References of "Remote Sensing of Environment"
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See detailRemote sensing of colour, temperature and salinity – new challenges and opportunities
Alvera Azcarate, Aïda ULg; Ruddick, Kevin; Minnett, Peter

in Remote Sensing of Environment (in press)

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See detailEvaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements
Hmimina, G.; Dufrêne, Eric; Pontailler, J.-Y. et al

in Remote Sensing of Environment (2013), (132), 145-158

Vegetation phenology is the st udy of the timing of seasonal events that are considered to be the result of adaptive responses to climate variations on short and long time scales. In the field of remote ... [more ▼]

Vegetation phenology is the st udy of the timing of seasonal events that are considered to be the result of adaptive responses to climate variations on short and long time scales. In the field of remote sensing of vegetation phenology, phenologicalmetrics are derived fromtime series of optical data. For that purpose, considerable effort has been specifically focused on developing noise reduction and cloud-contaminated data removal techniques to improve the quality of remotely-sensed time series. Comparative studies between time series composed of satellite data acquired under clear and cloudy conditions and fromradiometric data obtainedwith high accuracy fromground-basedmeasurements constitute a direct and effective way to assess the operational use and limitations of remote sensing for predicting the main plant phenological events. In the present paper, we sought to explicitly evaluate the potential use of MODerate resolution Imaging Spectroradiometer (MODIS) remote sensing data for monitoring the seasonal dynamics of different types of vegetation cover that are representative of the major terrestrial biomes, including temperate deciduous forests, evergreen forests, African savannah, and crops. After cloud screening and filtering, we compared the temporal patterns and phenological metrics derived from in situ NDVI time series and from MODIS daily and 16-composite products. We also evaluated the effects of residual noise and the in uence of data gaps in MODIS NDVI time series on the identification of the most relevant metrics for vegetation phenology monitoring. The results show that the in exion points of a model fitted to a MODIS NDVI time series allow accurate estimates of the onset of greenness in the spring and the onset of yellowing in the autumn in deciduous forests (RMSE<oneweek). Phenologicalmetrics identical to those providedwith theMODIS Global Vegetation Phenology product (MDC12Q2) are less robust to data gaps, and they can be subject to large biases of approximately twoweeks or more during the autumn phenological transitions. In the evergreen forests, in situ NDVI time series describe the phenology with high fidelity despite small temporal changes in the canopy foliage. However, MODIS is unable to provide consistent phenological patterns. In crops and savannah, MODIS NDVI time series reproduce the general temporal patterns of phenology, but significant discrepancies appear between MODIS and ground-based NDVI time series during very localized periods of time depending on the weather conditions and spatial heterogeneity within the MODIS pixel. In the rainforest, the temporal pattern exhibited by a MODIS 16-day composite NDVI time series ismore likely due to a pattern of noise in the NDVI data structure according to both rainy and dry seasons rather than to phenological changes. More investigations are needed, but in all cases, this result leads us to conclude that MODIS time series in tropical rainforests should be interpreted with great caution. [less ▲]

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See detailAssessing the impact of brightness temperature simulation adjustment conditions in correcting Metop-A SST over the Mediterranean Sea
Tomazic, Igor ULg; Roquet, Hervé; Le Borgne, Pierre

in Remote Sensing of Environment (2013)

Multispectral sea surface temperature (SST) algorithms applied to infrared (IR) radiometer data exhibit regional biases due to the intrinsic inability of the SST algorithm to cope with the vast range of ... [more ▼]

Multispectral sea surface temperature (SST) algorithms applied to infrared (IR) radiometer data exhibit regional biases due to the intrinsic inability of the SST algorithm to cope with the vast range of atmospheric types, mainly influenced by water vapor and temperature profiles. Deriving a SST correction from simulated brightness temperatures (BTs), obtained by applying a Radiative Transfer Model (RTM) to Numerical Weather Prediction (NWP) atmospheric profiles and first guess SST, is one of the solutions to reduce regional biases. This solution is envisaged in the particular case of Metop-A Advanced Very High Resolution Radiometer (AVHRR) derived SST. Simulated BTs show errors, linked to RTM, atmospheric profiles or guess field errors. We investigated the conditions of adjusting simulated to observed BTs in the particular case of the Mediterranean Sea over almost one year. Our study led to define optimal spatio/temporal averaging parameters of the simulation observation differences, both during day and night, summer and colder season and for two simulation modes: operational (with reduced vertical resolution – 15 levels – NWP atmospheric profiles and two days old analysis used as first guess SST) and delayed (full vertical resolution – 91 levels – and concurrent analysis used as first guess SST). Each BT adjustment has been evaluated by comparing the corresponding corrected AVHRR SST to the AATSR SST that we adopted as validation reference. We obtained an optimized result across all defined conditions and modes for a spatial smoothing of 15 deg and a temporal averaging between 3 and 5 days. Specifically, analyses based on 10 day averages showed that a standard deviation based criterion favors spatial smoothing above 10 deg for all temporal averaging, while a bias based criterion favors shorter temporal averaging during daytime (< 5 days) and higher spatial smoothing (> 10 deg) for nighttime. This study has shown also the impact of diurnal warming both in deriving BT adjustment and in validation results. [less ▲]

