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NASA NTRS2023

Estimating the concentration of total suspended solids in inland and coastal waters from Sentinel-2 MSI: A semi-analytical approach

Inland and coastal waters provide key ecosystem services and are closely linked to human well-being. In this study, we propose a semi-analytical method, which can be applied to Sentinel-2 MultiSpectral Instrument (MSI) images to retrieve high spatial-resolution total suspended solids (TSS) concentration in a broad spectrum of aquatic ecosystems ranging from clear to extremely turbid waters. The presented approach has four main steps. First, the remote sensing reflectance ( R rs ) at a band lacking in MSI (620 nm) is estimated through an empirical relationship from R rs at 665 nm. Second, waters are classified into four types (clear, moderately turbid, highly turbid, and extremely turbid). Third, semi-analytical algorithms are used to estimate the particulate backscattering coefficient ( b bp ) at a reference band depending on the water types. Last, TSS is estimated from bbp at the reference band. Validation and comparison of the proposed method with three existing methods are performed using a simulated dataset ( N = 1000), an in situ dataset collected from global inland and coastal waters ( N = 1265) and satellite matchups ( N = 40). Results indicate that the proposed method can improve TSS estimation and provide accurate retrievals of TSS from all three datasets, with a median absolute percentage error (MAPE) of 14.88 %, 31.50 % and 41.69 % respectively. We also present comparisons of TSS mapping between the Sentinel-3 Ocean and Land Colour Instrument (OLCI) and MSI in Lake Kasumigaura, Japan and the Tagus Estuary, Portugal. Results clearly demonstrate the advantages of using MSI for TSS monitoring in small water bodies such as rivers, river mouths and other nearshore waters. MSI can provide more detailed and realistic TSS estimates than OLCI in these water bodies. The proposed TSS estimation method was applied to MSI images to produce TSS time-series in Lake Kasumigaura, which showed good agreements with in situ and OLCI-derived TSS time-series.

Sentinel-2↗
NASA NTRS1999

Introduction to this Special Issue on Geostatistics and Scaling of Remote Sensing

The germination of this special PE&RS issue began at the Royal Geographical Society (with the Institute of British Geographers)(RCS-IBC) annual meeting in January, 1997 held at the University of Exeter in Exeter, England. The cold and snow of an England winter were greatly tempered by the friendly and cordial discussions that ensued at the meeting on possible ways to foster both dialog and research across "the Big Pond" between geographers in the US and the UK on the use of geostatistics and geospatial techniques for remote sensing of land surface processes. It was decided that one way to stimulate and enhance cooperation on the application of geostatistics and geospatial methods in remote sensing was to hold parallel sessions on these topics at appropriate meeting venues in 1998 in both the US and the UK Selected papers given at these sessions would be published as a special issue of PE&RS on the US side, and as a special issue of Computers and Geosciences (C&G) on the UK side, to highlight the commonality in research on geostatistics and geospatial methods in remote sensing and spatial data analysis on both sides of the Atlantic Ocean. As a consequence, a session on "Ceostatistics and Geospatial Techniques for Remote Sensing of Land Surface Processes" was held at the Association of American Geographers (AAG) annual meeting in Boston, Massachusetts in March, 1998, sponsored by the AAG's Remote Sensing Specialty Group (RSSG). A similar session was held at the RGS-IBG annual meeting in Guildford, Surrey, England in January 1998, organized by the Modeling and Advanced Techniques Special Interest Group (MAT SIG) of the Remote Sensing Society (RSS). The six papers that in part, comprise this issue of PE&RS, are the US complement to such a dual journal publication effort. Both of us are co-editors of each of the journal special issues, with the lead editor of each journal being from their respective side of the Atlantic where the journals are published. The special issue of C&G that constitutes the other half of this co-edited journal series will be published in early 1999, with 3 papers by US authors being published along with 6 papers authored by individuals from the UK and other places in Europe.

