ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015
Spectral consistency with SPOT-VEGETATION is an important mission objective for PROBA-V, in parti... more Spectral consistency with SPOT-VEGETATION is an important mission objective for PROBA-V, in particular for its 1 km products. This must allow service providers such as the Copernicus Global Land Service to extend the 16-year long timeseries of SPOTVEGETATION global 1km data with similar PROBA-V products. To evaluate the extent of spectral consistency, an evaluation of spectral response differences is performed by applying the spectral response of PROBA-V and SPOT-VEGETATION 2 to a spectral library of representative global land cover conditions. Datasets for surface reflectance values and Normalized Difference Vegetation Index (NDVI) are thus established for both missions. Through linear regression between the two datasets, spectral correction functions are defined, which can be used to improve the spectral consistency between PROBA-V and SPOT-VEGETATION products. The correspondence between PROBA-V and SPOT-VEGETATION products is then evaluated for the overlapping period when product...
Understanding vegetation dynamics in Southern Africa in relation to climatic variability supports... more Understanding vegetation dynamics in Southern Africa in relation to climatic variability supports better management of natural resources. This study investigates the response of vegetation to rainfall variability in Southern Africa, based on a long-term time series of satellite images from SPOT-VGT and NOAA-AVHRR sensors. Of course, the differences in sensor-platform combination will hinder direct combination of these datasets. Therefore, the approach was, to first use VGT pre-processing as a benchmark for AVHRR pre-processing. Combination of the two NDVI datasets was improved afterwards by degrading the spectrally more accurate dataset, i.e. to decrease the dynamical range of the better' sensor rather than to upgrade the data from the oldest sensor to the new one. Comparison of the two NDVI datasets showed that though the values are similar, the discrepancy between the NDVI values of the sensors is spatially dependent. Applying empirical adjustment functions that account for th...
Belgian Scientific Research Programme on Remote Sensing by Satellite - phase four (Federal Office for Scientific, Technical and Cultural Affairs), contract T4/03/40, 1999-2001, final report, 132p (in Dutch with English summary)., 2001
2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), 2017
This paper discusses results from 12 months of a Round Robin exercise aimed at the inter-comparis... more This paper discusses results from 12 months of a Round Robin exercise aimed at the inter-comparison of different cloud detection algorithms for Proba-V. Clouds detection is a critical issue for satellite optical remote sensing, since potential errors in cloud masking directly translates into significant uncertainty in the retrieved downstream geophysical products. Cloud detection is particularly challenging for Proba-V due to the presence of a limited number of spectral bands and the lack of thermal infrared bands. The main objective of the project was the inter-comparison of several cloud detection algorithms for Proba-V over a wide range of surface types and environmental conditions. Proba-V Level 2a products have been distributed to six different algorithm providers representing companies and research institutes in several European countries. The considered cloud detection approaches are based on different strategies: Neural Network, Discriminant Analysis, Multi-spectral and Mult...
This paper introduces the methods of compiling the land-cover map of Northwest China(with an area... more This paper introduces the methods of compiling the land-cover map of Northwest China(with an area of approximately 3,100,000 km 2)using SPOT4-VEGETATION data sets and the validation techniques using high spatial resolution TM images. Based on the spectral reflectance, NDVI(Normalized difference Vegetation Index) and NDWI(Normalized Difference Water Index)time series data sets from SPOT4-VEGETATION, the land-cover map of Northwest China is compiled by applying the ISODATA unsuperviesd classification method. In order to evaluate this land-cover map's accuracy, 47 sampling units are selected from the whole mapping region. Each sample has a 25km×25km unit and evenness distribution as well as high heterogeneity. With the assumption of the TM interpretation in the 47 sampling units as the true land cover condition, we calculated every land cover type and its area percentage existing in every sampling unit in the SPOT4-VEGETAION map and the TM interpreted map, respectively. According to the statistical results, the land-cover classification system of the SPOT4-VEGETATION is modified,the sampling statistical histogram of the validation results in every province is established and the regression coefficient value is also calculated respectively. The validation results show that the land cover mapping of Northwest China using SPOT4-VEGETATION data sets and the ISODATE method can get an improving accuracy attributing to the high quality of SPOT4-VEGETATION data products and the effective method of spectral index combination of NDVI and NDWI. The reasons that reduce or impact the landcover classification accuracy mainly derive from two aspects:one is attributed to different landcover classes with same spectral characteristics, and the other is due to the mixed pixel problem. TO the former, adding some auxiliary information data such as DEM can reduce the possibility of misjudgment; to the later, the methods of mixed pixel decomposition and sub-pixel mapping may be good ways to increase the land cover classification and mapping accuracy.
