Atmospheric correction of Landsat-8/OLI and Sentinel-2/MSI data using iCOR algorithm: validation for coastal and inland waters

Image correction for atmospheric effects (iCOR) is an atmospheric correction tool that can process satellite data collected over coastal, inland or transitional waters and land. The tool is adaptable with minimal effort to hyper- or multi-spectral radiometric sensors. By using a single atmospheric correction implementation for land and water, discontinuities in reflectance within one scene are reduced. iCOR derives aerosol optical thickness from the image and allows for adjacency correction, which is SIMilarity Environmental Correction (SIMEC) over water. This paper illustrates the performance of iCOR for Landsat-8 OLI and Sentinel-2 MSI data acquired over water. An intercomparison of water leaving reflectance between iCOR and Aerosol Robotic Network – Ocean Color provided a quantitative assessment of performance and produced coefficient of determination (R2) higher than 0.88 in all wavebands except the 865 nm band. For inland waters, the SIMEC adjacency correction improved results in the red-edge and near-infrared region in relation to optical in situ measurements collected during field campaigns

Análise espaço-temporal dos sedimentos em suspensão em reservatório de baixa concentração por meio de sensoriamento remoto

O estudo de pequenos reservatórios e com baixa concentração de sedimentos em suspensão (CSS) ainda é um desafio para o sensoriamento remoto. Neste trabalho estimamos a CSS a partir das propriedades óticas da água e de imagens orbitais. Realizamos campanhas em datas escolhidas em função do calendário de passagem dos sensores, sazonalidade das chuvas e hidrograma do reservatório para coleta de amostras de água superficial e espectrorradiometria de campo. A calibração entre a CSS e o comportamento espectral gerou modelos de estimativa de CSS a partir de dados MODIS e Landsat 8, permitindo investigação do seu comportamento temporal e espacial. O modelo MODIS gerou uma série temporal de CSS desde 2000 a 2017, apresentando R2 = 0,8105 e RMSE% = 39,91%. O modelo Landsat 8 permitiu a análise espacial da CSS, apresentando R2 = 0,8352 e RMSE% = 15,12%. A combinação dos modelos propostos permitiu a análise temporal e espacial da CSS e seus relacionamentos com o regime de chuvas e variação de cota do reservatório do Descoberto (DF). Os resultados demonstraram que o uso de dados orbitais complementam as informações da CSS obtidas pelos métodos tradicionais de coleta e análise de qualidade da água em reservatórios de baixa CSS.The study of small reservoirs with low suspended sediment concentration (CSS) is still a challenge for remote sensing. In this work we estimate CSS from the optical properties of water and orbital imagery. Campaigns were carried out at selected dates according to the calendar of sensor passages, rainfall seasonality and hydrograph of the reservoir for the collection of surface water samples and field spectroradiometry. The calibration between CSS and spectral behavior generated CSS estimation models from MODIS and Landsat 8 data, allowing investigation of their temporal and spatial behavior. The MODIS model generated a time series of CSS from 2000 to 2017, presenting R2 = 0.8105 and RMSE% = 39.91%. The Landsat 8 model allowed the spatial analysis of CSS, with R2 = 0.8352 and RMSE% = 15.12%. The combination of the proposed models allowed the temporal and spatial analysis of the CSS and its relationships with the rainfall regime and the quota variation of the Descoberto reservoir (DF). The results showed that the use of orbital data complements the CSS information obtained by the traditional methods of collecting and analyzing water quality in low CSS reservoirs

Fundamental remote sensing science research program. Part 1: Scene radiation and atmospheric effects characterization project

Brief articles summarizing the status of research in the scene radiation and atmospheric effect characterization (SRAEC) project are presented. Research conducted within the SRAEC program is focused on the development of empirical characterizations and mathematical process models which relate the electromagnetic energy reflected or emitted from a scene to the biophysical parameters of interest

Feasibility Study for an Aquatic Ecosystem Earth Observing System Version 1.2.

