Monitoring inland water quality using remote sensing: Potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing
Given the recent advances in remote sensing analytics, cloud computing, and machine
learning, it is imperative to evaluate capabilities of remote sensing for water quality …
learning, it is imperative to evaluate capabilities of remote sensing for water quality …
A review of remote sensing applications for water security: Quantity, quality, and extremes
Water resources are critical to the sustainability of life on Earth. With a growing population
and climate change, it is imperative to assess the security of these resources. Over the past …
and climate change, it is imperative to assess the security of these resources. Over the past …
A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research
Spectral Indices derived from multispectral remote sensing products are extensively used to
monitor Earth system dynamics (eg vegetation dynamics, water bodies, fire regimes). The …
monitor Earth system dynamics (eg vegetation dynamics, water bodies, fire regimes). The …
Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach
Consistent, cross-mission retrievals of near-surface concentration of chlorophyll-a (Chla) in
various aquatic ecosystems with broad ranges of trophic levels have long been a complex …
various aquatic ecosystems with broad ranges of trophic levels have long been a complex …
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 …
Research trends in the use of remote sensing for inland water quality science: Moving towards multidisciplinary applications
Remote sensing approaches to measuring inland water quality date back nearly 50 years to
the beginning of the satellite era. Over this time span, hundreds of peer-reviewed …
the beginning of the satellite era. Over this time span, hundreds of peer-reviewed …
Estimation of soil moisture content under high maize canopy coverage from UAV multimodal data and machine learning
An accurate in-field estimate of soil moisture content (SMC) is critical for precision irrigation
management. Current ground methods to measure SMC were limited by the disadvantages …
management. Current ground methods to measure SMC were limited by the disadvantages …
Quantification of chlorophyll-a in typical lakes across China using Sentinel-2 MSI imagery with machine learning algorithm
S Li, K Song, S Wang, G Liu, Z Wen, Y Shang… - Science of the Total …, 2021 - Elsevier
Lake eutrophication has attracted the attention of the government and general public.
Chlorophyll-a (Chl-a) is a key indicator of algal biomass and eutrophication. Many efforts …
Chlorophyll-a (Chl-a) is a key indicator of algal biomass and eutrophication. Many efforts …
A global approach for chlorophyll-a retrieval across optically complex inland waters based on optical water types
Numerous algorithms have been developed to retrieve chlorophyll-a (Chla) concentrations
(mg m− 3) from Earth observation (EO) data collected over optically complex waters …
(mg m− 3) from Earth observation (EO) data collected over optically complex waters …
[HTML][HTML] PROSAIL-Net: A transfer learning-based dual stream neural network to estimate leaf chlorophyll and leaf angle of crops from UAV hyperspectral images
Accurate and efficient estimation of crop biophysical traits, such as leaf chlorophyll
concentrations (LCC) and average leaf angle (ALA), is an important bridge between …
concentrations (LCC) and average leaf angle (ALA), is an important bridge between …