Satellite-derived Reflectance Research Articles

Hypoxia Forecasting for Chesapeake Bay Using Artificial Intelligence

Abstract Seasonal hypoxia is a recurring threat to ecosystems and fisheries in the Chesapeake Bay. Hypoxia forecasting based on coupled hydrodynamic and biogeochemical models has proven useful for many stakeholders, as these models excel in accounting for the effects of physical forcing on oxygen supply, but may fall short in replicating the more complex biogeochemical processes that govern oxygen consumption. Satellite-derived reflectances could be used to indicate the presence of surface organic matter over the Bay. However, teasing apart the contribution of atmospheric and aquatic constituents from the signal received by the satellite is not straightforward. As a result, it is difficult to derive surface concentrations of organic matter from satellite data in a robust fashion. A potential solution to this complexity is to use deep learning to build end-to-end applications that do not require precise accounting of the satellite signal from the atmosphere or water, phytoplankton blooms, or sediment plumes. By training a deep neural network with data from a vast suite of variables that could potentially affect oxygen in the water column, improvement of short-term (daily) hypoxia forecast may be possible. Here, we predict oxygen concentrations using inputs that account for both physical and biogeochemical factors. The physical inputs include wind velocity reanalysis information, together with 3D outputs from an estuarine hydrodynamic model, including current velocity, water temperature, and salinity. Satellite-derived spectral reflectance data are used as a surrogate for the biogeochemical factors. These input fields are time series of weekly statistics calculated from daily information, starting 8 weeks before each oxygen observation was collected. To accommodate this input data structure, we adopted a model architecture of long short-term memory networks with eight time steps. At each time step, a set of convolutional neural networks are used to extract information from the inputs. Ablation and cross-validation tests suggest that among all input features, the strongest predictor is the 3D temperature field, with which the new model can outperform the state-of-the-art by ∼20% in terms of median absolute error. Our approach represents a novel application of deep learning to address a complex water management challenge. Significance Statement This study presents a novel approach that combines deep learning and hydrodynamic model outputs to improve the accuracy of hypoxia forecasts in the Chesapeake Bay. By training a deep neural network with both physical and biogeochemical information as input features, the model accurately predicts oxygen concentration at any depth in the water column 1 day in advance. This approach has the potential to benefit stakeholders and inform adaptation measures during the recurring threat of hypoxia in the Chesapeake Bay. The success of this study suggests the potential for similar applications of deep learning to address complex water management challenges. Further research could investigate the application of this approach to different forecast lead times and other regions and ecosystem types.

Estimating Surface Concentrations of Calanus finmarchicus Using Standardised Satellite-Derived Enhanced RGB Imagery

Calanus finmarchicus is a keystone zooplankton species that is commercially harvested and is critical in sustaining many important fisheries in the North Atlantic. However, due to their patchy population distributions, they are notoriously difficult to map using traditional ship-based techniques. This study involves the use of a combined approach of standardized ocean colour imagery and radiative transfer modelling to identify reflectance anomalies potentially caused by surface swarms of C. finmarchicus in the northern Norwegian Sea. Here, we have standardized satellite eRGB imagery that depicts a distinct ‘red’ patch, which coincides with in situ measurements of high surface concentrations of C. finmarchicus. Anomaly mapping using a novel colour matching technique shows a high degree of anomaly within this patch compared to the surrounding waters, indicating the presence of something other than the standard bio-optical model constituents influencing the optics of the water column. Optical closure between modelled and satellite-derived reflectance signals is achieved (and the anomaly is significantly reduced) through the addition of C. finmarchicus absorption into the model. Estimations of the surface concentrations of C. finmarchicus suggest between 80,000 and 150,000 individuals m−3 within the extent of the identified red patch. Furthermore, analysis of the impact of C. finmarchicus absorption on the OC3M algorithm performance points to the potential for the algorithm to over-estimate chlorophyll concentrations if C. finmarchicus populations are present in the surface waters.