International audienceMany Earth observing sensors have been designed, built and launched with primary objectives of either terrestrial or ocean remote sensing applications. Often the data from these sensors are also used for freshwater, estuarine and coastal water quality observations, bathymetry and benthic mapping. However, such land and ocean specific sensors are not designed for these complex aquatic environments and consequently are not likely to perform as well as a dedicated sensor would. As a CEOS action, CSIRO and DLR have taken the lead on a feasibility assessment to determine the benefits and technological difficulties of designing an Earth observing satellite mission focused on the biogeochemistry of inland, estuarine, deltaic and near coastal waters as well as mapping macrophytes, macro-algae, sea grasses and coral reefs. These environments need higher spatial resolution than current and planned ocean colour sensors offer and need higher spectral resolution than current and planned land Earth observing sensors offer (with the exception of several R&D type imaging spectrometry satellite missions). The results indicate that a dedicated sensor of (non-oceanic) aquatic ecosystems could be a multispectral sensor with ~26 bands in the 380-780 nm wavelength range for retrieving the aquatic ecosystem variables as well as another 15 spectral bands between 360-380 nm and 780-1400 nm for removing atmospheric and air-water interface effects. These requirements are very close to defining an imaging spectrometer with spectral bands between 360 and 1000 nm (suitable for Si based detectors), possibly augmented by a SWIR imaging spectrometer. In that case the spectral bands would ideally have 5 nm spacing and Full Width Half Maximum (FWHM), although it may be necessary to go to 8 nm wide spectral bands (between 380 to 780nm where the fine spectral features occur -mainly due to photosynthetic or accessory pigments) to obtain enough signal to noise. The spatial resolution of such a global mapping mission would be between ~17 and ~33 m enabling imaging of the vast majority of water bodies (lakes, reservoirs, lagoons, estuaries etc.) larger than 0.2 ha and ~25% of river reaches globally (at ~17 m resolution) whilst maintaining sufficient radiometric resolution

Performance of Landsat-8 and Sentinel-2 Surface Reflectance Products for River Remote Sensing Retrievals of Chlorophyll-A and Turbidity

Rivers and other freshwater systems play a crucial role in ecosystems, industry, transportation and agriculture. Despite the more than 40 years of inland water observations made possible by optical remote sensing, a standardized reflectance product for inland waters is yet forthcoming. The aim of this work is to compare the standard USGS land surface reflectance product to two Landsat-8 and Sentinel-2 aquatic remote sensing reflectance products over the Amazon, Columbia and Mississippi rivers. Landsat-8 reflectance products from all three routines are then evaluated for their comparative performance in retrieving chlorophyll-a and turbidity in reference to shipborne, underway in situ validation measurements. The land surface product shows the best agreement (4 percent Mean Absolute Percent Difference) with field measurements of radiometry collected on the Amazon River and generates 36 percent higher reflectance values in the visible bands compared to aquatic methods (ACOLITE (Atmospheric Correction for OLI (Operational Land Imager) 'lite') and SeaDAS (Sea-viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System)) with larger differences between land and aquatic products observed in Sentinel-2 (0.01 per steraradian) compared to Landsat-8 (0.001 per steraradian). Choice of atmospheric correction routine can bias Landsat-8 retrievals of chlorophyll-a and turbidity by as much as 59 percent and 35 percent respectively. Using a more restrictive time window for matching in situ and satellite imagery can reduce differences by 531 percent depending on correction technique. This work highlights the challenges of satellite retrievals over rivers and underscores the need for future optical and biogeochemical research aimed at improving our understanding of the absorbing and scattering properties of river water and their relationships to remote sensing reflectance

Towards high fidelity mapping of global inland water quality using earth observation data

