ArticleEnvironmental science and pollution research international2022
Deep learning-based remote sensing estimation of water transparency in shallow lakes by combining Landsat 8 and Sentinel 2 images.
Article in Environmental science and pollution research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing.Sensors (Basel, Switzerland) · 2022Pooled it
- Prediction of Water Quality Parameters in the Paraopeba River Basin Using Remote Sensing Products and Machine Learning.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
Water transparency is a key indicator of water quality as it reflects the turbidity and eutrophication in lakes and reservoirs. To carry out remote sensing monitoring of water transparency rapidly and intelligently, deep learning technology was used to construct a new retrieval model, namely, point-centered regression convolutional neural network (PSRCNN) suitable for Sentinel 2 and Landsat 8 images. The impact of input feature variables on the accuracy of the inversion model was examined, and the performance of an optimized PSRCNN model was also assessed. This model was applied to remote sensing images of three shallow lakes in the eastern China plain acquired in summer. The PSRCNN model, constructed using five identical bands from Landsat 8 and Sentinel 2 images and 20 band combinations as the input variables, the input window size of 5 × 5 pixels, proves a good predictive ability, with a verification accuracy of R
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