habitat map was 80.5 %, calculated using a confusion matrix table. The spatial information from this study can be used by local coastal managers and governments for baseline and monitoring purposes. The key interpretations from this study provide valuable findings that can be adapted and improved for future local mapping purposes.

Two decades of coral bleaching in selected islands of Pacific Ocean: A holistic impact assessment

Coral reefs are regarded as one of the most diverse, productive as well as sensitive ecosystems existing across the world. Major coral reefs across the Pacific Ocean exhibit a vast variety of species, the majority of which are under threat, largely due to changes in past conducive climatic and oceanic parameters. The present study involves the mapping of coral reefs at select islands of Great Barrier Reefs (Heron Island), Fiji (Namena Island), and Cook Island (Aitutaki Island) using Landsat 7 (ETM+) and Landsat 8 (OLI/ TIRS) satellite data for the years 2002 and 2020. Depth invariant bottom index (DII) is applied for water column correction and identification of bottom type submerged and heterogeneous coral reef classes. Maximum likelihood classifier (supervised classification) is performed to characterize various classes of the coral community. Turbidity values (10.4 – 53 Nephelometric Turbidity unit (NTU)), (4.74–26.1NTU), and (14.56–73.56 NTU) for Heron, Namena, and Aitutaki Islands indicate a pronounced effect of sedimentation due to an increase in Sea level rise and anthropogenic activities. Increasing trend of oceanic parameters viz. SST, PAR, salinity, and sea level rise indicated an adverse impact on coral health and triggered coral bleaching along these Islands especially due to El Niño events in the years 2015–2016. We identified the coral mortality for 18 years of period as 16.1 %, 20.2 % and 11. 2 % in Heron, Namena, and Aitutaki Island respectively. The study provides a holistic impact assessment of various anthropogenic activities and climate change on the health of the coral reef ecosystem.

INTEGRATION OF STUMPF'S RATIO MODEL AND RANDOM FOREST FOR SATELLITE-DERIVED BATHYMETRY ESTIMATION

Abstract. The development of remote sensing for coastal and marine environment mapping has significantly enhanced our understanding of these ecosystems, enabling improved mitigation strategies against the impacts of human activities. However, remote sensing must consider the complex interplay of the atmosphere and water column. Ongoing research focuses on refining water column correction techniques, including Depth Invariant Indices (DII), Radiative Transfer models, and bathymetry models. This study specifically aims to enhance the Stumpf's Ratio model (SRM) for bathymetry by employing the Random Forest (RF) machine learning regression algorithm. The resulting bathymetry model, which incorporates visible bands from a Sentinel-2 MSI image, and their Stumpf's ratios, outperforms other methods, yielding the highest accuracy with RMSE and R2 values of 1.25 m and 0.854, respectively. This was followed by the multivariate SRM with RMSE and R2 values of 2.196 m and 0.554 respectively These findings demonstrate the promising potential of using RF machine learning regression with SRM for bathymetry modelling.

Multitemporal Analysis of Seagrass Dynamics on Derawan Island (2003–2021) Using Remote Sensing Techniques

The shallow waters around Derawan Island are renowned for their beauty, attracting a significant number of tourists. Since the 2008 National Sports Week (PON) in East Kalimantan, the construction of inns and jetties has enhanced both accommodation and accessibility on the island. However, this development has also impacted the seagrass beds in the surrounding shallow waters. This study examines the changes in the area and density of seagrass beds from 2003 (prior to the PON activities) through to 2011 (a few years post-PON) and in 2021 (the most recent conditions), assessing the effects of lodging and jetty construction on these beds. Data were collected via field surveys using the photo transect method, and the benthic habitat map was created using Landsat 8 OLI Imagery, applying the Lyzenga water column correction algorithm and unsupervised classification method. The Normalized Difference Building Index (NDBI) algorithm and land digitization were utilized to track the development of the inns and jetties, revealing a rapid, widespread increase in construction throughout the island's southern region (R-square = 0.59). The study findings indicate a significant degradation of seagrass meadows between 2003 and 2021, particularly near populated areas on the southern coast, resulting in decreased density levels.

