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Land surface phenology has been widely retrieved although no consensus exists on the optimal satellite dataset and the method to extract phenology metrics. This study is the first comprehensive comparison of vegetation variables and methods to retrieve land surface phenology for 1999–2017 time series of Copernicus Global Land products derived from SPOT-VEGETATION and PROBA-V data. We investigated the sensitivity of phenology to (I) the input vegetation variable: normalized difference vegetation index (NDVI), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fraction of vegetation cover (FCOVER); (II) the smoothing and gap filling method for deriving seasonal trajectories; and (III) the method to extract phenological metrics: thresholds based on a percentile of the annual amplitude of the vegetation variable, autoregressive moving averages, logistic function fitting, and first derivative methods. We validated the derived satellite phenological metrics (start of the season (SoS) and end of the season (EoS)) using available ground observations of Betula pendula, B. alleghaniensis, Acer rubrum, Fagus grandifolia, and Quercus rubra in Europe (Pan-European PEP725 network) and the USA (National Phenology Network, USA-NPN). The threshold-based method applied to the smoothed and gap-filled LAI V2 time series agreed best with the ground phenology, with root mean square errors of ˜10 d and ˜25 d for the timing of SoS and EoS respectively. This research is expected to contribute for the operational retrieval of land surface phenology within the Copernicus Global Land Service.

The application of Monteith’s models to estimate the gross primary production (GPP) of forest ecosystems is a common and efficient practice. A modified version of one of these parametric models, C-Fix, has been widely tested in Italy at daily to annual temporal scales. The current paper is a step forward demonstrating the capability of this model to reproduce also inter-annual forest GPP variations. To this aim, dendrochronological measurements collected in two study areas were utilized to validate the model. The first study area is a coastal Mediterranean pine forest and the second is a more temperate ecosystem dominated by the presence of European beech. The experiment was organized into the following main steps. Modified C-Fix was applied to simulate annual GPP over a 15-year period for both sites. Both the main driving factors used by the model and the estimated GPP were then analysed and assessed against the detrended tree-ring width series of the collected cores. The study yields indications on the factors most influential on GPP for the two forest ecosystems and supports the capability of the proposed modelling approach to reproduce relevant inter-annual GPP variations, which is useful for predicting the impacts of ongoing climatic changes.

High-quality retrieval of land surface phenology (LSP) is increasingly important for understanding the effects of climate change on ecosystem function and biosphere–atmosphere interactions. We analyzed four state-of-the-art phenology methods: threshold, logistic-function, moving-average and first derivative based approaches, and retrieved LSP in the North Hemisphere for the period 1999–2017 from Copernicus Global Land Service (CGLS) SPOT-VEGETATION and PROBA-V leaf area index (LAI) 1 km V2.0 time series. We validated the LSP estimates with near-surface PhenoCam and eddy covariance FLUXNET data over 80 sites of deciduous forests. Results showed a strong correlation (R2 > 0.7) between the satellite LSP and ground-based observations from both PhenoCam and FLUXNET for the timing of the start (SoS) and R2 > 0.5 for the end of season (EoS). The threshold-based method performed the best with a root mean square error of ~9 d with PhenoCam and ~7 d with FLUXNET for the timing of SoS (30th percentile of the annual amplitude), and ~12 d and ~10 d, respectively, for the timing of EoS (40th percentile).

In the Sahel region, moderate to coarse spatial resolution remote sensing time series are used in early warning monitoring systems with the aim of detecting unfavorable crop and pasture conditions and informing stakeholders about impending food security risks. Despite growing evidence that vegetation productivity is directly related to phenology, most approaches to estimate such risks do not explicitly take into account the actual timing of vegetation growth and development. The date of the start of the season (SOS) or of the peak canopy density can be assessed by remote sensing techniques in a timely manner during the growing season. However, there is limited knowledge about the relationship between vegetation biomass production and these variables at the regional scale. This study describes the first attempt to increase our understanding of such a relationship through the analysis of phenological variables retrieved from SPOT-VEGETATION time series of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). Two key phenological variables (growing season length (GSL); timing of SOS) and the maximum value of FAPAR attained during the growing season (Peak) are analyzed as potentially related to a proxy of biomass production (CFAPAR, the cumulative value of FAPAR during the growing season). GSL, SOS and Peak all show different spatial patterns of correlation with CFAPAR. In particular, GSL shows a high and positive correlation with CFAPAR over the whole Sahel (mean r = 0.78). The negative correlation between delays in SOS and CFAPAR is stronger (mean r = −0.71) in the southern agricultural band of the Sahel, while the positive correlation between Peak FAPAR and CFAPAR is higher in the northern and more arid grassland region (mean r = 0.75). The consistency of the results and the actual link between remote sensing-derived phenological parameters and biomass production were evaluated using field measurements of aboveground herbaceous biomass of rangelands in Senegal. This study demonstrates the potential of phenological variables as indicators of biomass production. Nevertheless, the strength of the relation between phenological variables and biomass production is not universal and indeed quite variable geographically, with large scattered areas not showing a statistically significant relationship.