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See detailOutlier detection in satellite data using spatial coherence
Alvera Azcarate, Aïda ULg; Sirjacobs, Damien ULg; Barth, Alexander ULg et al

in Remote Sensing of Environment (2012), 119

Satellite data sets often contain outliers (i.e., anomalous values with respect to the surrounding pixels), mostly due to undetected clouds and rain or to atmospheric and land contamination. A methodology ... [more ▼]

Satellite data sets often contain outliers (i.e., anomalous values with respect to the surrounding pixels), mostly due to undetected clouds and rain or to atmospheric and land contamination. A methodology to detect outliers in satellite data sets is presented. The approach uses a truncated Empirical Orthogonal Function (EOF) basis. The information rejected by this EOF basis is used to identify suspect data. A proximity test and a local median test are also performed, and a weighted sum of these three tests is used to accurately detect outliers in a data set. Most satellite data undergo automated quality-check analyses. The approach presented exploits the spatial coherence of the geophysical fields, therefore detecting outliers that would otherwise pass such checks. The methodology is applied to infrared sea surface temperature (SST), microwave SST and chlorophyll-a concentration data over different domains, to show the applicability of the technique to a range of variables and temporal and spatial scales. A series of sensitivity tests and validation with independent data are also conducted. [less ▲]

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See detailGround-based Network of NDVI measurements for tracking temporal dynamics of canopy structure and vegetation phenology in different biomes
Soudani, K.; Hmimina, K.; Delpierre, N. et al

in Remote Sensing of Environment (2012), 123

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See detailSpatio-temporal dynamics of phytoplankton and primary production in Lake Tanganyika using a MODIS based bio-optical time series
Bergamino, Nadia; Horion, Stéphanie; Stenuite, Stéphane et al

in Remote Sensing of Environment (2010)

Lake Tanganyika, the second largest freshwater ecosystem in Africa, is characterised by a significant heterogeneity in phytoplankton concentration linked to its particular hydrodynamics. To gather a ... [more ▼]

Lake Tanganyika, the second largest freshwater ecosystem in Africa, is characterised by a significant heterogeneity in phytoplankton concentration linked to its particular hydrodynamics. To gather a proper understanding of primary production, it is necessary to consider spatial and temporal dynamics throughout the lake. In the present work, daily MODIS-AQUA satellite measurements were used to estimate chlorophyll-a concentrations and the diffuse attenuation coefficient (K490) for surface waters. The spatial regionalisation of Lake Tanganyika, based on Empirical Orthogonal Functions of the chlorophyll-a dataset (July 2002–November 2005), allowed for the separation of the lake in 11 spatially coherent and co-varying regions, with 2 delocalised coastal regions. Temporal patterns of chlorophyll-a showed significant differences between regions. Estimation of the daily primary production in each region indicates that the dry season is more productive than the wet season in all regions with few exceptions. Whole-lake daily primary productivity calculated on an annual basis (2003) was 646±142 mgC m−2 day−1. Comparing our estimation to previous studies, photosynthetic production in Lake Tanganyika appears to be presently lower (about 15 %), which is consistent with other studies which used phytoplankton biovolume and changes of δ13C in the lake sediments. The decrease in lake productivity in recent decades may be associated to changes in climate conditions. [less ▲]

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See detailOptimized extraction of daily bio-optical time series derived from MODIS/Aqua imagery for Lake Tanganyika, Africa
Horion, Stéphanie; Bergamino, Nadia; Stenuite, Stéphane et al

in Remote Sensing of Environment (2010)