Comparison of Ground-Based, Unmanned Aerial Vehicles and Satellite Remote Sensing Technologies for Monitoring Pasture Biomass on Dairy Farms

Systematic measurement of pasture biomass (kg DM/ha) is crucial for optimising pasture utilisation and increasing dairy farm profitability. On-farm pasture monitoring can be conducted using various sensors, but calibrations are necessary to convert the measured variable into pasture biomass. In this study, we conducted three experiments in New South Wales (Australia) to evaluate the use of the rising plate meter (RPM), pasture reader (PR), unmanned aerial vehicles (UAV) and satellites as pasture monitoring tools. We tested various calibration methods that can improve the accuracy of the estimations and be implemented more easily on-farm. The results indicate that UAV and satellite-derived reflectance indices (e.g., Normalised Difference Vegetation Index) can be indirectly calibrated with height measurements obtained from an RPM or PR. Height measurements can be then converted into pasture biomass ideally by conducting site-specific sporadic calibrations cuts. For satellites, using the average of the entire paddock, root mean square error (RMSE) = 226 kg DM/ha for kikuyu (Pennisetum clandestinum Hochst. ex Chiov) and 347 kg DM/ha for ryegrass (Lolium multiflorum L.) is as effective as but easier than matching NDVI pixels with height measurement using a Global Navigation Satellite System (RMSE = 227 kg DM/ha for kikuyu and 406 kg DM/ha for ryegrass). For situations where no satellite images are available for the same date, the average of all images available within a range of up to four days from the day ground measurements were taken could be used (RMSE = 225 kg DM/ha for kikuyu and 402 kg DM/ha for ryegrass). These methodologies aim to develop more practical and easier-to-implement calibrations to improve the accuracy of the predictive models in commercial farms. However, more research is still needed to test these hypotheses under extended periods, locations, and pasture species.

Field experiment demonstrates the potential utility of satellite-derived reflectance indices for monitoring regeneration of boreal forest communities

Large-scale reforestation efforts require scalable and affordable monitoring technologies to track progress. Boreal forests disturbed by oil and gas development provide an opportunity to utilize ongoing reclamation efforts to assess new monitoring technologies with potential utility to other regions. In this study, we tested the application of reflectance indices derived from high spatial resolution satellite imagery to monitor early-stage reforestation. We installed a randomized nursery experiment in Alberta, Canada wherein the total stocking density and relative abundance of four common boreal tree species were manipulated. Satellite imagery from Maxar's WorldView-2 and WorldView-3 satellites were collected over the experimental plots annually from 2017 through 2020. From these reflectance data, we calculated one texture index, three greenness indices, and one index that integrated both texture and greenness. Finally, two sets of regression models were developed, one of which included only manipulated stem density while the other set included both the density and the mean height of stems in a given year. In the first year of the experiment, we detected no significant differences in any reflectance index across the range of manipulations. Thereafter, all indices showed significant differences, with the integrated texture-greenness index demonstrating the best relative performance among those tested. Our results illustrate the potential for satellite data to yield information regarding tree density and height during the critical early years of restoration. We suggest specific steps that future researchers could use extend this work to other regions and enable potential adoption of this monitoring approach at scale.

Assessing an Atmospheric Correction Algorithm for Time Series of Satellite-Based Water-Leaving Reflectance Using Match-Up Sites in Australian Coastal Waters

An atmospheric correction algorithm for medium-resolution satellite data over general water surfaces (open/coastal, estuarine and inland waters) has been assessed in Australian coastal waters. In situ measurements at four match-up sites were used with 21 Landsat 8 images acquired between 2014 and 2017. Three aerosol sources (AERONET, MODIS ocean aerosol and climatology) were used to test the impact of the selection of aerosol optical depth (AOD) and Ångström coefficient on the retrieved accuracy. The initial results showed that the satellite-derived water-leaving reflectance can have good agreement with the in situ measurements, provided that the sun glint is handled effectively. Although the AERONET aerosol data performed best, the contemporary satellite-derived aerosol information from MODIS or an aerosol climatology could also be as effective, and should be assessed with further in situ measurements. Two sun glint correction strategies were assessed for their ability to remove the glint bias. The most successful one used the average of two shortwave infrared (SWIR) bands to represent sun glint and subtracted it from each band. Using this sun glint correction method, the mean all-band error of the retrieved water-leaving reflectance at the Lucinda Jetty Coastal Observatory (LJCO) in north east Australia was close to 4% and unbiased over 14 acquisitions. A persistent bias in the other strategy was likely due to the sky radiance being non-uniform for the selected images. In regard to future options for an operational sun glint correction, the simple method may be sufficient for clear skies until a physically based method has been established.