A processing of remotely-sensed Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) time series at 1-km spatial resolution is established to estimate sugarcane yield over the state of São Paulo, Brazil. It includes selecting adequate time series according to the signal spatial purity, using thermal time instead of calendar time and smoothing temporally the irregularly sampled observations. A systematic construction of various metrics and their capacity to predict yield is explored to identify the best performance, and see how timely the yield forecast can be made. The resulting dataset not only reveals a strong spatio-temporal structure, but is also capable of detecting both absolute changes in biomass accumulation and changes in its inter-annual variability. Sugarcane yield can thus be estimated with a RMSE of 1.5 t/ha (or 2%) without taking into account the strong linear trend in yield increase witnessed in the past decade. Including the trend reduces the error to 0.6 t/ha, correctly predicting whether the yield in a given year is above or below the trend in 90% of cases. The methodological framework presented here could be applied beyond the specific case of sugarcane in São Paulo, namely to other crops in other agro-ecological landscapes, to enhance current systems for monitoring agriculture or forecasting yield using remote sensing.

The analysis of vegetation dynamics is essential in semi-arid regions, in particular because of the frequent occurrence of long periods of drought. In this paper, multi-temporal series of the Normalized Difference of Vegetation Index (NDVI), derived from SPOT-VEGETATION satellite data between September 1998 and June 2010, were used to analyze the vegetation dynamics over the semi-arid central region of Tunisia. A study of the persistence of three types of vegetation (pastures, annual agriculture and olive trees) is proposed using fractal analysis, in order to gain insight into the stability/instability of vegetation dynamics. In order to estimate the state of vegetation cover stress, we propose evaluating the properties of an index referred to as the Vegetation Anomaly Index (VAI). A positive VAI indicates high vegetation dynamics, whereas a negative VAI indicates the presence of vegetation stress. The VAI is tested for the above three types of vegetation, during the study period from 1998 to 2010, and is compared with other drought indices. The VAI is found to be strongly correlated with precipitation.

The application of Monteith’s models to estimate the gross primary production (GPP) of forest ecosystems is a common and efficient practice. A modified version of one of these parametric models, C-Fix, has been widely tested in Italy at daily to annual temporal scales. The current paper is a step forward demonstrating the capability of this model to reproduce also inter-annual forest GPP variations. To this aim, dendrochronological measurements collected in two study areas were utilized to validate the model. The first study area is a coastal Mediterranean pine forest and the second is a more temperate ecosystem dominated by the presence of European beech. The experiment was organized into the following main steps. Modified C-Fix was applied to simulate annual GPP over a 15-year period for both sites. Both the main driving factors used by the model and the estimated GPP were then analysed and assessed against the detrended tree-ring width series of the collected cores. The study yields indications on the factors most influential on GPP for the two forest ecosystems and supports the capability of the proposed modelling approach to reproduce relevant inter-annual GPP variations, which is useful for predicting the impacts of ongoing climatic changes. [ABSTRACT FROM PUBLISHER]

Land surface phenology derived from remotely sensed satellite data can substantially improve our macroecological knowledge and the representation of phenology in earth system models. We characterized the baseline phenology of the vegetation at the global scale from the GEOCLIM climatology of leaf area index (LAI) estimated from 1-km SPOT-VEGETATION time series for 1999–2010. The phenological metrics were calibrated over an ensemble of ground observations of the timing of leaf unfolding and autumnal colouring of leaves. The start and end of season were best identified using respectively 30% and 40% threshold of LAI amplitude values. The accuracy of the derived phenological metrics, evaluated using available ground observations for birch forests over Europe (and lilac shrubs over North America), improved as compared to those derived from MODIS-EVI and produced an overall root mean square error of 7 days (19 days) for the timing of the start of season, 15 for the end of season, and 16 for the length of season. The spatial patterns of the derived LAI phenology agreed well with those from MODIS-EVI and -NDVI, although the timing of the start, end, and length of season differed by about one month at the global scale, with higher uncertainties in areas of limited seasonality of the satellite signal and systematic biases due to the differences in the methodologies and datasets. The baseline LAI phenology was spatially consistent with the global distributions of climatic drivers and biome land cover. [ABSTRACT FROM AUTHOR]