Quattrochi, Dale A.↗
NASA NTRS1994

Visible-infrared remote-sensing model and applications for ocean waters

Remote sensing has become important in the ocean sciences, especially for research involving large spatial scales. To estimate the in-water constituents through remote sensing, whether carried out by satellite or airplane, the signal emitted from beneath the sea surface, the so called water-leaving radiance (L(w)), is of prime importance. The magnitude of L(w) depends on two terms: one is the intensity of the solar input, and the other is the reflectance of the in-water constituents. The ratio of the water-leaving radiance to the downwelling irradiance (E(d)) above the sear surface (remote-sensing reflectance, R(sub rs)) is independent of the intensity of the irradiance input, and is largely a function of the optical properties of the in-water constituents. In this work, a model is developed to interpret r(sub rs) for ocean water in the visible-infrared range. In addition to terms for the radiance scattered from molecules and particles, the model includes terms that describe contributions from bottom reflectance, fluorescence of gelbstoff or colored dissolved organic matter (CDOM), and water Raman scattering. By using this model, the measured R(sub rs) of waters from the West Florida Shelf to the Mississippi River plume, which covered a (concentration of chlorophyll a) range of 0.07 - 50 mg/cu m, were well interpreted. The average percentage difference (a.p.d.) between the measured and modeled R(sub rs) is 3.4%, and, for the shallow waters, the model-required water depth is within 10% of the chart depth. Simple mathematical simulations for the phytoplankton pigment absorption coefficient (a(sub theta)) are suggested for using the R(sub rs) model. The inverse problem of R(sub rs), which is to analytically derive the in-water constituents from R(sub rs) data alone, can be solved using the a(sub theta) functions without prior knowledge of the in-water optical properties. More importantly, this method avoids problems associated with a need for knowledge of the shape and value of the chlorophyll-specific absorption coefficient. The simulation was tested for a wide range of water types, including waters from Monterey Bay, the West Florida Shelf, and the Mississippi River plume. Using the simulation, the R(sub rs)-derived in-water absorption coefficients were consistent with the values from in-water measurements (r(exp 2) greater than 0.94, slope approximately 1.0). In the remote-sensing applications, a new approach is suggested for the estimation of primary production based on remote sensing. Using this approach, the calculated primary production (PP) values based upon remotely sensed data were very close to the measured values for the euphotic zone (r(exp 2) = 0.95, slope 1.26, and 32% average difference), while traditional, pigment-based PP model provided values only one-third the size of the measured data. This indicates a potential to significantly improve the accuracy of the estimation of primary production based upon remote sensing.

Lee, Zhongping↗
NASA NTRS2007

Review of Remote Sensing Needs and Applications in Africa

Remote sensing data has had an important role in identifying and responding to inter-annual variations in the African environment during the past three decades. As a largely agricultural region with diverse but generally limited government capacity to acquire and distribute ground observations of rainfall, temperature and other parameters, remote sensing is sometimes the only reliable measure of crop growing conditions in Africa. Thus, developing and maintaining the technical and scientific capacity to analyze and utilize satellite remote sensing data in Africa is critical to augmenting the continent's local weather/climate observation networks as well as its agricultural and natural resource development and management. The report Review of Remote Sensing Needs and Applications in Africa' has as its central goal to recommend to the US Agency for International Development an appropriate approach to support sustainable remote sensing applications at African regional remote sensing centers. The report focuses on "RS applications" to refer to the acquisition, maintenance and archiving, dissemination, distribution, analysis, and interpretation of remote sensing data, as well as the integration of interpreted data with other spatial data products. The report focuses on three primary remote sensing centers: (1) The AGRHYMET Regional Center in Niamey, Niger, created in 1974, is a specialized institute of the Permanent Interstate Committee for Drought Control in the Sahel (CILSS), with particular specialization in science and techniques applied to agricultural development, rural development, and natural resource management. (2) The Regional Centre for Maiming of Resources for Development (RCMRD) in Nairobi, Kenya, established in 1975 under the auspices of the United Nations Economic Commission for Africa and the Organization of African Unity (now the African Union), is an intergovernmental organization, with 15 member states from eastern and southern Africa. (3) The Regional Remote Sensing Unit (RRSU) in Gaborone, Botswana, began work in June 1988 and operates under the Agriculture Information Management System (AIMS), as part of the Food, Agriculture and Natural Resources (FANR) Directorate, based at the Southern Africa Development Community (SADC) Secretariat.