Benthic Habitat Classification Using Sentinel-2a Image With And Without Water Column Correction In Pengudang Village, Bintan Regency

Research on the mapping of benthic habitats in the waters of Pengudang Village, Bintan Regency, uses Sentinel-2A imagery. This mapping process has the potential to display the results of classification of benthic habitats in shallow waters. The purpose of this study was to assess the Maximum Likelihood Classification (MLH) algorithm model for mapping benthic habitat cover through Sentinel-2A satellite imagery using the lyzenga water column correction and without water column correction and to calculate the accuracy of the MLH classification results for benthic habitats in waters Pengudang Village. This classification uses a pixel-based image classification which is grouped into a cover class for each pixel. This classification groups into 4 benthic habitat classes, namely dead coral sand (KMP), sea grass (LM), live coral sand (PKH) and sand (PS). The results showed that the application of the lyzenga correction using the MLH algorithm in mapping benthic habitats resulted in a significant accuracy rate (80,00%), while the application without lyzenga correction using the MLH algorithm resulted in an accuracy rate (76,00%). These findings support the use of the lyzenga correction method which can increase the level of accuracy by (4,00%) with the MLH algorithm as a guide in better mapping of benthic habitat cover in Pengudang Village.

Decadal Remote Sensing Analysis of Seagrass Changes in Palu Bay, Central Sulawesi

Seagrass meadows provide a variety of material, non-material and regulatory coastal ecosystem service; however, as the distribution of seagrass beds changes over time due to both anthropogenic activities and natural factors, it is important to monitor changes in seagrass condition. Seagrass meadows in Palu Bay are threatened by activities such as coastal development and land reclamation. Additionally, the bay was hit by a significant tsunami in 2018, which could have impacted ecosystems in the bay, including seagrass meadows. The aim of this study was to detect changes in seagrass extent and distribution over a 10-year period from 2012 to 2022 and changes in land use over approximately a decade (2010 and 2021) through the use of remote sensing technology. Changes in eagrass meadow areal extent were analyzed using data from a 2012 Landsat 7 Satellite Data Acquisition and a 2022 Landsat 8 Satellite Data Acquisition. Water column correction was implemented using the Lyzenga Algorithm. The results showed a significant decrease in the area of seagrass meadows around the coastal area of Palu Bay. Seagrass meadows in 2012 and 2022 covered 127.08 Ha and 87.79 Ha, respectively, indicating a decrease in extent of 43.29 Ha. As the accuracy of the satellite data classification results was 80%, the results are considered acceptable. Anthropogenic activities (mainly mining and construction related) are strongly suspected as the main drivers of this decline, while earthquake and tsunami events likely aggravated the degradation of coastal ecosystems in Palu Bay, including seagrass meadows.

Quantifying biomass and canopy cover of submerged aquatic vegetation on an offshore island

The coverage and biomass of submerged aquatic vegetation (SAV) are vital characteristics of marine resource management and development. However, using in situ measurements to assess the distribution and biomass of SAV is challenging because SAV distribution varies with biomass, substrate type, and depth, making it difficult to monitor. Satellite remote sensing is an advanced tool that can tackle this problem. In this study, several techniques, such as atmospheric correction, water column correction, and supervised classification, were integrated with Landsat-8 images to map SAV distribution on an offshore island in Viet Nam, Ly Son island. The substrate-classifying result had high accuracy (OA = 92.6%, Ҡ = 0.9 ). The coastal, blue, and green bands of Landsat-8 were used to estimate the SAV biomass using a polynomial regression (RMSE =±1440.6 g fresh m−2, R2=0.71). The results showed that the total fresh biomass of SAV in Ly Son island is 14551.04 tonnes, with a corresponding area of 332.1 hectares, equivalent to an average biomass of 4381.5 ± 1440.6 g fresh m−2. The distribution and biomass maps of SAV produced in this study could be used as input to measure the attributes of SAV biodiversity, contributing more effectively to monitoring marine ecosystems in Viet Nam.