Este estudo objetivou avaliar o índice de Vegetação Padronizado (IVP) no monitoramento indicativo de estiagens em períodos críticos da soja em municípios da região Sul do Brasil que plantam mais de 7000 ha da leguminosa. Para tanto, foram utilizados dados SPOT-Vegetation do período de 1998 a 2012 para obtenção da anomalia do índice de vegetação com a finalidade de avaliar o vigor da vegetação no período compreendido entre o 1° decêndio de dezembro ao 3° decêndio de fevereiro, dos períodos agrícolas 2004/2005 a 2011/2012. Anomalias positivas de IVP indicam bom vigor da vegetação e anomalias negativas são indicativos de ocorrência de estiagem. Para subsidiar os resultados, foram calculadas as anomalias de precipitação a partir dos dados do TRMM (Tropical Rainfall Measuring Mission). Nos períodos analisados de 2004/2005, 2008/2009 e 2011/2012, foram obtidas as maiores áreas com anomalias negativas de IVP, ou seja, com baixo vigor da vegetação, coincidindo com períodos de ocorrência de estiagens na região sul do Brasil. De forma geral, os resultados comprovam o potencial do IVP para o monitoramento indicativo de estiagens podendo auxiliar na tomada de decisão para o setor agrícola.

Livestock plays a central economic role in Niger, but it is highly vulnerable due to the high inter-annual variability of rain and hence pasture production. This study aims to develop an approach for mapping pasture biomass production to support activities of the Niger Ministry of Livestock for effective pasture management. Our approach utilises the observed spatiotemporal variability of biomass production to build a predictive model based on ground and remote sensing data for the period 1998-2012. Measured biomass (63 sites) at the end of the growing season was used for the model parameterisation. The seasonal cumulative Fraction of Absorbed Photosynthetically Active Radiation (CFAPAR), calculated from 10-day image composites of SPOT-VEGETATION FAPAR, was computed as a phenology-tuned proxy of biomass production. A linear regression model was tested aggregating field data at different levels (global, department, agro-ecological zone, and intersection of agro-ecological and department units) and subjected to a cross validation (cv) by leaving one full year out. An increased complexity (i.e. spatial detail) of the model increased the estimation performances indicating the potential relevance of additional and spatially heterogeneous agro-ecological characteristics for the relationship between herbaceous biomass at the end of the season and CFAPAR. The model using the department aggregation yielded the best trade-off between model complexity and predictive power (R² = 0.55, R²cv = 0.48). The proposed approach can be used to timely produce maps of estimated biomass at the end of the growing season before ground point measurements are made available. [ABSTRACT FROM AUTHOR]

Annual maps of the remote sensing green-up date derived from SPOT-VEGETATION data were compared to the phenological observations collected by the PlantWatch citizen science project across Canada between 1998 and 2012. Green-up dates were found to relate to the leaf-out dates for four woody species ( Populus tremuloides , Acer rubrum , Syringa vulgaris , Larix laricina ), with a RMSE from 13.6 to 15.6 days. This was true for all landcover types except in pixels where agriculture or water bodies were dominant. This is less accurate than the results from previous studies for boreal Eurasia (RMSE = 8.7 days), with phenology data from an operational network. When data were aggregated at a regional level, the remote sensing green-up date matched well with the interannual variations in leafing and also in flowering of most of the recorded species. These included spring events for trees, shrubs and non-woody plants which were either native to Canada or introduced. For most plants, spring flowering and leafing times are functions of accumulated temperature. For this reason, plant species develop in a predictable sequence, and interannual variations in this cohort of species leafing and flowering are correlated. This explains the correlation with remote sensing green-up. Data from this volunteer PlantWatch network proved consistent with independent satellite data, suggesting that combining the two will strengthen the future capacity to monitor vegetation changes. [ABSTRACT FROM AUTHOR]

