In the last 10 years, development in robotics, computer vision, and sensor technology has provided new spectral remote sensing tools to capture unprecedented ultra-high spatial and high spectral resolution with unmanned aerial vehicles (UAVs). This development has led to a revolution in geospatial data collection in which not only few specialist data providers collect and deliver remotely sensed data, but a whole diverse community is potentially able to gather geospatial data that fit their needs. However, the diversification of sensing systems and user applications challenges the common application of good practice procedures that ensure the quality of the data. This challenge can only be met by establishing and communicating common procedures that have had demonstrated success in scientific experiments and operational demonstrations. In this review, we evaluate the state-of-the-art methods in UAV spectral remote sensing and discuss sensor technology, measurement procedures, geometric processing, and radiometric calibration based on the literature and more than a decade of experimentation. We follow the 'journey' of the reflected energy from the particle in the environment to its representation as a pixel in a 2D or 2.5D map, or 3D spectral point cloud. Additionally, we reflect on the current revolution in remote sensing, and identify trends, potential opportunities, and limitations.

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Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows

Adaptation of the dark spectrum fitting atmospheric correction for aquatic applications of the Landsat and Sentinel-2 archives

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 publications have demonstrated promising remote sensing models to estimate biological, chemical, and physical properties of inland waterbodies. Until recently, most of these publications focused largely on algorithm development as opposed to implementation of those algorithms to address specific science questions. This slow evolution contrasts with terrestrial and oceanic remote sensing, where methods development in the 1970s led to publications focused on understanding spatially expansive, complex processes as early as the mid-1980s. This review explores the progression of inland water quality remote sensing from methodological development to scientific applications. We use bibliometric analysis to assess overall patterns in the field and subsequently examine 236 key papers to identify trends in research focus and scale. The results highlight an initial 30 year period where the majority of publications focused on model development and validation followed by a spike in publications, beginning in the early-2000s, applying remote sensing models to analyze spatiotemporal trends, drivers, and impacts of changing water quality on ecosystems and human populations. Recent and emerging resources, including improved data availability and enhanced processing platforms, are enabling researchers to address challenging science questions and model spatiotemporally explicit patterns in water quality. Examination of the literature shows that the past 10–15 years has brought about a focal shift within the field, where researchers are using improved computing resources, datasets, and operational remote sensing algorithms to better understand complex inland water systems. Future satellite missions promise to continue these improvements by providing observational continuity with spatial/spectral resolutions ideal for inland waters.

https://eartharxiv.org/repository/object/645/download/1431/

Research Trends in the Use of Remote Sensing for Inland Water Quality Science: Moving Towards Multidisciplinary Applications

Ocean Optics Protocols for Satellite Ocean Color Sensor Validation. Revised

https://cyberleninka.org/article/n/110458.pdf

Landsat 8 Remote Sensing Reflectance (Rrs) Products: Evaluations, Intercomparisons, and Enhancements

A semi-analytical scheme to estimate Secchi-disk depth from Landsat-8 measurements

This study assesses the performance of the Geostationary Ocean Imager (GOCI) for mapping of suspended particulate matter in the Bohai Sea, a turbid water region. GOCI imagery for remote sensing reflectance and Total Suspended Solids (TSS) is analysed in detail for two days in June 2011 (8 images per day). Both instantaneous and daily composite maps are considered and a comparison is made with corresponding reflectance and TSS products from MODIS-AQUA. Results show TSS distributions corresponding to previous studies of the region. The advantage of the higher acquisition frequency (8 images/day instead of 1) offered by GOCI is clearly demonstrated in the daily composite which is more complete during this period of scattered but moving clouds. Consideration of temporal variation over the day indicates low natural variability but some artificial variability from processing errors — this analysis provides a first indication of how the higher frequency of data from geostationary ocean colour could lead to improved data quality control via temporal coherency outlier detection. While there is room for improvement on the GOCI calibration, atmospheric correction and retrieval algorithms, the current study suggests that the GOCI data can already be used now to study qualitatively sediment dynamics except in the extremely turbid waters which are masked out of the current dataset. In a wider context, it is considered that the technical challenges of geostationary ocean colour have been met by the GOCI concept, and, notwithstanding potential improvements on the concept and data processing methods, it is recommended that this mission serve as a model for future geostationary ocean colour sensors over Europe/Africa and the Americas.

https://odnature.naturalsciences.be/downloads/publications/2012_ruddick_osj_online.pdf

Variability of suspended particulate matter in the Bohai Sea from the geostationary Ocean Color Imager (GOCI)

Sensing an intense phytoplankton bloom in the western Taiwan Strait from radiometric measurements on a UAV

High-frequency observation of floating algae from AHI on Himawari-8

Creseis acicula is a pteropod species, widely distributed in the world’s ocean. The latest report of its bloom was in the Mediterranean Sea in 1990. On June 12, 2020, an unprecedented bloom of C. acicula was observed in Daya Bay, China, that lasted for over a month and caused economic losses. The maximum abundance of C. acicula was up to 5595 ind. m-3, 1000 times the local record high, and 10 times the highest number recorded elsewhere. Higher abundance (≥ 1000 ind. m-3) was found in the warm water of ≥ 30℃ along the southwest shore of Daya Bay. The initiation of the bloom matched a sharp increase of temperature and chlorophyll a and an abrupt decrease of salinity attributed to heavy rainfall that lasted for more than 20 days. Considering rising occurrences of extreme weather due to global change, possibilities of recurrent C. acicula blooms worldwide certainly deserve attention.

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Jing Yan

Papers

A semi-analytical scheme to estimate Secchi-disk depth from Landsat-8 measurements

Variability of suspended particulate matter in the Bohai Sea from the geostationary Ocean Color Imager (GOCI)

Sensing an intense phytoplankton bloom in the western Taiwan Strait from radiometric measurements on a UAV

High-frequency observation of floating algae from AHI on Himawari-8

An unprecedented outbreak of pelagic molluscs Creseis acicula in Daya Bay, South China Sea