Random Forests Algorithm for Two Levels of Coral Reef Ecosystem Mapping Using Planetscope Image in Malalayang Beach, Manado

The coral reef ecosystem has a significant physical and biological function and is also one of the coastal ecosystem components apart from the seagrass and mangrove ecosystem. Besides their ecological function, the coral reef also has an economic function. The condition of the coral reef ecosystem in Malalayang Beach has been changing for years. The utilization of remote sensing images can monitor current conditions. This research aims to map the coral reef ecosystem mapping in Malalayang Beach, Manado and conduct a test for the accuracy of coral reef ecosystem mapping using field survey data as a classification and validation sample. PlanetScope multispectral image has four channels to detect underwater objects: red, green, blue and near infrared. PlanetScope level 3B image for the research has a surface reflectance value for its pixel. The image processing stages of this research consist of sunglint correction, water column correction, and then continue to classify the coral reef ecosystem using random forests algorithm. Classification and accuracy training sample data were obtained using the photo transect technique. The sunglint correction regression equation is between 0.27 – 0.38. The coefficient of attenuation ratio in B1 is 0.927797938, B2 is 0.168841585, and B3 is 0.29033029. This value then becomes the input for the Lyzenga formula. The classification accuracy for level one using random forests is 72,54%, and the accuracy for level two mapping is 37,61%.Keywords: Coral Reef Ecosystem, Planetscope, Random Forests

Mapping Sea Grass Coverage of Tanjung Benoa Bali Using Medium Resolution Satellite Imagery Sentinel 2B

Seagrass beds are important components of a coastal ecosystem. This ecosystem serves as the primer producers of the water food chain, habitat for marine biota, produces organic carbon, and indirectly contributes to the economic well-being of coastal communities. However, the ecosystem is vulnerable to damage caused by natural factors and human activities. The objectives of this study were, firstly to identify the distribution of seagrass beds in Tanjung Benoa using Sentinel 2B satellite imagery and secondly to compare classification results from two different approaches namely pixel-based image classification and object-based image classification. Accuracy-test was carried out using field data reference of 195 sample points in the form of a 10 m X 10 m transect. The image pre-processing process was conducted with Bottom of Atmosphere (BoA) correction using the Dark Object Subtraction (DOS) method. Furthermore, the water column correction was performed using the Depth Invariant Index (DII) and the Lyzenga algorithm. The mapping results showed that the area of seagrass beds in the shallow waters of Tanjung Benoa reaches 242.99 ha. There were seven seagrass species in the study area, with an average cover of 75%. The accuracy of object-based image classification was higher than that of pixel-based classification with a difference up to 25% for six classes classification and 15% for two classes classification. Excellent results for classifying seagrasses based on cover density can be obtained when high-resolution satellite imagery and OBIA are combined with the SVM or Fuzzy Logic algorithm.

Sentinel-2 mapping of a turbid intertidal seagrass meadow in Southern Vietnam

Seagrass ecosystems are crucial to the carbon cycle, biodiversity, fisheries, and erosion prevention. However, there are gaps in mapping seagrass in tropical coastal waters due to turbidity and constantly changing tidal dynamics. To tackle such issues, tidal heights and reflectance of 33 Sentinel-2 images were used to calculate a multitemporal composite of Sentinel-2 images, which was classified using the Random Forest classifier. UAV images classified with Object-based image analysis provided ground truth for training and validation. The effectiveness of water column correction and using the multitemporal composite was evaluated by comparing overall classification accuracy. As a result, applying water column correction improved classification accuracy from 80.4% to 80.6%, while using the multi-temporal composite improved accuracy to 88.6%, among the highest accuracy achieved for seagrass classification in turbid waters. The combined protocol could lead to advances in quantifying seagrass distribution in the tropical coastal waters and their carbon sequestration capacity, resolving significant uncertainties in blue carbon estimation.