Effect of phytoplankton community size structure on remote-sensing reflectance and chlorophyll a products

Abstract Remotely-sensed ocean colour is the main tool for estimating chlorophyll a (Chl-a) concentration and primary productivity on the global scale. In order to investigate the source of errors in remotely-sensed Chl-a concentration we obtained in situ bio-optical properties, in situ reflectances, satellite-derived reflectances and the Chl-a concentration satellite products of the Ocean and Land Colour Imager (OLCI) Instrument on board Sentinel-3 A in waters off eastern Australia. The mesoscale eddies of these oligotrophic waters provide contrasting phytoplankton communities that allowed us to focus on the effect of phytoplankton size as a source of errors. In these waters, cold-core cyclonic eddies (CE) are dominated by large phytoplankton cells, while small cells dominate warm-core anticyclonic eddies (ACE). The chlorophyll-specific absorption and backscattering from contrasting sites show significant difference due to the differing package effect of phytoplankton size distributions. After normalising the absorption and backscattering spectra to Chl-a associated with just small phytoplankton, the spectra of optical properties become much more similar, showing that small-sized phytoplankton dominate IOPs even when large cells contain the greater fraction of Chl-a concentration of the phytoplankton community. Measured in situ reflectances agreed with reflectances calculated using a simple optical model based on measured IOPs. Furthermore, the in situ measured reflectances agreed well with the OLCI reflectance (mean normalised bias (MNB) of 7% for wavelengths

Glacier facies characterization using optical satellite data: Impacts of radiometric resolution, seasonality, and surface morphology

The spectral characterization of geographic landscapes is vital for their accurate mapping using remote sensing data. This can be done through spectral profiling, as demonstrated here, to characterize the surface facies of the Gangotri and neighbouring glaciers, central Himalaya. The satellite-derived reflectance curves were compared with the in-situ and published (validation) data. The study attempts to understand the influence of certain parameters such as the satellite sensor’s radiometric resolution, timing of data acquisition (seasonality), and surface morphology on glacier/snow–ice facies identification. Results show that the first two parameters complement each other in identifying the snow–ice facies accurately. High radiometric resolution (HRR) data concurred closely with the validation dataset and had higher mean entropy values over the glaciated areas than low radiometric resolution (LRR) ablation data. Presence of seasonal snow and degree of surface melting show considerable influence on satellite-derived reflectances of glacier facies. Our findings assert the usage of HRR ablation data in appraising the interannual and seasonal variability of glacier facies. While HRR post-ablation data overestimates the reflectance of snow–ice facies, LRR post-ablation data have limitations in their discrimination. Certain morphology and resultant features, such as crevasses and shadows, induce underestimation of the satellite-derived reflectances, creating confusion among the snow and ice facies. This spectral confusion can, however, be resolved by the use of ancillary data. Elevation, temperature, and band ratios/spectral indices are helpful in segregating snow–ice facies, while slope, band ratios, temperature, and texture measures effectively discriminate the other facies.

Life cycle of bamboo in the southwestern Amazon and its relation to fire events

Abstract. Bamboo-dominated forests comprise 1 % of the world's forests and 3 % of the Amazon forests. The Guadua spp. bamboos that dominate the southwest Amazon are semelparous; thus flowering and fruiting occur once in a lifetime before death. These events occur in massive spatially organized patches every 28 years and produce huge quantities of necromass. The bamboo–fire hypothesis argues that increased dry fuel after die-off enhances fire probability, creating opportunities that favor bamboo growth. In this study, our aim is to map the bamboo-dominated forests and test the bamboo–fire hypothesis using satellite imagery. Specifically, we developed and validated a method to map the bamboo die-off and its spatial distribution using satellite-derived reflectance time series from the Moderate Resolution Imaging Spectroradiometer (MODIS) and explored the bamboo–fire hypothesis by evaluating the relationship between bamboo die-off and fires detected by the MODIS thermal anomalies product in the southwest Amazon. Our findings show that the near-infrared (NIR) is the most sensitive spectral interval to characterize bamboo growth and cohort age. Automatic detection of historical bamboo die-off achieved an accuracy above 79 %. We mapped and estimated 15.5 million ha of bamboo-dominated forests in the region. The bamboo–fire hypothesis was not supported because only a small fraction of bamboo areas burned during the analysis timescale, and, in general, bamboo did not show higher fire probability after the die-off. Nonetheless, fire occurrence was 45 % higher in dead than live bamboo in drought years, associated with ignition sources from land use, suggesting a bamboo–human–fire association. Although our findings show that the observed fire was not sufficient to drive bamboo dominance, the increased fire occurrence in dead bamboo in drought years may contribute to the maintenance of bamboo and potential expansion into adjacent bamboo-free forests. Fire can even bring deadly consequences to these adjacent forests under climate change effects.