In agroecosystems, the annual carbon (C) inputs to soil are one of the most promising greenhouse gas mitigation options. Net primary productivity (NPP) provides the inputs of C in ecosystems that can potentially be sequestered in soil organic matter. In this study, we estimates the C density and accumulation rate in rice-wheat agroecosystem at four sites of Indo-Gangetic Plains and Vindhyan highlands through amalgamation of ground truth (GT) and remote sensing (RS) approach. In addition to this, we validated field-measured aboveground net production (ANP) with remotely sensed SPOT-Vegetation data. ANP of the sites ranged from 6.8 to 11.1 and 3.3 to 8.8 t-C ha year for GT and RS, respectively. Both estimates (GT and RS) were linearly and significantly related with each other ( y = 1.33 x − 5.82, R = 0.93, P = 0.04; where x = GT ANP and y = RS ANP). For the whole region, total NPP (rice + wheat) was ranged from 7.9 to 12.5 t-C ha year. The C accumulation potential of the present agroecosystems was 9.1 and 1.9 t-C ha year in the form of ANP and belowground NPP (BNP), respectively. The aboveground C stock of agroecosystem allocated in foliage (7.8 %), stem (57.6 %), and grain (35.1 %). Survey about the fate of post-harvested materials indicated that aboveground portion of the crop is almost used completely within the year for different purposes including human consumption and cattle feeding. Therefore, only BNP and litter contribute to long-term C sequestration. Since agroecosystem has enormous potential to sequester C, better management of aboveground portion NPP of the crop may enhance C sequestration potential of agroecosystem. [ABSTRACT FROM AUTHOR]

Monitoring phenological change in agricultural land improves our understanding of the adaptation of crops to a warmer climate. Winter wheat–maize and winter wheat–cotton double-cropping are practised in most agricultural areas in the North China Plain. A curve-fitting method is presented to derive winter wheat phenology from SPOT-VEGETATION S10 normalized difference vegetation index (NDVI) data products. The method uses a double-Gaussian model to extract two phenological metrics, the start of season (SOS) and the time of maximum NDVI (MAXT). The results are compared with phenological records at local agrometeorological stations. The SOS and MAXT have close agreement within situobservations of the jointing date and milk-in-kernel date respectively. The phenological metrics detected show spatial variations that are consistent with known phenological characteristics. This study indicates that time-series analysis with satellite data could be an effective tool for monitoring the phenology of crops and its spatial distribution in a large agricultural region. [ABSTRACT FROM AUTHOR]

A processing of remotely-sensed Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) time series at 1-km spatial resolution is established to estimate sugarcane yield over the state of São Paulo, Brazil. It includes selecting adequate time series according to the signal spatial purity, using thermal time instead of calendar time and smoothing temporally the irregularly sampled observations. A systematic construction of various metrics and their capacity to predict yield is explored to identify the best performance, and see how timely the yield forecast can be made. The resulting dataset not only reveals a strong spatio-temporal structure, but is also capable of detecting both absolute changes in biomass accumulation and changes in its inter-annual variability. Sugarcane yield can thus be estimated with a RMSE of 1.5 t/ha (or 2%) without taking into account the strong linear trend in yield increase witnessed in the past decade. Including the trend reduces the error to 0.6 t/ha, correctly predicting whether the yield in a given year is above or below the trend in 90% of cases. The methodological framework presented here could be applied beyond the specific case of sugarcane in São Paulo, namely to other crops in other agro-ecological landscapes, to enhance current systems for monitoring agriculture or forecasting yield using remote sensing. [ABSTRACT FROM AUTHOR]

Abstract: The analysis and forecasting of extreme climatic events has become increasingly relevant to plan effective financial and food-related interventions in third-world countries. Natural disasters and climate change, both large and small scale, have a great impact on non-industrialized populations who rely exclusively on activities such as crop production, fishing, and similar livelihood activities. It is important to identify the extent of the areas prone to severe drought conditions in order to study the possible consequences of the drought on annual crop production. In this paper, we aim to identify such areas within the South Tigray zone, Ethiopia, using a transformation of the Normalized Difference Vegetation Index (NDVI) called Absolute Difference NDVI (ADVI). Negative NDVI shifts from the historical average can generally be linked to a reduction in the vigor of local vegetation. Drought is more likely to increase in areas where negative shifts occur more frequently and with high magnitude, making it possible to spot critical situations. We propose a new methodology for the assessment of drought risk in areas where crop production represents a primary source of livelihood for its inhabitants. We estimate ADVI return levels pixel per pixel by fitting extreme value models to independent monthly minima. The study is conducted using SPOT-Vegetation (VGT) ten-day composite (S10) images from April 1998 to March 2009. In all short-term and long-term predictions, we found that central and southern areas of the South Tigray zone are prone to a higher drought risk compared to other areas. [Copyright &y& Elsevier]