High-resolution data are increasingly used for various applications, yet the revisit time is stil... more High-resolution data are increasingly used for various applications, yet the revisit time is still low for some applications, particularly in frequently cloud-covered areas. Therefore, sensors are often combined, which raises issues on data consistency. In this study, we start from L1 to L3 data, and investigate the impact of harmonization measures, correcting for difference in radiometric gain and spectral response function (SRF), and the use of a common processing chain with the same atmospheric correction for Sentinel-2A/B, Landsat-8, DEIMOS-1, and Proba-V center cameras. These harmonization measures are evaluated step-wise in two applications: (1) agricultural monitoring, and (2) hydrological modelling in an urban context, using biophysical parameters and NDVI. The evaluation includes validation with in situ data, relative consistency analysis between different sensors, and the evaluation of the time series noise. A higher accuracy was not obtained when validating against in sit...
<p>In order to assure the quality of operational satellite products, there ... more <p>In order to assure the quality of operational satellite products, there is a strong demand for timely available in-situ flux data. Typically, the Earth Observation Community has to rely on publicly available data processed and distributed by flux networks such as the European Fluxes Database Cluster, AmeriFlux Network, and other major networks globally.</p> <p>While the centralized processing systems employed by the major networks provide exceptional advantages for long-term data quality, reproducibility and comparability, to date these result in 1-5 year delays between the time of the actual in-situ flux measurement and the publicly online availability of processed and quality controlled data, especially for derived parameters such as Gross Primary Production (GPP) often used by Earth Observation experts. Such delays hamper the use of in-situ fluxes for timely (and ultimately near-real-time) operational satellite product monitoring, envisioned and often required by the Earth Observation Community.</p> <p>Within the European Copernicus Global Land Service (CGLOPS), a validation protocol is in place for each publicly available satellite product. One of the elements is the yearly Scientific Quality Evaluation (SQE), where data of the most recent calendar year are quality checked within the three months after the end of the year. This implies that in-situ data should be available within this timespan in order to be included in the operational quality monitoring. Recently, a set of new tools to collect, process, analyze, partition, time- and space- allocate and share time-synchronized flux data from multiple flux stations were developed and deployed globally. These new tools can be effective in solving the time delay issues listed above without sacrificing quality, reproducibility and comparability of the in-situ flux data.</p> <p>The fully automated remotely-accessible microcomputer, SmartFlux, utilizes EddyPro software  to calculate fully-processed fluxes in near-real-time, alongside supporting data and flux footprints. All data are merged into a single quality-controlled file timed using GPS-driven PTP time protocol to assure a microseconds-scale time synch between  the instruments within each station and between different stations.</p> <p>The flux data analysis software, Tovi, can seamlessly ingest the data from the SmartFlux stations to allow a non-micrometeorologist analyze and interpret the flux data. Specifically, it allows rapid execution of the QC/QA and data analysis steps using interactive GUI, including advanced QC and gap fill schemes, footprint calculations and flux apportioning, NEE (Net Ecosystem Exchange) flux partitioning, automated generation of specific lists of references for each workflow, etc. All processing routines and analysis steps are reproducibile and intercomparable to other SmartFlux stations across the globe.</p> <p>Based upon the timely needs for the in-situ flux data and the newly available technical tools, a pilot initiative was set-up to test the viability of using 2019 data generated by multiple SmartFlux stations and Tovi analysis software to quality control, gap fill, and partition NEE into GPP product to support the quality assurance analysis of the global Copernicus Dry Matter Productivity (DMP) product. This presentation will show the actual established workflow, and demonstrate the detailed post-processing of in-situ flux data for timely operational satellite product monitoring.</p>
PROBA-V (PRoject for On-Board Autonomy–Vegetation) was launched in May-2013 as an operational con... more PROBA-V (PRoject for On-Board Autonomy–Vegetation) was launched in May-2013 as an operational continuation to the vegetation (VGT) instruments on-board the Système Pour l’Observation de la Terre (SPOT)-4 and -5 satellites. The first reprocessing campaign of the PROBA-V archive from Collection 0 (C0) to Collection 1 (C1) aims at harmonizing the time series, thanks to improved radiometric and geometric calibration and cloud detection. The evaluation of PROBA-V C1 focuses on (i) qualitative and quantitative assessment of the new cloud detection scheme; (ii) quantification of the effect of the reprocessing by comparing C1 to C0; and (iii) evaluation of the spatio-temporal stability of the combined SPOT/VGT and PROBA-V archive through comparison to METOP/advanced very high resolution radiometer (AVHRR). The PROBA-V C1 cloud detection algorithm yields an overall accuracy of 89.0%. Clouds are detected with very few omission errors, but there is an overdetection of clouds over bright surfac...