Multi-band spectral matching inversion algorithm to derive water column properties in optically shallow waters: An optimization of parameterization

Deriving inherent optical properties (IOPs) and other water quality parameters from satellite remote sensing data covering optically shallow environments has historically been problematic due to difficulties in separating the benthic signal from that of the water column. While recent advances have improved such retrievals, most methods have high uncertainties for very shallow (<5m) or very bright (e.g., carbonate sand) targets. Here, we present a two-stage process to improve IOP derivations from satellite-derived reflectance data, termed the Shallow Water Optimization with Resolved Depth (SWORD). Within this process, a raster bathymetry is first derived through multiple pixel-wise implementations of a spectral matching algorithm on mapped reflectance data. This bathymetry is then used as a fixed input in subsequent implementations of the algorithm, leading to improved IOP retrievals. The SWORD approach was developed and tested using a dataset of simulated reflectance spectra as well as MERIS reflectance data covering two optically shallow water environments. Bathymetries derived using this process showed strong agreement with those determined from soundings and coastal relief models. Although SWORD-derived raster albedo maps showed general concordance to benthic habitat surveys, we found limited benefit of fixing this parameter in spectral matching routines. IOP derivations from SWORD show expected spatiotemporal patterns in the Florida Keys region, consistent with local hydrodynamic processes, seasonal fluctuations, and known anomalous events. This approach is portable to multispectral reflectance data from similar satellite instruments, allowing regular and ongoing assessment and monitoring of optical water quality for ecologically and economically important marine systems.

Quantifying water stress effect on daily light use efficiency in Mediterranean ecosystems using satellite data

ABSTRACTThe capacity of six water stress factors (ε′i) to track daily light use efficiency (ε) of water-limited ecosystems was evaluated. These factors are computed with remote sensing operational products and a limited amount of ground data: ε′1 uses ground precipitation and air temperature, and satellite incoming global solar radiation; ε′2 uses ground air temperature, and satellite actual evapotranspiration and incoming global solar radiation; ε′3 uses satellite actual and potential evapotranspiration; ε′4 uses satellite soil moisture; ε′5 uses satellite-derived photochemical reflectance index; and ε′6 uses ground vapor pressure deficit. These factors were implemented in a production efficiency model based on Monteith’s approach in order to assess their performance for modeling gross primary production (GPP). Estimated GPP was compared to reference GPP from eddy covariance (EC) measurements (GPPEC) in three sites placed in the Iberian Peninsula (two open shrublands and one savanna). ε′i were correlated to ε, which was calculated by dividing GPPEC by ground measured photosynthetically active radiation (PAR) and satellite-derived fraction of absorbed PAR. Best results were achieved by ε′1, ε′2, ε′3 and ε′4 explaining around 40% and 50% of ε variance in open shurblands and savanna, respectively. In terms of GPP, R2 ≈ 0.70 were obtained in these cases.

Assessing the Impact of Typhoons on Rice Production in the Philippines

AbstractThis study quantifies the impact of typhoons on rice production in the Philippines. To this end, satellite-derived reflectance data are used to detect the location of rice fields at 500-m resolution. Utilizing typhoon-track data within a wind field model and satellite-derived precipitation measures, fragility curves are then employed to proxy the damage of storms on rice production within each rice field. The results from a panel spatial regression model show that typhoons substantially reduced local provincial production in the quarter of the strike, having caused losses of up to 12.5 million tons since 2001. Using extreme value theory to predict future losses, the results suggest that a typhoon like the recent Haiyan, which is estimated to have caused losses of around 260 000 tons, has a return period of 13 years. This methodology can provide a relatively timely tool for rice damage assessments after tropical cyclones in the region.

