Evaluating Radiometric Sensitivity of LandSat 8 Over Coastal-Inland Waters

The operational Land Imager (OLI) aboard Landsat 8 was launched in February 2013 to continue the Landsat's mission of monitoring earth resources at relatively high spatial resolution. Compared to Landsat heritage sensors, OLI has an additional 443-nm band (termed coastal/aerosol (CA) band), which extends its potential for mapping/monitoring water quality in coastal/inland waters. In addition, OLI's pushbroom design allows for longer integration time and, as a result, higher signal-to-noise ratio (SNR). Using a series of radiative transfer simulations, we provide insights into the radiometric sensitivity of OLI when studying coastal/inland waters. This will address how the changes in water constituents manifest at top-of-atmosphere (TOA) and whether the changes are resolvable at TOA (focal plane) relative to OLI's overall noise

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

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

A chlorophyll-a algorithm for Landsat-8 based on mixture density networks

Material suplementario disponible en:Retrieval of aquatic biogeochemical variables, such as the near-surface concentration of chlorophyll-a (Chla) in inland and coastal waters via remote observations, has long been regarded as a challenging task. This manuscript applies Mixture Density Networks (MDN) that use the visible spectral bands available by the Operational Land Imager (OLI) aboard Landsat-8 to estimate Chla. We utilize a database of co-located in situ radiometric and Chla measurements (N = 4,354), referred to as Type A data, to train and test an MDN model (MDNA). This algorithm’s performance, having been proven for other satellite missions, is further evaluated against other widely used machine learning models (e.g., support vector machines), as well as other domain-specific solutions (OC3), and shown to offer significant advancements in the field. Our performance assessment using a held-out test data set suggests that a 49% (median) accuracy with near-zero bias can be achieved via the MDNA model, offering improvements of 20 to 100% in retrievals with respect to other models. The sensitivity of the MDNA model and benchmarking methods to uncertainties from atmospheric correction (AC) methods, is further quantified through a semi-global matchup dataset (N = 3,337), referred to as Type B data. To tackle the increased uncertainties, alternative MDN models (MDNB) are developed through various features of the Type B data (e.g., Rayleigh-corrected reflectance spectra ρs ). Using held-out data, along with spatial and temporal analyses, we demonstrate that these alternative models show promise in enhancing the retrieval accuracy adversely influenced by the AC process. Results lend support for the adoption of MDNB models for regional and potentially global processing of OLI imagery, until a more robust AC method is developed. Index Terms—Chlorophyll-a, coastal water, inland water, Landsat-8, machine learning, ocean color, aquatic remote sensing

An Integrated physics-based approach to demonstrate the potential of the Landsat Data Continuity Mission (LDCM) for monitoring coastal/inland waters

Monitoring coastal or inland waters, recognized as case II waters, using the existing Landsat technology is somewhat restricted because of its low Signal-to-Noise ratio (SNR) as well as its relatively poor radiometric resolution. As a primary task, we introduce a novel technique, which integrates the Landsat-7 data as a surrogate for LDCM with a 3D hydrodynamic model to monitor the dynamics of coastal waters near river discharges as well as in a small lake environment. The proposed approach leverages both the thermal and the reflective Landsat-7 imagery to calibrate the model and to retrieve the concentrations of optically active components of the water. To do so, the model is first calibrated by optimizing its thermal outputs with the surface temperature maps derived from the Landsat-7 data. The constituent retrieval is conducted in the second phase where multiple simulated concentration maps are provided to an in-water radiative transfer code (Hydrolight) to generate modeled surface reflectance maps. Prior to any remote sensing task, one has to ensure that a dataset comes from a well-calibrated imaging system. Although the calibration status of Landsat-7 has been regularly monitored over multiple desert sites, it was desired to evaluate its performance over dark waters relative to a well-calibrated instrument designed specifically for water studies. In the light of this, several Landsat- 7 images were cross-calibrated against the Terra-MODIS data over deep, dark waters whose optical properties remain relatively stable. This study is intended to lay the groundwork and provide a reference point for similar studies planned for the new Landsat. In an independent case study, the potential of the new Landsat sensor was examined using an EO-1 dataset and applying a spectral optimization approach over case II waters. The water constituent maps generated from the EO-1 imagery were compared against those derived from Landsat-7 to fully analyze the improvement levels pertaining to the new Landsat\u27s enhanced features in a water constituent retrieval framework

Measuring freshwater aquatic ecosystems: The need for a hyperspectral global mapping satellite mission