Brown, Molly E.↗
NASA NTRS2019

Approach for Propagating Radiometric Data Uncertainties Through NASA Ocean Color Algorithms

Spectroradiometric satellite observations of the ocean are commonly referred to as “ocean color” remote sensing. NASA has continuously collected, processed, and distributed ocean color datasets since the launch of the Sea-viewing Wide-field-of-view Sensor (SeaWiFS) in 1997. While numerous ocean color algorithms have been developed in the past two decades that derive geophysical data products from sensor-observed radiometry, few papers have clearly demonstrated how to estimate measurement uncertainty in derived data products. As the uptake of ocean color data products continues to grow with the launch of new and advanced sensors, it is critical that pixel-by-pixel data product uncertainties are estimated during routine data processing. Knowledge of uncertainties can be used when studying long-term climate records, or to assist in the development and performance appraisal of bio-optical algorithms. In this method paper we provide a comprehensive overview of how to formulate first-order first-moment (FOFM) calculus for propagating radiometric uncertainties through a selection of bio-optical models. We demonstrate FOFM uncertainty formulations for the following NASA ocean color data products: chlorophyll-a pigment concentration (Chl), the diffuse attenuation coefficient at 490 nm (K(sub d,490)), particulate organic carbon (POC), normalized fluorescent line height (nflh), and inherent optical properties (IOPs). Using a quality-controlled in situ hyperspectral remote sensing reflectance (R(sub rs,i)) dataset, we show how computationally inexpensive, yet algebraically complex, FOFM calculations may be evaluated for correctness using the more computationally expensive Monte Carlo approach. We compare bio-optical product uncertainties derived using our test R(sub rs) dataset assuming spectrally-flat, uncorrelated relative uncertainties of 1, 5, and 10%. We also consider spectrally dependent, uncorrelated relative uncertainties in R(sub rs). The importance of considering spectral covariances in R(sub rs), where practicable, in the FOFM methodology is highlighted with an example SeaWiFS image. We also present a brief case study of two POC algorithms to illustrate how FOFM formulations may be used to construct measurement uncertainty budgets for ecologically-relevant data products. Such knowledge, even if rudimentary, may provide useful information to end-users when selecting data products or when developing their own algorithms.

Bio-optics↗
NASA NTRS2017

Revisiting Short-Wave-Infrared (SWIR) Bands for Atmospheric Correction in Coastal Waters

The shortwave infrared (SWIR) bands on the existing Earth Observing missions like MODIS have been designed to meet land and atmospheric science requirements. The future geostationary and polar-orbiting ocean color missions, however, require highly sensitive SWIR bands (greater than 1550nm) to allow for a precise removal of aerosol contributions. This will allow for reasonable retrievals of the remote sensing reflectance (R(sub rs)) using standard NASA atmospheric corrections over turbid coastal waters. Design, fabrication, and maintaining high-performance SWIR bands at very low signal levels bear significant costs on dedicated ocean color missions. This study aims at providing a full analysis of the utility of alternative SWIR bands within the 1600nm atmospheric window if the bands within the 2200nm window were to be excluded due to engineering/cost constraints. Following a series of sensitivity analyses for various spectral band configurations as a function of water vapor amount, we chose spectral bands centered at 1565 and 1675nm as suitable alternative bands within the 1600nm window for a future geostationary imager. The sensitivity of this band combination to different aerosol conditions, calibration uncertainties, and extreme water turbidity were studied and compared with that of all band combinations available on existing polar-orbiting missions. The combination of the alternative channels was shown to be as sensitive to test aerosol models as existing near-infrared (NIR) band combinations (e.g., 748 and 869nm) over clear open ocean waters. It was further demonstrated that while in extremely turbid waters the 1565/1675 band pair yields R(sub rs) retrievals as good as those derived from all other existing SWIR band pairs (greater than 1550nm), their total calibration uncertainties must be less than 1% to meet current science requirements for ocean color retrievals (i.e., delta R(sub rs) (443) less than 5%). We further show that the aerosol removal using the NIR and SWIR bands (available on the existing polar-orbiting missions) can be very sensitive to calibration uncertainties. This requires the need for monitoring the calibration of these bands to ensure consistent multi-mission ocean color products in coastal/inland waters.

atmospheric correction↗
NASA NTRS2020

Robust Algorithm for Estimating Total Suspended Solids (TSS) in Inland and Nearshore Coastal Waters