High-resolution benthic habitat mapping from machine learning on PlanetScope imagery and ICESat-2 data

This study proposed a comprehensive approach that utilized PlanetScope imagery for classifying tropical-marine benthic habitats after retrieving bathymetry from ICESat-2 data and water-column correction for areas around Lyson Islands, Vietnam. Exact bathymetry derivation and water column correction were applied to the PlanetScope imagery, making it an effective method for mapping marine benthic habitats. Water column correction was achieved by applying Depth Invariant Index (DII) and Bottom Reflectance Index (BRI). Moreover, two conventional machine learning algorithms, including Random Forest and Support Vector Machine, and a current deep Convolutional Neural Network (CNN) was employed to classify the benthic features. The overall accuracy of these classifiers are 80.74%, 84.19%, and 89.80% with the BRI, 80.17%, 82.75%, and 87.85% with the DII compared to 37.64%, 42.5%, and 47.2% of without corrected water columns respectively. The CNN model demonstrated that the approach significantly maximizes the improvement in benthic classification results in coastal region.

MAPPING BENTHIC HABITAT FROM WORLDVIEW-3 IMAGE USING RANDOM FOREST CASE STUDY: NUSA LEMBONGAN, BALI, INDONESIA

Abstract. Benthic habitats are coastal ecosystems that provide many benefits and play an important role in the diversity of nature. The maps are developed using random forest method on the Worldview-3 image. Optically shallow water around Nusa Lembongan was selected as the study area. Sunglint and water column correction were applied to surface reflectance data to produce deglint, depth invariant index, and deglint-depth invariant index band for random forest classification. In addition, tuning parameters, including the number of trees and the function to determine the number of randomly selected, are used in the classification. The benthic habitats classification scheme was constructed based on the variations of in situ data, which consisted of coral reefs, seagrass, macroalgae, and substrate. The confusion matrix was used to analyze the accuracy, and the McNemar test to evaluate the level of statistical significance between different processing scenarios. The best benthic habitats map is determined based on the accuracy and spatial distribution of the object. Meanwhile, the random forest algorithm produced 62.72% – 73.00% overall accuracy and these accuracy variations were not statistically significant. According to the findings, surface reflectance data with the parameter setting comprising 500 trees and square root function yielded the best random forest scenario for mapping benthic ecosystems.

Enhancing UAV images to improve the observation of submerged whales using a water column correction method

Enhancing <scp>UAV</scp> images to improve the observation of submerged whales using a water column correction method

Spatial Distribution of Coral Reef Degradation with Human Activities in the Coastal Waters of Samatellu Lompo Island, South Sulawesi

A healthy coral reef ecosystem can be beneficial for the survival of fish habitats and aquatic ecosystems. This study aims to analyze the influence of human activities on the spatial distribution of coral reefs in the coastal waters of Samatellu Lompo Island, Pangkajene Islands Regency, South Sulawesi in 2000, 2014, 2018, and 2021. The spatial distribution of coral reefs was obtained through a field survey using the underwater transect photo method. Then, satellite images were processed by using the Lyzenga algorithm for water column correction, and aquatic objects were classified by using unsupervised classification. Human activities that affect coral reef destruction were obtained through interviews and it was strengthened with related literature studies. The results showed that the coral reefs in the coastal waters of Samatellu Lompo decreased from 2000-2021. In 2000, the live coral area was 13.53 ha, whereas in 2021 it was 8,031 ha. Destructive fishing activities such as using bombs and poison in catching fish are the main factors of coral reef destruction. In addition, destructive fishing activities commonly occur in the western and northern waters of Samatellu Lompo that causing the live coral into dead coral or rubble.