AbstractFreshwater ecosystems underpin global water and food security, yet are some of the most endangered ecosystems in the world because they are particularly vulnerable to land management change and climate variability. The US National Research Council's guidance to NASA regarding missions for the coming decade includes a polar orbiting, global mapping hyperspectral satellite remote sensing mission, the Hyperspectral Infrared Imager (HyspIRI), to make quantitative measurements of ecosystem change. Traditionally, freshwater ecosystems have been challenging to measure with satellite remote sensing because they are small and spatially complex, require high fidelity spectroradiometry, and are best described with biophysical variables derived from high spectral resolution data. In this study, we evaluate the contribution of a hyperspectral global mapping satellite mission to measuring freshwater ecosystems. We demonstrate the need for such a mission, and evaluate the suitability and gaps, through an examination of the measurement resolution issues impacting freshwater ecosystem measurements (spatial, temporal, spectral and radiometric). These are exemplified through three case studies that use remote sensing to characterize a component of freshwater ecosystems that drive primary productivity. The high radiometric quality proposed for the HyspIRI mission makes it uniquely well designed for measuring freshwater ecosystems accurately at moderate to high spatial resolutions. The spatial and spectral resolutions of the HyspIRI mission are well suited for the retrieval of multiple biophysical variables, such as phycocyanin and chlorophyll-a. The effective temporal resolution is suitable for characterizing growing season wetland phenology in temperate regions, but may not be appropriate for tracking algal bloom dynamics, or ecosystem responses to extreme events in monsoonal regions. Global mapping missions provide the systematic, repeated measurements necessary to measure the drivers of freshwater biodiversity change. Archival global mapping missions with open access and free data policies increase end user uptake globally. Overall, an archival, hyperspectral global mapping mission uniquely meets the measurement requirements of multiple end users for freshwater ecosystem science and management

HIRIS (High-Resolution Imaging Spectrometer: Science opportunities for the 1990s. Earth observing system. Volume 2C: Instrument panel report

The high-resolution imaging spectrometer (HIRIS) is an Earth Observing System (EOS) sensor developed for high spatial and spectral resolution. It can acquire more information in the 0.4 to 2.5 micrometer spectral region than any other sensor yet envisioned. Its capability for critical sampling at high spatial resolution makes it an ideal complement to the MODIS (moderate-resolution imaging spectrometer) and HMMR (high-resolution multifrequency microwave radiometer), lower resolution sensors designed for repetitive coverage. With HIRIS it is possible to observe transient processes in a multistage remote sensing strategy for Earth observations on a global scale. The objectives, science requirements, and current sensor design of the HIRIS are discussed along with the synergism of the sensor with other EOS instruments and data handling and processing requirements

Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecological Applications 28 (2018): 749-760, doi: 10.1002/eap.1682.The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite‐based sensors can repeatedly record the visible and near‐infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100‐m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short‐wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14‐bit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3‐d repeat low‐Earth orbit could sample 30‐km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.National Center for Ecological Analysis and Synthesis (NCEAS); National Aeronautics and Space Administration (NASA) Grant Numbers: NNX16AQ34G, NNX14AR62A; National Ocean Partnership Program; NOAA US Integrated Ocean Observing System/IOOS Program Office; Bureau of Ocean and Energy Management Ecosystem Studies program (BOEM) Grant Number: MC15AC0000

The suitability of using Landsat TM-5 Images for estimating chromophoric dissolved organic matter in subarctic Lakes

Recent trends of permafrost thawing in the subarctic are expected to cause increased release of dissolved organic carbon (DOC) to inland waters, which might have cascading effects on downstream aquatic ecosystems and release of CO2 to the atmosphere. This study therefore aimed at evaluating the applicability of an empirical band ratio algorithm for estimating chromophoric dissolved organic matter (CDOM; a proxy for DOC) from the easily accessible satellite images Landsat TM-5, to counter the inaccessibility of the region in general. The study targeted 14 smaller lakes in the Stordalen catchment in northern Sweden where values of CDOM absorbance had been obtained from the summer of 2009 that could be used to evaluate algorithm suitability. The satellite image type and algorithm have been successfully applied to predict CDOM in previous studies of lakes with relatively high absorbance, but in this study no significant correlations were found between the in situ measured and the remote sensing estimates for the studied lakes (in situ aCDOM (440) = 0.29 - 1.22 m-1; R2 ≤ 0.21); except for when lakes with certain characteristics were tested separately (shallow lakes R2 = 0.86). It was concluded that Landsat TM-5 images are not generally suitable for estimating CDOM in the Stordalen area. However higher quality satellite products probably would; since with a higher ground-, spectral- and radiometric resolution some disturbances could be reduced, more lakes could be included in the study and they would be more accurately recorded. Nonetheless more in situ collected data is needed for supporting the discussed deductions and for adaptive algorithm modifications

Satellite Sensor Requirements for Monitoring Essential Biodiversity Variables of Coastal Ecosystems

The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite-based sensors can repeatedly record the visible and near-infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100-m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short-wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14-bit digitization, absolute radiometric calibratio