One of the challenging tasks in modern aquatic remote sensing is the retrieval of near-surface concentrations of Total Suspended Solids (TSS). This study aims to present a Statistical, inherent Optical property (IOP) -based, and muLti-conditional Inversion proceDure (SOLID) for enhanced retrievals of satellite-derived TSS under a wide range of in-water bio-optical conditions in rivers, lakes, estuaries, and coastal waters. In this study, using a large in situ database (N > 3500), the SOLID model is devised using a three-step procedure: (a) water-type classification of the input remote sensing reflectance (R(sub rs)), (b) retrieval of particulate backscattering (b(sub bp)) in the red or near-infrared (NIR) regions using semi-analytical, machine-learning, and empirical models, and (c) estimation of TSS from b(sub bp) via water-type-specific empirical models. Using an independent subset of our in situ data (N = 2729) with TSS ranging from 0.1 to 2626.8 [g/m (exp 3)], the SOLID model is thoroughly examined and compared against several state-of-the-art algorithms (Miller and McKee, 2004; Nechad et al., 2010; Novoa et al., 2017; Ondrusek et al., 2012; Petus et al., 2010). We show that SOLID outperforms all the other models to varying degrees, i.e., from 10 to > 100%, depending on the statistical attributes (e.g., global versus water-type-specific metrics). For demonstration purposes, the model is implemented for images acquired by the MultiSpectral Imager aboard Sentinel-2A/B over the Chesapeake Bay, San-Francisco-Bay-Delta Estuary, Lake Okeechobee, and Lake Taihu. To enable generating consistent, multimission TSS products, its performance is further extended to, and evaluated for, other missions, such as the Ocean and Land Color Instrument (OLCI), Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and Operational Land Imager (OLI). Sensitivity analyses on uncertainties induced by the atmospheric correction indicate that 10% uncertainty in Rrs leads to < 20% uncertainty in TSS retrievals from SOLID. While this study suggests that SOLID has a potential for producing TSS products in global coastal and inland waters, our statistical analysis certainly verifies that there is still a need for improving retrievals across a wide spectrum of particle loads.

Total suspended solids↗
NASA NTRS2001

Results of the Second SeaWiFS Data Analysis Round Robin, March 2000 (DARR-00)

The accurate determination of upper ocean apparent optical properties (AOPs) is essential for the vicarious calibration of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) instrument and the validation of the derived data products. To evaluate the importance of data analysis methods upon derived AOP values, the Second Data Analysis Round Robin (DARR-00) activity was planned during the latter half of 1999 and executed during March 2000. The focus of the study was the intercomparison of several standard AOP parameters: (1) the upwelled radiance immediately below the sea surface, L(sub u)(0(-),lambda); (2) the downward irradiance immediately below the sea surface, E(sub d)(0(-),lambda); (3) the diffuse attenuation coefficients from the upwelling radiance and the downward irradiance profiles, L(sub L)(lambda) and K(sub d)(lambda), respectively; (4) the incident solar irradiance immediately above the sea surface, E(sub d)(0(+),lambda); (5) the remote sensing reflectance, R(sub rs)(lambda); (6) the normalized water-leaving radiance, [L(sub W)(lambda)](sub N); (7) the upward irradiance immediately below the sea surface, E(sub u)(0(-)), which is used with the upwelled radiance to derive the nadir Q-factor immediately below the sea surface, Q(sub n)(0(-),lambda); and (8) ancillary parameters like the solar zenith angle, theta, and the total chlorophyll concentration, C(sub Ta), derived from the optical data through statistical algorithms. In the results reported here, different methodologies from three research groups were applied to an identical set of 40 multispectral casts in order to evaluate the degree to which differences in data analysis methods influence AOP estimation, and whether any general improvements can be made. The overall results of DARR-00 are presented in Chapter 1 and the individual methods used by the three groups and their data processors are presented in Chapters 2-4.