The analysis of vegetation dynamics is essential in semi-arid regions, in particular because of the frequent occurrence of long periods of drought. In this paper, multi-temporal series of the Normalized Difference of Vegetation Index (NDVI), derived from SPOT-VEGETATION satellite data between September 1998 and June 2010, were used to analyze the vegetation dynamics over the semi-arid central region of Tunisia. A study of the persistence of three types of vegetation (pastures, annual agriculture and olive trees) is proposed using fractal analysis, in order to gain insight into the stability/instability of vegetation dynamics. In order to estimate the state of vegetation cover stress, we propose evaluating the properties of an index referred to as the Vegetation Anomaly Index (VAI). A positive VAI indicates high vegetation dynamics, whereas a negative VAI indicates the presence of vegetation stress. The VAI is tested for the above three types of vegetation, during the study period from 1998 to 2010, and is compared with other drought indices. The VAI is found to be strongly correlated with precipitation. [ABSTRACT FROM AUTHOR]

The monitoring of terrestrial carbon dynamics is important in studies related with global climate change. This paper presents results of the inter-annual variability of Net Primary Productivity (NPP) from 1981 to 2000 derived using observations from NOAA-AVHRR data using Global Production Efficiency Model (GloPEM). The GloPEM model is based on physiological principles and uses the production efficiency concept, in which the canopy absorption of photosynthetically active radiation (APAR) is used with a conversion 'efficiency' to estimate Gross Primary Production (GPP). NPP derived from GloPEM model over India showed maximum NPP about 3,000 gCmyear in west Bengal and lowest up to 500 gCmyear in Rajasthan. The India averaged NPP varied from 1,084.7 gCmyear to 1,390.8 gCmyear in the corresponding years of 1983 and 1998 respectively. The regression analysis of the 20 year NPP variability showed significant increase in NPP over India ( r = 0.7, F = 17.53, p < 0.001). The mean rate of increase was observed as 10.43 gCmyear. Carbon fixation ability of terrestrial ecosystem of India is increasing with rate of 34.3 TgC annually ( t = 4.18, p < 0.001). The estimated net carbon fixation over Indian landmass ranged from 3.56 PgC (in 1983) to 4.57 PgC (in 1998). Grid level temporal correlation analysis showed that agricultural regions are the source of increase in terrestrial NPP of India. Parts of forest regions (Himalayan in Nepal, north east India) are relatively less influenced over the study period and showed lower or negative correlation (trend). Finding of the study would provide valuable input in understanding the global change associated with vegetation activities as a sink for atmospheric carbon dioxide. [ABSTRACT FROM AUTHOR]

Abstract: The goal of this study is to propose a new classification of African ecosystems based on an 8-year analysis of Normalized Difference Vegetation Index (NDVI) data sets from SPOT/VEGETATION. We develop two methods of classification. The first method is obtained from a k-nearest neighbour (k-NN) classifier, which represents a simple machine learning algorithm in pattern recognition. The second method is hybrid in that it combines k-NN clustering, hierarchical principles and the Fast Fourier Transform (FFT). The nomenclature of the two classifications relies on three levels of vegetation structural categories based on the Land Cover Classification System (LCCS). The two main outcomes are: (i) The delineation of the spatial distribution of ecosystems into five bioclimatic ecoregions at the African continental scale; (ii) Two ecosystem maps were made sequentially: an initial map with 92 ecosystems from the k-NN, plus a deduced hybrid classification with 73 classes, which better reflects the bio-geographical patterns. The inclusion of bioclimatic information and successive k-NN clustering elements helps to enhance the discrimination of ecosystems. Adopting this hybrid approach makes the ecosystem identification and labelling more flexible and more accurate in comparison to straightforward methods of classification. The validation of the hybrid classification, conducted by crossing-comparisons with validated continental maps, displayed a mapping accuracy of 54% to 61%. [ABSTRACT FROM AUTHOR]