Abstract After the end of the ‘Satellite Pour l'Observation de la Terre’ (SPOT) VEGETATION (S... more Abstract After the end of the ‘Satellite Pour l'Observation de la Terre’ (SPOT) VEGETATION (SPOT/VGT) mission in May/2014, the SPOT/VGT data archive, consisting of raw data coming from both the VEGETATION 1 (VGT1) and VEGETATION 2 (VGT2) instruments, was reprocessed, aiming at improved cloud screening and correcting for known artefacts such as the smile pattern in the VGT2 Blue band and the Sun-Earth distance bug in Top-of-Atmosphere reflectance calculation, with the objective of improving temporal consistency. The aim of this paper is to inform the user community of the changes in and the evaluation of the new SPOT/VGT Collection 3 (VGT-C3). The evaluation of the reprocessing is based on (i) the relative comparison between SPOT/VGT Collection 2 (VGT-C2) and VGT-C3 surface reflectances and Normalized Difference Vegetation Index (NDVI), (ii) consistency analysis between VGT1-C3 and VGT2-C3, and (iii) the comparison of the archive with external datasets from METOP/Advanced Very High Resolution Radiometer (AVHRR) and TERRA/Moderate Resolution Imaging Spectroradiometer (MODIS). Surface reflectances are slightly higher after the reprocessing, with larger differences in July compared to January, caused by the corrected Sun-Earth distance modelling. For NDVI, the overall impact of the reprocessing is relatively small and differences show no seasonality. Trends in the differences over the years are related to changes in calibration coefficients. Systematic differences between VGT1-C3 and VGT2-C3 surface reflectance are well below 1%, with largest bias between VGT1 and VGT2 for the NIR band and the NDVI (VGT2 > VGT1, especially for larger NDVI values). Both the comparison with METOP/AVHRR (surface reflectance and NDVI) and TERRA/MODIS (NDVI) reveal trends over time: systematic bias between VGT2 and METOP/AVHRR tends to decrease over time, while comparison with TERRA/MODIS indicates an increasing bias between VGT2 and MODIS. VGT2 NDVI seems to be gradually evolving to slightly larger values, which is consistent with the change in overpass time of VGT2 and the different illumination conditions caused by the orbital drift of the sensor. Results demonstrate however the SPOT/VGT-C3 archive is more stable over time compared to the previous archive, although bidirectional reflectance distribution function (BRDF) normalization is recommended in order to correct for bidirectional effects.
The forcing role of climate variability on vegetation was examined over East and Central Africa, ... more The forcing role of climate variability on vegetation was examined over East and Central Africa, using time series of satellite images and climate data. In search of a meteorological variable that best explains the temporal evolution of vegetation over multiple years, lagged correlations were tested between time series (1998-2011) of ten daily SPOT-VGT NDVI images and series of precipitation and soil moisture content from the ECMWF ERA-Interim re-analysis. First, it was found that NDVI displays an area-wide near-instantaneous response to soil moisture content as opposed to a regionally differing lagged response to precipitation. Second, interannual anomalies of NDVI correlate increasingly better with anomalies of soil moisture content in deeper soil layers, in a more spatially coherent pattern. This may be ascribed to the function of the soil as a buffering reservoir, reflected in relatively smooth series of soil moisture content with a gradual decline after the seasonal peak. Third...