Hooker, Stanford B.↗
NASA NTRS2004

High Spectral Resolution MODIS Algorithms for Ocean Chlorophyll in Case II Waters

The Case 2 chlorophyll a algorithm is based on a semi-analytical, bio-optical model of remote sensing reflectance, R(sub rs)(lambda), where R(sub rs)(lambda) is defined as the water-leaving radiance, L(sub w)(lambda), divided by the downwelling irradiance just above the sea surface, E(sub d)(lambda,0(+)). The R(sub rs)(lambda) model (Section 3) has two free variables, the absorption coefficient due to phytoplankton at 675 nm, a(sub phi)(675), and the absorption coefficient due to colored dissolved organic matter (CDOM) or gelbstoff at 400 nm, a(sub g)(400). The R(rs) model has several parameters that are fixed or can be specified based on the region and season of the MODIS scene. These control the spectral shapes of the optical constituents of the model. R(sub rs)(lambda(sub i)) values from the MODIS data processing system are placed into the model, the model is inverted, and a(sub phi)(675), a(sub g)(400) (MOD24), and chlorophyll a (MOD21, Chlor_a_3) are computed. Algorithm development is initially focused on tropical, subtropical, and summer temperate environments, and the model is parameterized in Section 4 for three different bio-optical domains: (1) high ratios of photoprotective pigments to chlorophyll and low self-shading, which for brevity, we designate as 'unpackaged'; (2) low ratios and high self-shading, which we designate as 'packaged'; and (3) a transitional or global-average type. These domains can be identified from space by comparing sea-surface temperature to nitrogen-depletion temperatures for each domain (Section 5). Algorithm errors of more than 45% are reduced to errors of less than 30% with this approach, with the greatest effect occurring at the eastern and polar boundaries of the basins. Section 6 provides an expansion of bio-optical domains into high-latitude waters. The 'fully packaged' pigment domain is introduced in this section along with a revised strategy for implementing these variable packaging domains. Chlor_a_3 values derived semi-analytically and Chlor_a_2 values derived empirically using the O Reilly et al. OC3M algorithm from MODIS Terra radiances are compared to field chlorophyll-a concentrations in Sections 7 and 8.

Carder, Kendall L.↗
NASA NTRS2005

Variability of particulate organic carbon concentration in the north polar Atlantic based on ocean color observations with Sea-viewing Wide Field-of-view Sensor (SeaWiFS)

We use satellite data from Sea-viewing Wide Field-of-view Sensor (SeaWiFS) to investigate distributions of particulate organic carbon (POC) concentration in surface waters of the north polar Atlantic Ocean during the spring summer season (April through August) over a 6-year period from 1998 through 2003. By use of field data collected at sea, we developed regional relationships for the purpose of estimating POC from remote-sensing observations of ocean color. Analysis of several approaches used in the POC algorithm development and match-up analysis of coincident in situ derived and satellite-derived estimates of POC resulted in selection of an algorithm that is based on the blue-to-green ratio of remote-sensing reflectance R(sub rs) (or normalized water-leaving radiance L(sub wn)). The application of the selected algorithm to a 6-year record of SeaWiFS monthly composite data of L(sub wn) revealed patterns of seasonal and interannual variability of POC in the study region. For example, the results show a clear increase of POC throughout the season. The lowest values, generally less than 200 mg per cubic meters, and at some locations often less than 50 mg per cubic meters, were observed in April. In May and June, POC can exceed 300 or even 400 mg per cubic meters in some parts of the study region. Patterns of interannual variability are intricate, as they depend on the geographic location within the study region and particular time of year (month) considered. By comparing the results averaged over the entire study region and the entire season (April through August) for each year separately, we found that the lowest POC occurred in 2001 and the highest POC occurred in 2002 and 1999.

Stramska, Malgorzata↗
NASA NTRS2006

Bio-Optical Properties and Ocean Color Algorithms for Coastal Waters Influenced by the Mississippi River During a Cold Front Passage