Multi-temporal series of satellite SPOT-VEGETATION normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) data from 1998 to 2007 were used for analyzing vegetation change of the ecotone in the west of the Northeast China Plain. The yearly and monthly maximal values, anomalies and change rates of NDVI and NDWI were calculated to reveal the interannual and seasonal changes in vegetation cover and vegetation water content. Linear regression method was adopted to characterize the trends in vegetation change. The yearly maximal NDVI decreased from 0.41 in 1998 to 0.37 in 2007, implying the decreasing trend of vegetation activity. There was a significant decrease of maximal NDVI in spring and summer over the study period, while an increase trend was observed in autumn. The vegetation-improved regions and vegetation-degraded regions occupied 17.03% and 20.30% of the study area, respectively. The maximal NDWI over growing season dropped by 0.027 in 1998–2007, and about 15.15% of the study area showed a decreasing trend of water content. Vegetation water stress in autumn was better than that in spring. Vegetation cover and water content variations were sensitive to annual precipitation, autumn precipitation and summer temperature. The vegetation degradation trend in this ecotone might be induced by the warm-drying climate especially continuous spring and summer drought in the recent ten years. [ABSTRACT FROM AUTHOR]

Abstract: Governments compile their agricultural statistics in tabular form by administrative area, which gives no clue to the exact locations where specific crops are actually grown. Such data are poorly suited for early warning and assessment of crop production. 10-Daily satellite image time series of Andalucia, Spain, acquired since 1998 by the SPOT Vegetation Instrument in combination with reported crop area statistics were used to produce the required crop maps. Firstly, the 10-daily (1998–2006) 1-km resolution SPOT-Vegetation NDVI-images were used to stratify the study area in 45 map units through an iterative unsupervised classification process. Each unit represents an NDVI-profile showing changes in vegetation greenness over time which is assumed to relate to the types of land cover and land use present. Secondly, the areas of NDVI-units and the reported cropped areas by municipality were used to disaggregate the crop statistics. Adjusted R-squares were 98.8% for rainfed wheat, 97.5% for rainfed sunflower, and 76.5% for barley. Relating statistical data on areas cropped by municipality with the NDVI-based unit map showed that the selected crops were significantly related to specific NDVI-based map units. Other NDVI-profiles did not relate to the studied crops and represented other types of land use or land cover. The results were validated by using primary field data. These data were collected by the Spanish government from 2001 to 2005 through grid sampling within agricultural areas; each grid (block) contains three 700m×700m segments. The validation showed 68%, 31% and 23% variability explained (adjusted R-squares) between the three produced maps and the thousands of segment data. Mainly variability within the delineated NDVI-units caused relatively low values; the units are internally heterogeneous. Variability between units is properly captured. The maps must accordingly be considered “small scale maps”. These maps can be used to monitor crop performance of specific cropped areas because of using hypertemporal images. Early warning thus becomes more location and crop specific because of using hypertemporal remote sensing. [Copyright &y& Elsevier]

One of the reported changes of arctic ecosystems in response to warming climate is the advance of the leaf appearance in spring. Such phenological changes play a role in the structural changes within tundra ecosystem communities. Recently, we developed a model that estimates the leaf appearance date for deciduous trees in taiga. We apply this model to the whole low-arctic tundra, and we compare the simulated green-up dates with the green-up dates obtained from satellite observations and to in situ measurements of deciduous shrub leaf appearance. The model, although calibrated for taiga, performs remarkably well in tundra, with root mean square error ranging between 4 and 8 days for most of the tundra region, the same order as in taiga regions. The results seem to indicate that air temperature is the main factor controlling spring leaf phenology in tundra, just as in taiga, although these results do not permit us to reject soil temperature as the main trigger for leaf appearance in tundra. Because our model performs in tundra as well as in taiga, it can be used across the ecotone, and during a northward migration of the species from the taiga to the low-arctic region. The leaf appearance model and the satellite observations reveal that leaf appearance has tended to occur earlier by approximately 10 days both in Alaska since 1975, and in west Siberian tundra since 1965. [ABSTRACT FROM AUTHOR]