Seagrass mapping using high resolution multispectral satellite imagery: A comparison of water column correction models

Satellite remote sensing is an efficient and economical technique for studying coastal bottoms in clear and shallow waters. Accordingly, the main objective of this study is the generation of benthic maps using high spatial resolution multispectral images from the WorldView-2/3 satellites. In this context, one of the main challenges consists of eliminating the disturbances caused in the signal by the atmosphere, the sea surface, and the water column. Regarding the water column correction, there is controversy about its effectiveness to improve the results achieved. To assess the impact of the water column correction in seagrass mapping, two coastal areas with different characteristics have been selected. Specifically, an analysis has been carried out consisting of the assessment of the Lyzenga and Sagawa water column correction models to identify the algorithm that provides the best mapping precision and, additionally, to seek if this pre-processing stage is helpful when classifying the seabed. The classification models selected for the study were: Gaussian Naïve Bayes (GNB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Subspace KNN (S-KNN). Machine learning techniques have proven to achieve better results and, in particular, SVM and KNN models provide the best overall accuracy. The results after benthic mapping have demonstrated, that image classification without water column corrections provides better accuracy (95.36% and 99.20%) than using Lyzenga (73.49% and 97.80%) or Sagawa (82.04% and 99.10%), for Case 2 and 1 waters, respectively.

Evaluation of Water Column Correction Methods in Mapping Seagrass Bed Using Remote Sensing Data in Khanh Hoa Province, Vietnam

The use of remote sensing images for the interpretation of underwater substrate objects depends on their reflectance spectrum of different water depths. Thus, the water column correction step is important in the interpretation. Two commonly used water column correction methods are Lyzenga’s depth invariant index (DII) method and Sagawa’s bottom reflectance index (BRI) method. To evaluate the role of each method in Khanh Hoa waters, Tuan Le water, with a high seagrass coverage and moderate turbidity, and Thuy Trieu Lagoon, with high biodiversity of seagrass species and turbidity water, were selected as representatives. 70% of the survey data was used for ground training data of seagrass (dense and patchy), muddy-sand and sand features and the interpretation of both methods, whereas the remaining 30% of the survey data was used for validation of the mapping results. The maximum likelihood classification approach was used to extract the seagrass map and evaluated by overall accuracy as well as the Kappa coefficient. The processing results show that, in Tuan Le water, using the DII method gives an accuracy of 82.1% and the BRI of 80.1%; and in Thuy Trieu Lagoon, the DII method has an accuracy of 80.67% and the BRI of 80.0%. These results demonstrate that both methods have high accuracy results in both areas, but the method of BRI gives better results.

Multi-band bottom index: A novel approach for coastal environmental monitoring using hyperspectral data

Hyperspectral remote sensing has potential for improving shallow-water environmental monitoring, including benthic cover mapping and estimation of bathymetry and water optical properties. Detailed spectral information provided by hyperspectral sensors is particularly required to discriminate benthic covers that have similar spectral patterns (e.g., coral and algae), which cannot be achieved by multispectral sensors with a limited number of wavebands. An important process in such applications is separation of the bottom reflection from the absorption and scattering caused by the water column. Recently, inversion of a semi-analytical radiative transfer model using airborne hyperspectral data has become popular for simultaneous estimation of benthic cover, bathymetry, and water optical properties. However, it requires non-trivial settings and ancillary information, and generally is not user friendly. A cost-effective water-column correction method based on two-band combination has also been popular for multi-band remote sensing; however, it is not suited to hyperspectral data, because of the combinatorial proliferation of many hyperspectral wave bands. To fill the gap between the two approaches, in this paper, a new cost-effective method for water-column correction optimal for hyperspectral data is proposed. The method, called the multi-band bottom index (MBI), incorporates empirical radiative transfer theory and a traditional spectral enhancement technique (i.e., logarithmic residual method). A case study was conducted for the coral reef habitats around Ishigaki Island, using free hyperspectral satellite imagery (Hyperion). The result shows that our MBI approach has more robust performance in the estimation of the water attenuation coefficient than the existing water-column correction approach. The derived MBI map successfully mitigates water-column effects and enhances the spectral pattern of benthic materials. Comparison with in-situ spectra in the literature revealed that the overall spectral pattern of the MBI is reasonable for the benthic categories of coral, seagrass, and sandy bottom. The MBI approach is also promising for application to recently launched hyperspectral satellite sensors, such as HISUI, DESIS, and PRISMA.