During the passage of a cold front in March 2002, bio-optical properties examined in coastal waters impacted by the Mississippi River indicated westward advective flows and increasing river discharge containing a larger nonalgal particle content contributed significantly to surface optical variability. A comparison of seasonal data from three cruises indicated spectral models of absorption and scattering to be generally consistent with other coastal environments, while their parameterization in terms of chlorophyll a concentration (Chl) showed seasonal variability. The exponential slope of the colored dissolved organic matter (CDOM) averaged 0.0161 plus or minus 0.00054 per nanometer, and for nonalgal absorption it averaged 0.011 per nanometer with deviations from general trends observed due to anomalous water properties. Although the phytoplankton specific absorption coefficients varied over a wide range (0.02 to 0.1 square meters (mg Chl) sup -1)) being higher in offshore surface waters, values of phytoplankton absorption spectra at the SeaWiFS wavebands were highly correlated to modeled values. The normalized scattering spectral shapes and the mean spectrum were in agreement to observations in other coastal waters, while the backscattering ratios were on average lower in phytoplankton dominated surface waters (0.0101 plus or minus 0.002) and higher in near-bottom waters (0.0191 plus or minus 0.0045) with low Chl. Average percent differences in remote sensing reflectance R (sub rs) derived form modeled and in-eater radiometric measurements were highest in the blue wavebands (52%) and at sampling stations with a ore stratified water column. Estimates of Chl and CDOM absorption derived from SeaWiFS images generated using regional empirical algorithms were highly correlated to in situ data.

D'Sa Eurico J.↗
NASA NTRS2008

Remote Sensing of the Absorption Coefficients and Chlorophyll a Concentration in the U.S. Southern Middle Atlantic Bight from SeaWiFS and MODIS-Aqua

At present, satellite remote sensing of coastal water quality and constituent concentration is subject to large errors as compared to the capability of satellite sensors in oceanic waters. In this study, field measurements collected on a series of cruises within U.S. southern Middle Atlantic Bight (SMAB) were applied to improve retrievals of satellite ocean color products in order to examine the factors that regulate the bio-optical properties within the continental shelf waters of the SMAB. The first objective was to develop improvements in satellite retrievals of absorption coefficients of phytoplankton (a(sub ph)), colored dissolved organic matter (CDOM) (a(sub g)), non-pigmented particles (a(sub d)), and non-pigmented particles plus CDOM (a(sub dg)), and chlorophyll a concentration ([Chl_a]). Several algorithms were compared to derive constituent absorption coefficients from remote sensing reflectance (R(sub rs)) ratios. The validation match-ups showed that the mean absolute percent differences (MAPD) were typically less than 35%, although higher errors were found for a(sub d) retrievals. Seasonal and spatial variability of satellite-derived absorption coefficients and [Chl_a] was apparent and consistent with field data. CDOM is a major contributor to the bio-optical properties of the SMAB, accounting for 35-70% of total light absorption by particles plus CDOM at 443 nm, as compared to 30-45% for phytoplankton and 0-20% for non-pigmented particles. The overestimation of [Chl_a] from the operational satellite algorithms may be attributed to the strong CDOM absorption in this region. River discharge is important in controlling the bio-optical environment, but cannot explain all of the regional and seasonal variability of biogeochemical constituents in the SMAB.

Pan, Xiaoju↗
NASA NTRS2011

Chlorophyll-a Algorithms for Oligotrophic Oceans: A Novel Approach Based on Three-Band Reflectance Difference

A new empirical algorithm is proposed to estimate surface chlorophyll-a concentrations (Chl) in the global ocean for Chl less than or equal to 0.25 milligrams per cubic meters (approximately 77% of the global ocean area). The algorithm is based on a color index (CI), defined as the difference between remote sensing reflectance (R(sub rs), sr(sup -1) in the green and a reference formed linearly between R(sub rs) in the blue and red. For low Chl waters, in situ data showed a tighter (and therefore better) relationship between CI and Chl than between traditional band-ratios and Chl, which was further validated using global data collected concurrently by ship-borne and SeaWiFS satellite instruments. Model simulations showed that for low Chl waters, compared with the band-ratio algorithm, the CI-based algorithm (CIA) was more tolerant to changes in chlorophyll-specific backscattering coefficient, and performed similarly for different relative contributions of non-phytoplankton absorption. Simulations using existing atmospheric correction approaches further demonstrated that the CIA was much less sensitive than band-ratio algorithms to various errors induced by instrument noise and imperfect atmospheric correction (including sun glint and whitecap corrections). Image and time-series analyses of SeaWiFS and MODIS/Aqua data also showed improved performance in terms of reduced image noise, more coherent spatial and temporal patterns, and consistency between the two sensors. The reduction in noise and other errors is particularly useful to improve the detection of various ocean features such as eddies. Preliminary tests over MERIS and CZCS data indicate that the new approach should be generally applicable to all existing and future ocean color instruments.