In the face of climate change and for sustainable management of arid areas, it is crucial to unde... more In the face of climate change and for sustainable management of arid areas, it is crucial to understand the link between water availability and vegetation growth. This linkage is often investigated using time series of rainfall data and a vegetation index from satellite sensors (e.g. NDVI). However, rainfall alone is not sufficient to describe available water for vegetation, and other bio-geophysical factors should be taken into account. Hydrological modeling offers a powerful tool for this purpose. In this study, hydrological response units (HRU) delineated in a distributed hydrological model system (JAMS), were used to compute a complete water balance and to assess the vegetation response to climatic variability in a Mountain Basin (Córdoba, Argentina). Vegetation changes were analyzed through NDVI time series from SPOTVEGETATION images over the 12 year period 1998-2009. Precipitation was always the least correlated variable to vegetation growth, except in less vegetated rocky are...
Land surface reflectance measurements from the VEGETATION program (SPOT-4, SPOT-5 and PROBA-V sat... more Land surface reflectance measurements from the VEGETATION program (SPOT-4, SPOT-5 and PROBA-V satellites) have led to the acquisition of consistent time-series of Normalized Difference Vegetation Index (NDVI) at a global scale. The wide imaging swath (>2000 km) of the family of VEGETATION space-borne sensors ensures a daily coverage of the Earth at the expense of a varying observation and illumination geometries between successive orbit overpasses for a given target located on the ground. Such angular variations infer saw-like patterns on time-series of reflectance and NDVI. The presence of directional effects is not a real issue provided that they can be properly removed, which supposes an appropriate BRDF (Bidirectional Reflectance Distribution Function) sampling as offered by the VEGETATION program. An anisotropy correction supports a better analysis of the temporal shapes and spatial patterns of land surface reflectance values and vegetation indices such as NDVI. Herein we de...
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015
Spectral consistency with SPOT-VEGETATION is an important mission objective for PROBA-V, in parti... more Spectral consistency with SPOT-VEGETATION is an important mission objective for PROBA-V, in particular for its 1 km products. This must allow service providers such as the Copernicus Global Land Service to extend the 16-year long timeseries of SPOTVEGETATION global 1km data with similar PROBA-V products. To evaluate the extent of spectral consistency, an evaluation of spectral response differences is performed by applying the spectral response of PROBA-V and SPOT-VEGETATION 2 to a spectral library of representative global land cover conditions. Datasets for surface reflectance values and Normalized Difference Vegetation Index (NDVI) are thus established for both missions. Through linear regression between the two datasets, spectral correction functions are defined, which can be used to improve the spectral consistency between PROBA-V and SPOT-VEGETATION products. The correspondence between PROBA-V and SPOT-VEGETATION products is then evaluated for the overlapping period when product...
Understanding vegetation dynamics in Southern Africa in relation to climatic variability supports... more Understanding vegetation dynamics in Southern Africa in relation to climatic variability supports better management of natural resources. This study investigates the response of vegetation to rainfall variability in Southern Africa, based on a long-term time series of satellite images from SPOT-VGT and NOAA-AVHRR sensors. Of course, the differences in sensor-platform combination will hinder direct combination of these datasets. Therefore, the approach was, to first use VGT pre-processing as a benchmark for AVHRR pre-processing. Combination of the two NDVI datasets was improved afterwards by degrading the spectrally more accurate dataset, i.e. to decrease the dynamical range of the better' sensor rather than to upgrade the data from the oldest sensor to the new one. Comparison of the two NDVI datasets showed that though the values are similar, the discrepancy between the NDVI values of the sensors is spatially dependent. Applying empirical adjustment functions that account for th...