Spectral and spatial requirements of remote measurements of pelagic Sargassum macroalgae

Remote detection of pelagic Sargassum is often hindered by its spectral similarity to other floating materials and by the inadequate spatial resolution. Using measurements from multi-spectral satellite sensors (Moderate Resolution Imaging Spectroradiometer or MODIS), Landsat, WorldView-2 (or WV-2) as well as hyperspectral sensors (Hyperspectral Imager for the Coastal Ocean or HICO, Airborne Visible-InfraRed Imaging Spectrometer or AVIRIS) and airborne digital photos, we analyze and compare their ability (in terms of spectral and spatial resolutions) to detect Sargassum and to differentiate it from other floating materials such as Trichodesmium, Syringodium, Ulva, garbage, and emulsified oil. Field measurements suggest that Sargassum has a distinctive reflectance curvature of ~630nm due to its chlorophyll c pigments, which provides a unique spectral signature when combined with the reflectance ratio between brown (~650nm) and green (~555nm) wavelengths. For a 10-nm resolution sensor on the hyperspectral HyspIRI mission currently being planned by NASA, a stepwise rule to examine several indexes established from 6 bands (centered at 555, 605, 625, 645, 685, 755nm) is shown to be effective to unambiguously differentiate Sargassum from all other floating materials Numerical simulations using spectral endmembers and noise in the satellite-derived reflectance suggest that spectral discrimination is degraded when a pixel is mixed between Sargassum and water. A minimum of 20–30% Sargassum coverage within a pixel is required to retain such ability, while the partial coverage can be as low as 1–2% when detecting floating materials without spectral discrimination. With its expected signal-to-noise ratios (SNRs~200:1), the hyperspectral HyspIRI mission may provide a compromise between spatial resolution and spatial coverage to improve our capacity to detect, discriminate, and quantify Sargassum.

Relating Demographic Characteristics of a Small Mammal to Remotely Sensed Forest-Stand Condition

Many ecological systems around the world are changing rapidly in response to direct (land-use change) and indirect (climate change) human actions. We need tools to assess dynamically, and over appropriate management scales, condition of ecosystems and their responses to potential mitigation of pressures. Using a validated model, we determined whether stand condition of floodplain forests is related to densities of a small mammal (a carnivorous marsupial, Antechinus flavipes) in 60 000 ha of extant river red gum (Eucalyptus camaldulensis) forests in south-eastern Australia in 2004, 2005 and 2011. Stand condition was assessed remotely using models built from ground assessments of stand condition and satellite-derived reflectance. Other covariates, such as volumes of fallen timber, distances to floods, rainfall and life stages were included in the model. Trapping of animals was conducted at 272 plots (0.25 ha) across the region. Densities of second-year females (i.e. females that had survived to a second breeding year) and of second-year females with suckled teats (i.e. inferred to have been successful mothers) were higher in stands with the highest condition. There was no evidence of a relationship with stand condition for males or all females. These outcomes show that remotely-sensed estimates of stand condition (here floodplain forests) are relatable to some demographic characteristics of a small mammal species, and may provide useful information about the capacity of ecosystems to support animal populations. Over-regulation of large, lowland rivers has led to declines in many facets of floodplain function. If management of water resources continues as it has in recent decades, then our results suggest that there will be further deterioration in stand condition and a decreased capacity for female yellow-footed antechinuses to breed multiple times.

Match-Up Analysis of MERIS Radiometric Data in the Northern Adriatic Sea

This letter discusses the normalized water-leaving reflectance obtained from two atmospheric corrections built in the MEGS 8.0 processor applied to the Medium Resolution Imaging Spectrometer (MERIS) marine data. In situ reference data for intercomparisons with satellite-derived reflectance products are from the Ocean Color component of the Aerosol Robotic Network (AERONET-OC). Case of study is the northern Adriatic Sea AERONET-OC site characterized by both Case-1 and Case-2 waters. The accuracy of MERIS pigment indices is also discussed in relation to uncertainties and biases affecting atmospherically corrected data.

Evaluation of chlorophyll-a remote sensing algorithms for an optically complex estuary