Hu, Chuanmin↗
NASA NTRS2014

Obtaining Remote-Sensing Reflectance from Multiple Instrument Systems

Obtaining accurate in situ measurements of Apparent Optical Properties (AOPs) is critical to maintaining satellite data quality. One approach to ensure accuracy is to deploy several independent instruments to measure the same phenomenon. During a cruise in June 2012, off the lee coast of the island of Hawaii, repeated profiles were made with two separate radiometric systems, one from Satlantic, Inc. (Hyperpro) and the other from Biospherical Instruments, Inc. (C-Ops). The C-Ops is multispectral, while the Hyperpro is hyperspectral. Both measure above-water solar irradiance (E(sub s)), downwelling in-water irradiance (E(sub d)), and upwelling in-water radiance (L(sub u)). From these measurements remotely-sensed reflectance (R(sub rs))can be calculated and compared with satellite data. All instruments were calibrated shortly before use, and while differences are to be expected due to temporal changes and spectral weighting differences, these should be consistent and minimal. We explore these differences, and compare to data retrieved from the NASA Moderate Resolution Imaging Spectroradiometer onboard Aqua (MODIS Aqua) when available. We also examine data collection and processing protocols for these systems.

Carbon↗
NASA NTRS2019

Improving Satellite Global Chlorophyll a [alpha] Data Products Through Algorithm Refinement and Data Recovery

A recently developed algorithm to estimate surface ocean chlorophyll a concentrations (Chl in milligrams per cubic meter), namely, the ocean color index (OCI) algorithm, has been adopted by the U.S. National Aeronautics and Space Administration to apply to all satellite ocean color sensors to produce global Chl maps. The algorithm is a hybrid between a band‐difference color index algorithm for low‐Chl waters and the traditional band‐ratio algorithms (OCx) for higher‐Chl waters. In this study, the OCI algorithm is revisited for its algorithm coefficients and for its algorithm transition between color index and OCx using a merged data set of high‐performance liquid chromatography and fluorometric Chl. Results suggest that the new OCI algorithm (OCI2) leads to lower Chl estimates than the original OCI (OCI1) for Chl less than 0.05 milligrams per cubic meter, but smoother algorithm transition for Chl between 0.25 and 0.40 milligrams per cubic meter. Evaluation using in situ data suggests that similar to OCI1, OCI2 has significantly improved image quality and cross‐sensor consistency between SeaWiFS (Sea-viewing Wide Field-of-view Sensor), MODISA (Moderate Resolution Imaging Spectroradiometer on Aqua), and VIIRS (Visible Infrared Imaging Radiometer Suite) over the OCx algorithms for oligotrophic oceans. Mean cross‐sensor difference in monthly Chl data products over global oligotrophic oceans reduced from approximately 10 percent for OCx to 1-2 percent for OCI2. More importantly, data statistics suggest that the current straylight masking scheme used to generate global Chl maps can be relaxed from 7 by 5 to 3 by 3 pixels without losing data quality in either Chl or spectral remote sensing reflectance (R (sub rs) by lambda (sensor wavelength), per steradian (sr (sup −1)) for not just oligotrophic oceans but also more productive waters. Such a relaxed masking scheme yields an average relative increase of 39 percent in data quantity for global oceans, thus making it possible to reduce data product uncertainties and fill data gaps.

Hu, Chuanmin↗
NASA NTRS2023

Tracking Nonradial Outflows in Extreme Ultraviolet and White Light Solar Images

Understanding the solar corona requires knowledge of its dynamics through its various layers and subsequent connectivity to the heliosphere. This requires understanding the nature of the outflows and the physical transitions through the middle corona (∼1.5–6.0 Rs). While this region is still inaccessible to in situ measurements, remote sensing observations are available, but their interpretation can be controversial due to line-of-sight effects and the nonradial motion of outflowing structures close to the Sun (≤3.0 Rs). In this work, we describe a method to mitigate these challenges by generating nonradial height–time profiles of outflows by using advanced image processing techniques. The northern and southern boundaries of a large equatorial streamer during the 2008 solar minimum were identified in STEREO/SECCHI solar images, using two different methodologies based on thresholds of brightness and piecewise polynomial function fitting. To address line-of-sight issues, we used tomographic reconstruction of the 3D distribution of the coronal electron density based on SECCHI/COR2 images. Spectral analysis of the time series of the position angle of the streamer boundary revealed its oscillatory nature at some heights at 36–48 and 10.5–14.6 hr. Dividing the distance between the northern and southern streamer boundaries in equal parts at each height, we obtained nonradial height–time paths from which we generated nonradial profiles of corona/solar wind plasma outflow. We tracked outflows as they moved uninterruptedly from the Sun in EUVI, through COR1 and into COR2. Finally, we discuss the preliminary results of nonradial plane-of-sky velocities for a coronal mass ejection and two small-scale features.