Belgian Scientific Research Programme on Remote Sensing by Satellite - phase four (Federal Office for Scientific, Technical and Cultural Affairs), contract T4/03/40, 1999-2001, final report, 132p (in Dutch with English summary)., 2001
2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), 2017
This paper discusses results from 12 months of a Round Robin exercise aimed at the inter-comparis... more This paper discusses results from 12 months of a Round Robin exercise aimed at the inter-comparison of different cloud detection algorithms for Proba-V. Clouds detection is a critical issue for satellite optical remote sensing, since potential errors in cloud masking directly translates into significant uncertainty in the retrieved downstream geophysical products. Cloud detection is particularly challenging for Proba-V due to the presence of a limited number of spectral bands and the lack of thermal infrared bands. The main objective of the project was the inter-comparison of several cloud detection algorithms for Proba-V over a wide range of surface types and environmental conditions. Proba-V Level 2a products have been distributed to six different algorithm providers representing companies and research institutes in several European countries. The considered cloud detection approaches are based on different strategies: Neural Network, Discriminant Analysis, Multi-spectral and Mult...
This paper introduces the methods of compiling the land-cover map of Northwest China(with an area... more This paper introduces the methods of compiling the land-cover map of Northwest China(with an area of approximately 3,100,000 km 2)using SPOT4-VEGETATION data sets and the validation techniques using high spatial resolution TM images. Based on the spectral reflectance, NDVI(Normalized difference Vegetation Index) and NDWI(Normalized Difference Water Index)time series data sets from SPOT4-VEGETATION, the land-cover map of Northwest China is compiled by applying the ISODATA unsuperviesd classification method. In order to evaluate this land-cover map's accuracy, 47 sampling units are selected from the whole mapping region. Each sample has a 25km×25km unit and evenness distribution as well as high heterogeneity. With the assumption of the TM interpretation in the 47 sampling units as the true land cover condition, we calculated every land cover type and its area percentage existing in every sampling unit in the SPOT4-VEGETAION map and the TM interpreted map, respectively. According to the statistical results, the land-cover classification system of the SPOT4-VEGETATION is modified,the sampling statistical histogram of the validation results in every province is established and the regression coefficient value is also calculated respectively. The validation results show that the land cover mapping of Northwest China using SPOT4-VEGETATION data sets and the ISODATE method can get an improving accuracy attributing to the high quality of SPOT4-VEGETATION data products and the effective method of spectral index combination of NDVI and NDWI. The reasons that reduce or impact the landcover classification accuracy mainly derive from two aspects:one is attributed to different landcover classes with same spectral characteristics, and the other is due to the mixed pixel problem. TO the former, adding some auxiliary information data such as DEM can reduce the possibility of misjudgment; to the later, the methods of mixed pixel decomposition and sub-pixel mapping may be good ways to increase the land cover classification and mapping accuracy.
High-resolution data are increasingly used for various applications, yet the revisit time is stil... more High-resolution data are increasingly used for various applications, yet the revisit time is still low for some applications, particularly in frequently cloud-covered areas. Therefore, sensors are often combined, which raises issues on data consistency. In this study, we start from L1 to L3 data, and investigate the impact of harmonization measures, correcting for difference in radiometric gain and spectral response function (SRF), and the use of a common processing chain with the same atmospheric correction for Sentinel-2A/B, Landsat-8, DEIMOS-1, and Proba-V center cameras. These harmonization measures are evaluated step-wise in two applications: (1) agricultural monitoring, and (2) hydrological modelling in an urban context, using biophysical parameters and NDVI. The evaluation includes validation with in situ data, relative consistency analysis between different sensors, and the evaluation of the time series noise. A higher accuracy was not obtained when validating against in sit...