Abstract: In the Mediterranean regions, fires are considered a major cause of land degradation. Every year, around 45,000 forest fires break out in the Mediterranean basin causing the destruction of about 2.6 million hectares (). In Italy, as in other countries of the Mediterranean Basin, a small number of fires generally destroy a large percentage of the total burned areas every year. In these cases, the use of coarse resolution satellite sensors appears to be very useful for the discrimination of burned areas. In this study, SPOT-Vegetation (SPOT-VGT) data at full spatial resolution were analysed in order to investigate the spectral features of burned areas observed in the Mediterranean ecosystems in the Calabria Region during the 1998 fire season. Among the total fire events occurred in the considered period wildland fires larger than 1000ha were selected for this study. SPOT-VGT imagery acquired before and after fire events were considered. Single channels or spectral indices suitable/or specifically designed for burned areas mapping were analysed. In particular near-infrared (NIR), short-wave infrared reflectance (SWIR), albedo, normalized difference of vegetation index (NDVI), normalized difference of infrared index (NDII), burned area index (BAI), global environmental monitoring index (GEMI) and soil adjusted vegetation index (SAVI), were considered in this study. The changes observed before and after fire occurrence in the considered parameters were presented and discussed. Results showed that among the spectral indices considered in this work, the highest discrimination capability was generally observed for NDII, SAVI, GEMI, BAI and NIR, nevertheless, strongly differences were observed from one fire event to another, and this fact suggests that the discrimination capability must be analysed coupled with the specific land covers affected by fire. [Copyright &y& Elsevier]

Abstract: Measurements of spring phenological dates in boreal regions using NDVI can be affected by snowmelt. This impacts the analysis of interannual variations in phenology and the estimates of annual carbon fluxes. For these two objectives, snowmelt effect must be removed from the phenological detection. We propose a methodology for determining the date of onset of greening in the 1982–2004 period using SPOT-VEGETATION (VGT) and NOAA Advanced Very High Resolution Radiometer (AVHRR) data. From 1998 onwards, the date of onset of greening is taken as the date at which the Normalized Difference Water Index (NDWI), calculated from SPOT-VGT near and short-wave infrared bands, starts increasing. This index decreases with snowmelt but increases with vegetation greening. For the 1982–2001 period, the date of onset of greening is the date at which AVHRR-NDVI equals a pixel specific threshold (PST), determined using the results of the NDWI method in the years common to the two datasets. The methods are validated using in situ measurements of the dates of leaf appearance. RMSE of 6.7 and 7.8 days, respectively, is found using NDWI-VGT and PST-NOAA methodologies, and the difference between the two methodologies in the common years is small. Very importantly, the dates are not biased. The interannual variations of the 23-year spring phenology dataset on the study area in northern Eurasia are analysed. In average over the study area, an advance of 8 days and a delay of 3.6 days are, respectively, found over the periods 1982–1991 and 1993–2004. These results confirm and complete previous studies about the greening trend, remove the uncertainty due to snow, and may improve carbon budget calculations. [Copyright &y& Elsevier]

Abstract: An algorithm for burned area mapping in Africa based on classification trees was developed using SPOT-VEGETATION (VGT) imagery. The derived 1 km spatial resolution burned area maps were compared with 30 m spatial resolution maps obtained with 13 Landsat ETM+ scenes, through linear regression analysis. The procedure quantifies the bias in burned area estimation present in the low spatial resolution burned area map. Good correspondence was observed for seven sites, with values of the coefficient of determination (R 2) ranging from 0.787 to 0.983. Poorer agreement was observed in four sites (R 2 values between 0.257 and 0.417), and intermediate values of R 2 (0.670 and 0.613) were obtained for two sites. The observed variation in the level of agreement between the Landsat and VGT estimates of area burned results from differences in the spatial pattern and size distribution of burns in the different fire regimes encompassed by our analysis. Small and fragmented burned areas result in large underestimation at 1 km spatial resolution. When large and compact burned areas dominate the landscape, VGT estimates of burned area are accurate, although in certain situations there is some overestimation. Accuracy of VGT burned area estimates also depends on vegetation type. Results showed that in forest ecosystems VGT maps underestimate substantially the amount of burned area. The most accurate estimates were obtained for woodlands and grasslands. An overall linear regression fitted with the data from the 13 comparison sites revealed that there is a strong relationship between VGT and Landsat estimates of burned area, with a value of R 2 of 0.754 and a slope of 0.803. Our findings indicate that burned area mapping based on 1 km spatial resolution VGT data provides adequate regional information. [Copyright &y& Elsevier]

