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Transfer learning in environmental remote sensing
Abstract Machine learning (ML) has proven to be a powerful tool for utilizing the rapidly
increasing amounts of remote sensing data for environmental monitoring. Yet ML models …
increasing amounts of remote sensing data for environmental monitoring. Yet ML models …
A review of remote sensing for water quality retrieval: Progress and challenges
H Yang, J Kong, H Hu, Y Du, M Gao, F Chen - Remote Sensing, 2022 - mdpi.com
Water pollution has become one of the most serious issues threatening water environments,
water as a resource and human health. The most urgent and effective measures rely on …
water as a resource and human health. The most urgent and effective measures rely on …
A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes
Abstract Landsat-8 Operational Land Imager (OLI) provides an opportunity to map
chlorophyll-a (Chla) in lake waters at spatial scales not feasible with ocean color missions …
chlorophyll-a (Chla) in lake waters at spatial scales not feasible with ocean color missions …
Deep learning based regression for optically inactive inland water quality parameter estimation using airborne hyperspectral imagery
Airborne hyperspectral remote sensing has the characteristics of high spatial and spectral
resolutions, and provides an opportunity for accurate and efficient inland water qauality …
resolutions, and provides an opportunity for accurate and efficient inland water qauality …
Hyperspectral-to-image transform and CNN transfer learning enhancing soybean LCC estimation
Leaf chlorophyll content (LCC) is a distinct indicator of crop health status used to estimate
nutritional stress, diseases, and pests. Thus, accurate LCC information can assist in the …
nutritional stress, diseases, and pests. Thus, accurate LCC information can assist in the …
[HTML][HTML] Time-series modelling of harmful cyanobacteria blooms by convolutional neural networks and wavelet generated time-frequency images of environmental …
Early warning systems for harmful cyanobacterial blooms (HCBs) that enable precautional
control measures within water bodies and in water works are largely based on inferential …
control measures within water bodies and in water works are largely based on inferential …
A decade-long chlorophyll-a data record in lakes across China from VIIRS observations
Abstract Chlorophyll-a (Chl-a) is one of the optically active constituents in waters, and its
concentration is frequently utilized as a proxy for lake trophic levels. However, generating a …
concentration is frequently utilized as a proxy for lake trophic levels. However, generating a …
A new approach to monitor water quality in the Menor sea (Spain) using satellite data and machine learning methods
The Menor sea is a coastal lagoon declared by the European Union as a sensitive area to
eutrophication due to human activities. To control the deterioration of its water quality, it is …
eutrophication due to human activities. To control the deterioration of its water quality, it is …
Using convolutional neural network for predicting cyanobacteria concentrations in river water
Abstract Machine learning modeling techniques have emerged as a potential means for
predicting algal blooms. In this study, synthetic spatio-temporal water quality data for a river …
predicting algal blooms. In this study, synthetic spatio-temporal water quality data for a river …
[HTML][HTML] Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms–A case study in the …
J Qun'ou, X Lidan, S Siyang, W Meilin, X Huijie - Ecological Indicators, 2021 - Elsevier
Monitoring the water pollution level in real time is the most critical issue for protecting the
water quality of water reservoirs. Due to the restrictions on flight areas of Unmanned Arial …
water quality of water reservoirs. Due to the restrictions on flight areas of Unmanned Arial …