Remote Sensing Big Data for Water Environment Monitoring: Current Status, Challenges, and Future Prospects

Abstract

Accurate water extraction and quantitative estimation of water quality are two key and challenging issues for remote sensing of water environment. Recent advances in remote sensing big data, cloud computing, and machine learning have promoted these two fields into a new era. This study reviews the operating framework and methods of remote sensing big data for water environment monitoring, with emphasis on water extraction and quantitative estimation of water quality. The following aspects were investigated in this study: (a) image data source and model evaluation metrics; (b) state-of-the-art methods for water extraction, including threshold-based methods, water indices, and machine learning-based methods; (c) state-of-the-art models for quantitative estimation of water quality, including empirical models, semi-empirical/semi-analytical models, and machine learning-based models; (d) some shortcomings and three challenges of current remote sensing big data for water environment monitoring, namely the new data gap caused by massive heterogeneous data, inefficient water environment monitoring due to "low spatiotemporal resolution," and low accuracy of water quality estimation models resulting from complex water composition and insufficient atmospheric correction methods for water bodies; and (e) five recommendations to solve these challenges, namely, using cloud computing and emerging sensors/platforms to monitor water changes in intensive time series, establishing models based on ensemble machine learning algorithms, exploring quantitative estimation models of water quality that couple physics and causality, identifying the missing elements in water environment assessments, and developing new governance models to meet the widespread applications of remote sensing of water environment. This review can help provide a potential roadmap and information support for researchers, practitioners, and management departments in the theoretical exploration and innovative application of remote sensing big data for water environment monitoring.

Comparing the machine learning and empirical algorithms on remote sensing estimates water quality parameters of constructed wetland

“…Remote sensing satellites that are now publicly free available have both high time-frequency and high spatial resolution, i.e., Sentinel-2 A/B double satellites combination have a 5-day revisit period and a spatial resolution of 10m [2]. Satellite monitoring of water quality can achieve low-cost, high spatial and temporal resolution monitoring, but the larger error is its weakness [3]. The use of satellites for water quality monitoring has been widely used in oceans, inland lakes, and rivers [3], but few studies for monitoring wetland water quality have been reported.…”

“…Satellite monitoring of water quality can achieve low-cost, high spatial and temporal resolution monitoring, but the larger error is its weakness [3]. The use of satellites for water quality monitoring has been widely used in oceans, inland lakes, and rivers [3], but few studies for monitoring wetland water quality have been reported. Therefore, the wetland remote sensing always focuses on ecosystem monitoring, soil matters and boundary changes [4]- [7].…”

Triboelectric Nanogenerators as Power Sources for Chemical Sensors and Biosensors

“…The advancement in micro/nanotechnology has significantly enriched the quality and reliability of miniaturized sensors needed across multiple sectors including health monitoring, , agriculture, aquaculture, environment monitoring, robotics, − rehabilitation, , automation, space, , etc. Complemented by recent progress in flexible and printed electronics, these solutions continue to enrich the above areas and open new opportunities by allowing the detection of various chemical and biological analytes or parameters such as dopamine, glucose, tyrosine, and creatinine for human healthcare. , The rapidly aging society and new forms of lethal diseases have pushed healthcare systems to adopt innovative solutions such as digital healthcare, which have contributed immensely to the rising demand for portable and wearable chemical sensors and biosensors globally. − Numerous selective and sensitive chemical sensors and biosensors have been reported in the literature − using distinct working mechanisms and exploiting the optical, piezoelectric, electrochemical, and enzymatic properties of various sensing materials [e.g., metal–organic frameworks (MOFs), two-dimensional (2D) materials, conducting polymers, carbon nanotubes (CNTs)]. − However, one of the long-standing problems with these sensors is the lack of a suitable source of power.…”

“…The advancement in micro/nanotechnology has significantly enriched the quality and reliability of miniaturized sensors needed across multiple sectors including health monitoring, 1 , 2 agriculture, 3 aquaculture, 4 environment monitoring, 5 robotics, 6 − 8 rehabilitation, 9 , 10 automation, 11 space, 12 , 13 etc. Complemented by recent progress in flexible and printed electronics, these solutions continue to enrich the above areas and open new opportunities by allowing the detection of various chemical and biological analytes or parameters such as dopamine, 14 glucose, 15 tyrosine, 16 and creatinine 17 for human healthcare.…”

Aplicação de uma ferramenta de Business Intelligence para o diagnóstico do uso de água subterrâneas no estado do Paraná e em seus municípios

“…Associando extensos bancos de dados e integrando-os, a ferramenta vem facilitando a produção e análise de informações, e, com isso, promovendo uma melhor gestão nas empresas e instituições (GOHIL et al, 2021;PONTES et al, 2021). Sua aplicação para a gestão e o planejamento público, de recursos hídricos e de outras infraestruturas de saneamento já vem sendo realizada (SUN; SCAN-LON, 2019) e expandida com o uso do geoprocessamento (ANA, 2019;CHEN et al, 2022;CRAVERO et al, 2018;WICKRAMASU-RIYA et al, 2013).…”