<p>In order to assure the quality of operational satellite products, there ... more <p>In order to assure the quality of operational satellite products, there is a strong demand for timely available in-situ flux data. Typically, the Earth Observation Community has to rely on publicly available data processed and distributed by flux networks such as the European Fluxes Database Cluster, AmeriFlux Network, and other major networks globally.</p> <p>While the centralized processing systems employed by the major networks provide exceptional advantages for long-term data quality, reproducibility and comparability, to date these result in 1-5 year delays between the time of the actual in-situ flux measurement and the publicly online availability of processed and quality controlled data, especially for derived parameters such as Gross Primary Production (GPP) often used by Earth Observation experts. Such delays hamper the use of in-situ fluxes for timely (and ultimately near-real-time) operational satellite product monitoring, envisioned and often required by the Earth Observation Community.</p> <p>Within the European Copernicus Global Land Service (CGLOPS), a validation protocol is in place for each publicly available satellite product. One of the elements is the yearly Scientific Quality Evaluation (SQE), where data of the most recent calendar year are quality checked within the three months after the end of the year. This implies that in-situ data should be available within this timespan in order to be included in the operational quality monitoring. Recently, a set of new tools to collect, process, analyze, partition, time- and space- allocate and share time-synchronized flux data from multiple flux stations were developed and deployed globally. These new tools can be effective in solving the time delay issues listed above without sacrificing quality, reproducibility and comparability of the in-situ flux data.</p> <p>The fully automated remotely-accessible microcomputer, SmartFlux, utilizes EddyPro software  to calculate fully-processed fluxes in near-real-time, alongside supporting data and flux footprints. All data are merged into a single quality-controlled file timed using GPS-driven PTP time protocol to assure a microseconds-scale time synch between  the instruments within each station and between different stations.</p> <p>The flux data analysis software, Tovi, can seamlessly ingest the data from the SmartFlux stations to allow a non-micrometeorologist analyze and interpret the flux data. Specifically, it allows rapid execution of the QC/QA and data analysis steps using interactive GUI, including advanced QC and gap fill schemes, footprint calculations and flux apportioning, NEE (Net Ecosystem Exchange) flux partitioning, automated generation of specific lists of references for each workflow, etc. All processing routines and analysis steps are reproducibile and intercomparable to other SmartFlux stations across the globe.</p> <p>Based upon the timely needs for the in-situ flux data and the newly available technical tools, a pilot initiative was set-up to test the viability of using 2019 data generated by multiple SmartFlux stations and Tovi analysis software to quality control, gap fill, and partition NEE into GPP product to support the quality assurance analysis of the global Copernicus Dry Matter Productivity (DMP) product. This presentation will show the actual established workflow, and demonstrate the detailed post-processing of in-situ flux data for timely operational satellite product monitoring.</p>
PROBA-V (PRoject for On-Board Autonomy–Vegetation) was launched in May-2013 as an operational con... more PROBA-V (PRoject for On-Board Autonomy–Vegetation) was launched in May-2013 as an operational continuation to the vegetation (VGT) instruments on-board the Système Pour l’Observation de la Terre (SPOT)-4 and -5 satellites. The first reprocessing campaign of the PROBA-V archive from Collection 0 (C0) to Collection 1 (C1) aims at harmonizing the time series, thanks to improved radiometric and geometric calibration and cloud detection. The evaluation of PROBA-V C1 focuses on (i) qualitative and quantitative assessment of the new cloud detection scheme; (ii) quantification of the effect of the reprocessing by comparing C1 to C0; and (iii) evaluation of the spatio-temporal stability of the combined SPOT/VGT and PROBA-V archive through comparison to METOP/advanced very high resolution radiometer (AVHRR). The PROBA-V C1 cloud detection algorithm yields an overall accuracy of 89.0%. Clouds are detected with very few omission errors, but there is an overdetection of clouds over bright surfac...