Assessment of WorldView-2 images for aboveground seagrass carbon stock mapping in patchy and continuous seagrass meadows

ABSTRACT Spatially explicit information on aboveground seagrass carbon stock (AGCseagrass) is required to understand the role of seagrass as a nature-based solution to mitigating and adapting to climate change. Remote sensing provides the most effective and efficient means to map AGCseagrass. This research aimed to assess the accuracy of multispectral images to map AGC in seagrass meadows with different characteristics. Kemujan Island in Indonesia was selected to represent patchy beds with relatively low seagrass percent cover and low water clarity, whereas Labuan Bajo of Indonesia served an example of continuous beds with a relatively high percent cover and water clarity. WorldView-2 is a high-spatial-resolution multispectral image with six visible bands, by which water can be penetrated. Several image processing techniques, including sunglint and water column correction, principal component analysis, and co-occurrence texture analysis, were applied to the atmospherically corrected WorldView-2 images. These datasets were used as inputs in the AGC empirical model using seven regression algorithms, namely, single-band linear regression, band-ratio linear regression, stepwise regression, random forest regression, support vector regression, extreme gradient boosting, and multivariate adaptive regression spline. Seagrass field data collected using photo quadrat technique were used to train the regression model and assess the accuracy of the resulting AGCseagrass maps. The results indicated that WorldView-2 can be used to map AGC in different seagrass meadows with a consistent accuracy. The most accurate AGCseagrass map for Kemujan Island had a root mean square error (RMSE) of 4.11 g C m−2 for the aboveground stock in the range of 0–28.70 g C m−2, and for Labuan Bajo, the RMSE of the map was 9.73 g C m−2, with aboveground stock range of 0.31–64.38 g C m−2. Model cross-validation revealed that the mapping model can be site-specific or robust depending on the characteristics of the field-derived AGCseagrass data used to train the regression algorithm. For example, the model developed for Labuan Bajo seagrasses, which had a higher AGC variance, can be successfully applied to Kemujan Island with its lower AGC variance, but not vice versa. This finding is a key factor in the future development of a robust AGCseagrass mapping model that is applicable to various seagrass meadows as a stepping stone to accelerating the filling of gaps in the global seagrass dataset.

Measurement of in-vivo spectral reflectance of bottom types: Implications for remote sensing of shallow waters

Field spectroscopy data serve as a standard for the inter-comparisons, calibration and validation of remote sensing data. It also greatly assists in the detection, characterization and mapping of the earth features from the satellite imagery. Strategies and methods for field-based spectro-radiometric measurements are well-established for land and deep ocean applications, however, there are certain challenges and limitations with respect to the shallow water coastal ecosystems. This paper focuses on the background theory, concepts, and measurement techniques of in-vivo sensing of benthic reflectance in shallow water environment. We investigated various measurement methods, influences of water column, air–water interface, sources of errors (introduced by experimental and environmental factors), and the correction methods that are involved in the determination of benthic substrate reflectance. Special emphasis is provided using in-vivo measurements made in the estuarine and shelf regions, illustrating the benthic substrate reflectance at various platforms, i.e., within the water column, above the water surface, at the top-of-atmosphere, in the air, and in a controlled (laboratory) environment. As part of the correction procedure, a new water column correction method is theoretically formulated and examined for varying water optical properties and water depth. A new step correction method is also developed (based on the additive approach) to correct the spectral reflectance measured by a full range spectroradiometer (350–2500 nm) that is in-built with two or more detectors. This study will have direct implications for improving the data quality of field-based spectro-radiometric measurements that are essential for shallow water remote sensing applications.