This body of work aims to contribute advancements towards developing globally applicable water quality retrieval models using Earth Observation data for freshwater systems. Eutrophication and increasing prevalence of potentially toxic algal blooms among global inland water bodies have become a major ecological concersn and require direct attention. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products. This study provides one of the first assessments of the state-ofthe-art for trophic status (chlorophyll-a) retrievals for small water bodies using Sentinel-3 Ocean and Land Color Imager (OLCI). Multiple fieldwork campaigns were undertaken for the collection of common aquatic biogeophysical and bio-optical parameters that were used to validate current atmospheric correction and chlorophyll-a retrieval algorithms. The study highlighted the difficulties of obtaining robust retrieval estimates from a coarse spatial resolution sensor from highly variable eutrophic water bodies. Atmospheric correction remains a difficult challenge to operational freshwater monitoring, however, the study further validated previous work confirming applicability of simple, empirically derived retrieval algorithms using top-of-atmosphere data. The apparent scarcity of paired in-situ optical and biogeophysical data for productive inland waters also hinders our capability to develop and validate robust retrieval algorithms. Radiative transfer modeling was used to fill this gap through the development of a novel synthetic dataset of top-of-atmosphere and bottom-of-atmosphere reflectances, which attempts to encompass the immense natural optical variability present in inland waters. Novel aspects of the synthetic dataset include: 1) physics-based, two-layered, size and type specific phytoplankton IOPs for mixed eukaryotic/cyanobacteria 6 assemblages, 2) calculations of mixed assemblage chl-a fluorescence, 3) modeled phycocyanin concentration derived from assemblage based phycocyanin absorption, 4) and paired sensor-specific TOA reflectances which include optically extreme cases and contribution of green vegetation adjacency. The synthetic bottom-of-atmosphere reflectance spectra were compiled into 13 distinct optical water types similar to those discovered using in-situ data. Inspection showed similar relationships and ranges of concentrations and inherent optical properties of natural waters. This dataset was used to calculate typical surviving water-leaving signal at top-of-atmosphere, as well as first order calculations of the signal-to-noise-ratio (SNR) for the various optical water types, a first for productive inland waters, as well as conduct a sensitivity analysis of cyanobacteria detection from top-of-atmosphere. Finally, the synthetic dataset was used to train and test four state-of-the-art machine learning architectures for multi-parameter retrieval and cross-sensor capability. Initial results provide reliable estimates of water quality parameters and inherent optical properties over a highly dynamic range of water types, at various spectral and spatial sensor resolutions. It is hoped the results of this work incrementally improves inland water Earth observation on multiple aspects of the forward and inverse modelling process, and provides an improvement in our capabilities for routine, global monitoring of inland water quality

The Use of Landsat 8 for Monitoring of Fresh and Coastal Waters

The most interaction between humankind and water occurs in coastal and inland waters (Case 2 waters) at a scale of tens or hundred of meters, but there is not yet an ocean color product (e.g. chlorophyll-a product) at this spatial scale. Landsat 8 could potentially address the remote sensing of these kinds of waters due to its improved features. This work presents an approach to obtain the color producing agents (CPAs) chlorophyll-a, colored dissolved organic material (CDOM) and suspended material (SM) from water bodies using Landsat 8. Adequate atmospheric correction becomes an important first step to accurately retrieving water parameters since the sensor-reaching signal due to water is very small when compared to the signal due to the atmospheric effects. We developed the model-based empirical line method (MoB-ELM) atmospheric correction method. The Mob-ELM employs pseudo invariant feature (PIF) pixels extracted from a reflectance product along with the in-water radiative transfer model HydroLight. We used a look-up-table-based (LUT-based) inversion methodology to simultaneously retrieve CPAs. The LUT of remote-sensing reflectance spectra was created in Hydrolight using inherent optical properties (IOPs) measured in the field. The retrieval algorithm was applied over three Landsat 8 scenes. The CPA concentration maps exhibit expected trends of low concentrations in clear waters and higher concentrations in turbid waters. We estimated a normalized root mean squared error (NRMSE) of about 14% for Chlorophyll-a, 11% for the total suspended solid (TSS), and 7% for colored dissolved organic matter (CDOM) when compared with in situ data. These results demonstrate that the developed algorithm allows the simultaneous mapping of concentration of all CPAs in Case 2 waters and over areas where the standard algorithms are not available due to spatial resolution. Therefore, this study shows that the Landsat 8 satellite can be utilized over Case 2 waters as long as a careful atmospheric correction is applied and IOPs are known