Abstract After the end of the ‘Satellite Pour l'Observation de la Terre’ (SPOT) VEGETATION (S... more Abstract After the end of the ‘Satellite Pour l'Observation de la Terre’ (SPOT) VEGETATION (SPOT/VGT) mission in May/2014, the SPOT/VGT data archive, consisting of raw data coming from both the VEGETATION 1 (VGT1) and VEGETATION 2 (VGT2) instruments, was reprocessed, aiming at improved cloud screening and correcting for known artefacts such as the smile pattern in the VGT2 Blue band and the Sun-Earth distance bug in Top-of-Atmosphere reflectance calculation, with the objective of improving temporal consistency. The aim of this paper is to inform the user community of the changes in and the evaluation of the new SPOT/VGT Collection 3 (VGT-C3). The evaluation of the reprocessing is based on (i) the relative comparison between SPOT/VGT Collection 2 (VGT-C2) and VGT-C3 surface reflectances and Normalized Difference Vegetation Index (NDVI), (ii) consistency analysis between VGT1-C3 and VGT2-C3, and (iii) the comparison of the archive with external datasets from METOP/Advanced Very High Resolution Radiometer (AVHRR) and TERRA/Moderate Resolution Imaging Spectroradiometer (MODIS). Surface reflectances are slightly higher after the reprocessing, with larger differences in July compared to January, caused by the corrected Sun-Earth distance modelling. For NDVI, the overall impact of the reprocessing is relatively small and differences show no seasonality. Trends in the differences over the years are related to changes in calibration coefficients. Systematic differences between VGT1-C3 and VGT2-C3 surface reflectance are well below 1%, with largest bias between VGT1 and VGT2 for the NIR band and the NDVI (VGT2 > VGT1, especially for larger NDVI values). Both the comparison with METOP/AVHRR (surface reflectance and NDVI) and TERRA/MODIS (NDVI) reveal trends over time: systematic bias between VGT2 and METOP/AVHRR tends to decrease over time, while comparison with TERRA/MODIS indicates an increasing bias between VGT2 and MODIS. VGT2 NDVI seems to be gradually evolving to slightly larger values, which is consistent with the change in overpass time of VGT2 and the different illumination conditions caused by the orbital drift of the sensor. Results demonstrate however the SPOT/VGT-C3 archive is more stable over time compared to the previous archive, although bidirectional reflectance distribution function (BRDF) normalization is recommended in order to correct for bidirectional effects.
The forcing role of climate variability on vegetation was examined over East and Central Africa, ... more The forcing role of climate variability on vegetation was examined over East and Central Africa, using time series of satellite images and climate data. In search of a meteorological variable that best explains the temporal evolution of vegetation over multiple years, lagged correlations were tested between time series (1998-2011) of ten daily SPOT-VGT NDVI images and series of precipitation and soil moisture content from the ECMWF ERA-Interim re-analysis. First, it was found that NDVI displays an area-wide near-instantaneous response to soil moisture content as opposed to a regionally differing lagged response to precipitation. Second, interannual anomalies of NDVI correlate increasingly better with anomalies of soil moisture content in deeper soil layers, in a more spatially coherent pattern. This may be ascribed to the function of the soil as a buffering reservoir, reflected in relatively smooth series of soil moisture content with a gradual decline after the seasonal peak. Third...
In the face of climate change and for sustainable management of arid areas, it is crucial to unde... more In the face of climate change and for sustainable management of arid areas, it is crucial to understand the link between water availability and vegetation growth. This linkage is often investigated using time series of rainfall data and a vegetation index from satellite sensors (e.g. NDVI). However, rainfall alone is not sufficient to describe available water for vegetation, and other bio-geophysical factors should be taken into account. Hydrological modeling offers a powerful tool for this purpose. In this study, hydrological response units (HRU) delineated in a distributed hydrological model system (JAMS), were used to compute a complete water balance and to assess the vegetation response to climatic variability in a Mountain Basin (Córdoba, Argentina). Vegetation changes were analyzed through NDVI time series from SPOTVEGETATION images over the 12 year period 1998-2009. Precipitation was always the least correlated variable to vegetation growth, except in less vegetated rocky are...
Land surface reflectance measurements from the VEGETATION program (SPOT-4, SPOT-5 and PROBA-V sat... more Land surface reflectance measurements from the VEGETATION program (SPOT-4, SPOT-5 and PROBA-V satellites) have led to the acquisition of consistent time-series of Normalized Difference Vegetation Index (NDVI) at a global scale. The wide imaging swath (>2000 km) of the family of VEGETATION space-borne sensors ensures a daily coverage of the Earth at the expense of a varying observation and illumination geometries between successive orbit overpasses for a given target located on the ground. Such angular variations infer saw-like patterns on time-series of reflectance and NDVI. The presence of directional effects is not a real issue provided that they can be properly removed, which supposes an appropriate BRDF (Bidirectional Reflectance Distribution Function) sampling as offered by the VEGETATION program. An anisotropy correction supports a better analysis of the temporal shapes and spatial patterns of land surface reflectance values and vegetation indices such as NDVI. Herein we de...
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