Nathalia Alzate↗
NASA NTRS1997

Remotely Sensing Larval Population Dynamics of Rice Field Anophelines

The primary objective of both studies was to determine if RS and GIS techniques could be used to distinguish between high and low larval-producing rice fields in California. Results of the first study suggested that early-season green-up and proximity to livestock pastures were positively correlated with high larval abundance. Based on the early-season spectral differences between high and low larval-producing fields, it appeared that canopy development and tillering influenced mosquito habitat quality. At that time, rice fields consisted of a mixture of plants and water, a combination that allowed An. freeborni females to lay eggs in partial sunlight, protected from both predators and wind. This established a population earlier in the season than in other, 'less-green' fields where tillering and plant emergence was too minimal for ovipositioning. The study also indicated the importance of the distance that a mosquito would have to fly in order to take a bloodmeal prior to ovipositing. These associations were fully explored in an expanded study two years later. The second study confirmed the positive relationship between early season canopy development and larval abundance, and also demonstrated the relationship between abundance and distance-to-pasture. The association between greenness (as measured using NDVI), distance-to-pasture, and abundance is illustrated. The second study also indicated the siginificance of the landscape context of rice fields for larval production. Fields that included opportunities for feeding and resting within the flight range of the mosquito had higher abundances than did fields that were in a homogeneous rice area.

Beck, Louisa R.↗
NASA NTRS2011

Lidar Remote Sensing for Characterizing Forest Vegetation - Special Issue. Foreword

The Silvilaser 2009 conference held in College Station, Texas, USA, was the ninth conference in the Silvilaser series, which started in 2002 with the international workshop on using lidar (Light Detection and Ranging) for analyzing forest structure, held in Victoria, British Columbia, Canada. Following the Canadian workshop, subsequent forestry-lidar conferences took place in Australia, Sweden, Germany, USA, Japan, Finland, and the United Kingdom (UK). By the time this Silvilaser 2009 special issue of PE&RS is published, the 10th international conference will have been held in Freiburg, Germany, and planning will be ongoing for the 11th meeting to take place in Tasmania, Australia, in October 2011. Papers presented at the 2005 conference held in Blacksburg, Virginia, USA, were assembled in a special issue of PE&RS published in December 2006. Other special issues resulting from previous conferences were published in journals such as the Canadian Journal of Remote Sensing (2003), the Scandinavian Journal of Forest Research (2004), and Japan s Journal of Forest Planning (2008). Given the conference history and the much longer record of publications on lidar applications for estimating forest biophysical parameters, which dates back to the early 1980s, we may consider lidar an established remote sensing technology for characterizing forest canopy structure and estimating forest biophysical parameters. Randy Wynne, a professor at Virginia Tech and the final keynote speaker at Silvilaser 2009, made the case that it was time to push 30 years of research into operations, along the lines of what has already been done to good effect in the Scandinavian countries. In Randy s words, it s time to "Just do it!" This special issue includes a selection of papers presented during the 2009 Silvilaser conference, which consisted of eight sections as follows: (1) biomass and carbon stock estimates, (2) tree species and forest type classification, (3) data fusion and integration, (4, 5, and 6) forest inventory, (7) silvicultural and ecological applications, and (8) terrestrial lidar applications. Within the constraint limiting the number of papers that could be fitted into the special issue we attempted to select those papers that best represented these conference topics and sections, giving special consideration to studies using forestry lidar data collected from each of the three platforms -- terrestrial, airborne, and spaceborne. Reflecting the international participation and reach of the conference, the studies presented here took place in the USA, Canada, Taiwan, the UK, and China.

Popescu, Sorin C.↗