High-quality retrieval of land surface phenology (LSP) is increasingly important for understanding the effects of climate change on ecosystem function and biosphere–atmosphere interactions. We analyzed four state-of-the-art phenology methods: threshold, logistic-function, moving-average and first derivative based approaches, and retrieved LSP in the North Hemisphere for the period 1999–2017 from Copernicus Global Land Service (CGLS) SPOT-VEGETATION and PROBA-V leaf area index (LAI) 1 km V2.0 time series. We validated the LSP estimates with near-surface PhenoCam and eddy covariance FLUXNET data over 80 sites of deciduous forests. Results showed a strong correlation (R2 > 0.7) between the satellite LSP and ground-based observations from both PhenoCam and FLUXNET for the timing of the start (SoS) and R2 > 0.5 for the end of season (EoS). The threshold-based method performed the best with a root mean square error of ~9 d with PhenoCam and ~7 d with FLUXNET for the timing of SoS (30th percentile of the annual amplitude), and ~12 d and ~10 d, respectively, for the timing of EoS (40th percentile). [ABSTRACT FROM AUTHOR]

• Leaf area index calculated from remote sensing data is optimal to estimate phenology. • The estimates of satellite metrics are strongly correlated with the ground-based data. • The threshold method, using the 30th percentile, performed the best for SoS. • The threshold method, using the 40th percentile, performed the best for EoS. • Phenological metrics for the timing of SoS and EoS show a strong latitudinal gradient. Land surface phenology has been widely retrieved although no consensus exists on the optimal satellite dataset and the method to extract phenology metrics. This study is the first comprehensive comparison of vegetation variables and methods to retrieve land surface phenology for 1999–2017 time series of Copernicus Global Land products derived from SPOT-VEGETATION and PROBA-V data. We investigated the sensitivity of phenology to (I) the input vegetation variable: normalized difference vegetation index (NDVI), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fraction of vegetation cover (FCOVER); (II) the smoothing and gap filling method for deriving seasonal trajectories; and (III) the method to extract phenological metrics: thresholds based on a percentile of the annual amplitude of the vegetation variable, autoregressive moving averages, logistic function fitting, and first derivative methods. We validated the derived satellite phenological metrics (start of the season (SoS) and end of the season (EoS)) using available ground observations of Betula pendula , B. alleghaniensis, Acer rubrum , Fagus grandifolia , and Quercus rubra in Europe (Pan-European PEP725 network) and the USA (National Phenology Network, USA-NPN). The threshold-based method applied to the smoothed and gap-filled LAI V2 time series agreed best with the ground phenology, with root mean square errors of ˜10 d and ˜25 d for the timing of SoS and EoS respectively. This research is expected to contribute for the operational retrieval of land surface phenology within the Copernicus Global Land Service. [ABSTRACT FROM AUTHOR]

The sensitivity of the radar signal to the seasonal dynamics in the Sahel region is a considerable asset for monitoring surface parameters including soil moisture. Given the sensitivity of the radar signal to vegetation mass production, roughness and soil moisture, the main problem has been to estimate the contribution of these three parameters to the signal. This study aims to circumvent this problem by combining radar with optical data. The DMP (Dry Mater Product) extracted from SPOT data allowed to estimate vegetation mass production. Surface roughness was estimated from radar data during the dry season. Because during the dry season, radar signal is only conditioned by soil roughness in this region a Radiative Transfer Model (RTM) was used: it consists in a microwave scattering model of layered vegetation based on the first-order solution of the radiative transfer equation and it accounts for multiple scattering within the canopy, surface roughness of the soil, and the interaction between canopy surface and soil. This model was designed to account for the branch size distribution, leaf orientation distribution, and branch orientation distribution for each size. In this study, the RTM has been calibrated with ESCAT (European Radar Satellite Scatterometer) data, and has been used in order to estimate soil moisture. The results obtained have allowed to track the spatial and temporal dynamics of soil moisture on the one hand, and on the other hand the influence of geology and morphopedology on the spatial dynamics of the soil moisture variability. These results are promising despite the fact that the inversed RTM often faces difficulties to interpret the signal for saturated soils, giving an aberrant value of soil moisture more often than not. [ABSTRACT FROM AUTHOR]