Estimation of chlorophyll-a concentrations (Chla in mgm−3) in optically complex waters using remote sensing is difficult because traditional empirical (e.g., OCx blue/green band ratios) or semi-analytical algorithms (e.g., GSM or QAA) rely on blue–green wavelengths where water constituents other than phytoplankton often dominate the optical properties and satellite-derived reflectance in these wavelengths often contains substantial uncertainties. Some recent attempts have been made to derive Chla based on red and near-infrared wavelengths to avoid such problems. Using in situ data collected from 9 cruises between 1998 and 2010 in Tampa Bay, a large estuary (~1000km2) located in Florida (U.S.A.), we first tested three Chla algorithms, where each algorithm derived Chla as a function of independent variable x=Rrs(λ2)/Rrs(λ1), x=[Rrs(λ1)−1−Rrs(λ2)−1]∗Rrs(λ3), and x=[Rrs(λ1)−1−Rrs(λ2)−1]/[Rrs(λ4)−1−Rrs(λ3)−1], respectively. Here Rrs(λ) is the remote sensing reflectance, λ is the wavelength from 660 to 760nm, and the positions of the these wavelengths were determined using an objective method while the functional forms of Chla=f(x) were determined through regression between Chla and x. A synthetic chlorophyll index (SCI) using 4 green-red MERIS wavelengths was also tested. Using field measured Rrs(λ) data, all these algorithms showed the potentials to retrieve Chla between ~2 and ~80mgm−3, with 2-band algorithms showing the best performance. However, when satellite-derived Rrs(λ) data were used, none of the four algorithms was applicable to MODIS in this estuary, and only a 2-band algorithm in the form of Rrs(709)/Rrs(665) was applicable to MERIS, with other algorithms significantly degraded. The coefficients of the 2-band algorithm for MERIS were determined through regression between in situ Chla and MERIS-derived Rrs(709)/Rrs(665), with the final form of Chla=10.55x1.51. The algorithm yielded mean relative errors of 35.3% for Chla between 1.0 and 30.0mgm−3 when an in situ dataset from the Environmental Protection Commission of Hillsborough County (EPCHC) was used to tune the algorithm coefficients, which was further validated using another independent dataset. Spatial Chla patterns derived from MERIS for both low and high concentrations also showed reasonable agreement with in situ measurements. In contrast, 3-band and 4-band algorithms yielded significant patchiness in spatial patterns that differed from in situ measurements. Tests in Chesapeake Bay (located on the east coast of the U.S.A.) showed reasonable spatial patterns of Chla using the Rrs(709)/Rrs(665) approach, with a mean relative error of 34.7% for Chla ranging between 1.6 and 21.5mgm−3, suggesting that the approach may be generally applicable to other turbid estuaries once algorithm coefficients are adjusted using local data.

Leaf and stand-level carbon uptake of a Mediterranean forest estimated using the satellite-derived reflectance indices EVI and PRI

Various aspects of global environmental change affect plant photosynthesis, the primary carbon input in ecosystems. Thus, accurate methods of measuring plant photosynthesis are important. Remotely sensed spectral indices can monitor in detail the green biomass of ecosystems, which provides a measure of potential photosynthetic capacity. In evergreen vegetation types, however, such as Mediterranean forests, the amount of green biomass changes little during the growing season and, therefore, changes in green biomass are not responsible for changes in photosynthetic rates in those forests. This study examined the net photosynthetic rates and the diametric increment of stems in a Mediterranean forest dominated by Quercus ilex using three spectral indices (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and photochemical reflectance index (PRI)) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Average annual EVI accounted for 83% of the variability of the diametric increment of Q. ilex stems over a 10 year period. NDVI was marginally correlated with the diametric increment of stems. This study was the first to identify a significant correlation between net photosynthetic rates and radiation use efficiency at the leaf level using PRI derived from satellite data analysed at the ecosystem level. These results suggest that each spectral index provided different and complementary information about ecosystem carbon uptake in a Mediterranean Q. ilex forest.

Impact of emissions from anthropogenic sources on satellite-derived reflectance

The present study deals with the impact of extensive anthropogenic activities associated with festivities and agricultural crop residues burning in the Indo-Gangetic Plains (IGP) on satellite-derived reflectance during November 2007. Intense smoke plumes were observed in the IRS-P4 OCM satellite data over IGP associated with agricultural crop residue burning during the study period. Terra-MODIS AOD and CALIPSO LIDAR backscatter datasets were analysed over the region to understand the spatial and temporal variation of the aerosol properties. Ground-based measurements on aerosol optical properties and black carbon (BC) mass concentration were carried out during 7–14 November 2007 over urban region of Hyderabad, India. Top of the Atmosphere (TOA) reflectance estimated from IRS-P4 Ocean Color Monitor (OCM) data showed large variations due to anthropogenic activities associated with crop residue burning and fireworks. Atmospheric corrections to OCM satellite data using 6S radiative transfer code with inputs from ground and satellite measurements could account for the variations due to differential aerosol loading. Results are discussed in this paper.