Lake Tanganyika is one of the world's great freshwater ecosystems. In recent decades its hydrodynamic characteristics have undergone important changes that have had consequences on the lake's primary ... [more ▼]

Lake Tanganyika is one of the world's great freshwater ecosystems. In recent decades its hydrodynamic characteristics have undergone important changes that have had consequences on the lake's primary productivity. The establishment of a long-term Ocean Color dataset for Lake Tanganyika is a fundamental tool for understanding and monitoring these changes. We developed an approach to create a regionally calibrated dataset of chlorophyll-a concentrations (CHL) and attenuation coefficients at 490 nm (K490) for the period from July 2002 to December 2006 using daily calibrated radiances retrieved from the MODIS-Aqua sensor. Standard MODIS Aqua Ocean Color products were found to not provide a suitable calibration for high altitude lakes such as the Lake Tanganyika. An optimization of the extraction process and the validation of the dataset were performed with independent sets of in situ measurements. Our results show that for the geographical, atmospheric and optical conditions of Lake Tanganyika: (i) a coastal aerosol model set with high relative humidity (90%) provides a suitable atmospheric correction; (ii) a significant correlation between in situ data and CHL estimates using the MODIS specific OC3 algorithm is possible; and (iii) K490 estimates provide a good level of significance. The resulting validated time series of bio-optical properties provides a fundamental information base for the study of phytoplankton and primary production dynamics and interannual trends. A comparison between surface chlorophyll-a concentrations estimated from field monitoring and from the MODIS based dataset shows that remote sensing allows improved detection of surface blooms in Lake Tanganyika. [less ▲]

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See detailRadiative transfer based scaling of LAI/FPAR retrievals from reflectance data of different resolutions.
Tian, Y; Wang, Y; Zhang, Y et al

in Remote Sensing of Environment (2002), 84(1), 143-159

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See detailPatterns of seasonal and interannual changes of surface chlorophyll concentration in the Black Sea revealed from the remote sensed data.
Nezlin, Nikolay Pavlovich; Kostianoy, Andrey G.; Grégoire, Marilaure ULg

in Remote Sensing of Environment (1999), 69

Several years of CZCS-measured surface pigment’s ecosystems (e.g., Shushkina et al., 1995; Vinogradov et concentrations in the Black Sea are analyzed to appraise al., 1995; 1996a,b; Nihoul et al., 1998 ... [more ▼]

Several years of CZCS-measured surface pigment’s ecosystems (e.g., Shushkina et al., 1995; Vinogradov et concentrations in the Black Sea are analyzed to appraise al., 1995; 1996a,b; Nihoul et al., 1998). The analysis of the seasonal and year-to-year fluctuations of phytoplank- the images of the ocean color collected with the help of ton biomass and understand the causes of these fluctua- remote sensing seems to be one of the most productive tions in terms of the Black Sea’s general dynamics. The methods of estimation the general patterns of temporal pattern of seasonal variations is typical for subtropical and spatial variations of plant pigment concentration in rather than temperate regions. The range of the absolute surface layer of sea water. value of plant pigment surface concentration measured The chlorophyll concentrations measured by remote by remote sensing does not differ greatly from the values sensing methods are known to be the subject of serious measured by direct methods. The pattern of year-to-year discrepancies as compared with in situ measurements variations seems to correlate with cyclic oscillations of (e.g., Chavez, 1995; Martin and Perry, 1994; Mitchell, winter air temperature. In western shallow regions it is 1992; Nihoul et al., 1998). However, these observations also correlated with the Danube discharge intensity. are rather regular and numerous; thus they are worth at- More intensive winter–spring blooms and a slightly lower tention for the analysis of the variations of the Black level of pigment concentration during warm season are Sea’s ecosystem. typical for years of with a mild winter. The causes of The Coastal Zone Color Scanner (CZCS) was develthese regularities seem to be the peculiarities of hydrolog- oped by NASA. It was launched on the Nimbus-7 satelical and meteorological regimes of the Black Sea. The in- lite in October 1978. During its 7.5 year lifetime (Octotensity of winter–spring bloom of phytoplankton appears ber 1978–June 1986), the CZCS acquired nearly 68,000 to depend on hydrological mechanism (i.e., the intensity images, each covering up to 2 million square kilometers of water mixing during winter period due to thermic con- of ocean surface. The Nimbus Project Office in collabovection and wind mixing) rather than the illumination in- ration with the National Aeronautics and Space Administensity. [less ▲]

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