NEURAL NETWORK MODELING OF LAKE SURFACE CHLOROPHYLL AND SEDIMENT CONTENT FROM LANDSAT TM IMAGERY
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Summary
Back-propagation neural network is used to model the transfer function between chlorophyll concentration and suspended solid, and sensor-received radiances at the first four bands of LandsatTM, and shows that, the lake is eutrophic even in the low productivity season.
- Type
- article
- Published
- 2001-01-01
- Cited by
- 12
- References
- 18
- OpenAlex
- https://openalex.org/W19798395
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:2299317
Keywords
Environmental science, Water quality, Mean squared error, Eutrophication, Remote sensing
References
- Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery: A Review
- Practical neural network recipes in C
- Fundamentals Of Neural Networks
- The Relationship of MSS and TM Digital Data with Suspended Sediments, Chlorophyll, and Temperature in Moon Lake, Mississippi
- A neural network approach for modeling nonlinear transfer functions: Application for wind retrieval from spaceborne scatterometer data
- Neural network for emulation of an inverse model: operational derivation of Case II water properties from MERIS data
- Application of neural network method to case II water
- Determination of chlorophyll concentration changes in Lake Garda using an image-based radiative transfer code for Landsat TM images
- Comparison of NIR/RED ratio and first derivative of reflectance in estimating algal-chlorophyll concentration: A case study in a turbid reservoir
- The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features
- Applying artificial neural network methodology to ocean color remote sensing
- A trophic state index for lakes1
- A neural network-based model for estimating the wind vector using ERS scatterometer data
- A Neural Network Model for Estimating Sea Surface Chlorophyll and Sediments from Thematic Mapper Imagery
- Landsat Thematic Mapper monitoring of turbid inland water quality
- The Relationship Between Suspended Sediment Concentration and Remotely Sensed Spectral Radiance: A Review
- Remote sensing of ocean colour in coastal, and other optically-complex, waters.
- Fundamentals of neural networks
Cited by
- Monitoring and assessment of wetlands using Earth Observation: the GlobWetland project.
- Comparative Analysis of Four Models to Estimate Chlorophyll-a Concentration in Case-2 Waters Using MODerate Resolution Imaging Spectroradiometer (MODIS) Imagery
- Artificial Neural Networks Application in Lake Water Quality Estimation Using Satellite Imagery
- LAKE WATER QUALITY ASSESSMENT FROM LANDSAT THEMATIC MAPPER DATA USING NEURAL NETWORK: AN APPROACH TO OPTIMAL BAND COMBINATION SELECTION1
- Remote sensing of water quality in an Australian tropical freshwater impoundment using matrix inversion and MERIS images
- An Adaptive Model to Monitor Chlorophyll-a in Inland Waters in Southern Quebec Using Downscaled MODIS Imagery
- Assessing State of the Art on Artificial Neural Network Paradigms for Level of Eutrophication Estimation of Water Bodies
- Multi-Algorithm Indices and Look-Up Table for Chlorophyll-a Retrieval in Highly Turbid Water Bodies Using Multispectral Data
- Evaluation of MERIS Chlorophyll-a Retrieval Processors in a Complex Turbid Lake Kasumigaura over a 10-Year Mission
- A Learning Vector Quantization Based Geospatial Modeling Approach for Inland WQ Remote Prediction
- Ocean water quality monitoring using remote sensing techniques: A review.
- CHLOROPHYLL AND PHYTOPLANKTON DETECTION USING REMOTE SENSING TO FIND FISHING AREA
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