The use of NOAA AVHRR satellite data for mapping sediment variability in the marine and coastal environment

Sediment-rich effluent from the Atchafalaya River (USA) flows into the clear waters of the Gulf of Mexico along the Louisiana Coast. The sediment, which serves as a material for shoreline progradation, is transported by near shore currents to various portions of the coastal zone. Satellite data from the National Oceanographic and Atmospheric Administration's (NOAA) Advanced Very High Resolution Radiometer (AVHRR), 1.1km resolution, atmospherically corrected channel 1 and 2, are used to map the suspended sediment transport in the Atchafalaya Bay region of the Louisiana coastal zone. The turbid water equation was solved with the Newton method of non-linear curve fitting using the suspended sediment concentration obtained from the field sample analysis and the satellite-derived Reflectance values for Rw. The values of the constants Fs and Gs are 0.66 and 32.86, respectively, with R2 = 0.8895 and the root mean square error for reflectance is 0.0089. Results based on four satellite images show that the suspended sediment plume transported along the shore, to the west is 10km wide on the average when southeast winds dominate. However, the suspended sediment transported to the deep sea in response to the passage of winter cold fronts could reach as far out to the sea as 70km when the northeast winds dominate. The area near Big Constance Lake, which has a persistently higher concentration of suspended sediment around the year, is a suspected non-depositional area. The southwest winds cause a circulation in the bay and thus periods of higher depositional rates. Journal of Building and Land Development Vol. 13 (2) 2006: pp. 65-73

Variability in measured and modelled remote sensing reflectance for coastal waters at LEO-15

A large database of in situ bio-optical measurements were collected at the LEO-15 (Long-term Ecosystem Observatory) off the southern coast of New Jersey, USA. The data were used to quantify the impact of coastal upwelling on near-shore bulk apparent (AOP) and inherent (IOP) optical properties. There was good qualitative agreement between the AOPs and IOPs in space and time. The measured IOPs were used as inputs to the Hydrolight radiative transfer model (RTE). Estimated spectral AOPs from the RTE were strongly correlated (generally R2>0.80) to measured AOPs. If optical closure between in-water measurements was achieved then the RTE was used to construct the spectral remote sensing reflectance. The modelled remote sensing reflectances were compared to satellite-derived reflectance estimates from four different algorithms. Quantitative agreement between the satellite-measured and in-water modelled remote sensing reflectance was good but results were variable between the different models. The strength of the correlation and spectral consistency was variable with space and time. Correlations were strongest in clear offshore waters and lowest in the near-shore turbid waters. In the near-shore waters, the correlation was strongest for blue wavelengths (400–555 nm) but lower for the red wavelengths of light.

Comparison between in situ and MODIS-derived spectral reflectances of snow and sea ice in the Amundsen Sea, Antarctica

The spectral albedo and directional reflectance of snow and sea ice were measured on sea ice of various types, including nilas, grey ice, pancake ice, multi-year pack ice, and land-fast ice in the Ross, Amundsen and Bellingshausen seas during a summer cruise in February through March 2000. Measurements were made using a spectroradiometer that has 512 channels in the visible and near-infrared (VNIR) region in which 16 of the 36 bands of the Moderate Resolution Imaging Spectroradiometer (MODIS) are covered. Directional reflectance is also retrieved from the MODIS radiometrically calibrated data (Level 1B) concurrently acquired from the first National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) satellite, Terra. The locations of the ground ice stations are identified accurately on the MODIS images, and the spectral albedo and directional reflectance values at the 16 VNIR MODIS bands are extracted for those pixel locations. MODIS-derived reflectance is then corrected for the intervening atmosphere whose parameters are retrieved from the MODIS atmospheric profiles product (MOD07_L2) for the same granule. The corresponding spectral albedo and directional reflectance with the same viewing geometry as MODIS are derived from our ground-based spectroradiometer measurements. Because the footprint of the ground spectroradiometer is much smaller than the pixel sizes of MODIS images, the averaged spectral reflectance and albedo in the vicinity of each ice station are simulated for the corresponding MODIS pixel from the ground spectral measurements by weighting over different surface types (various ice types and open water). An accurate determination of ice concentration is important in deriving ground reflectance of a simulated pixel from in situ measurements. The best agreement between the in situ and MODIS measurements was found when the ground had 10/10 ice concentration (discrepancy range 0.2–11.69%, average 4.8%) or was oneice-type dominant (discrepancy range 0.8–16.9%, average 6.2%). The more homogeneous the ground surface and the less variable the ground topography, the more comparable between the in situ and satellite-derived reflectance is expected.