Caracterizacion de la fenología de la vegetación a escala global mediante series temporales SPOT VEGETATION

Altres ajuts: Programes Copernicus, le Pôle Thématique Surfaces Continentales THEIA, GIOBIO (32-566) i LONGLOVE (32-594).La fenología de la vegetación a escala global se caracterizó a partir de series temporales del índice de área foliar (LAI) SPOT VEGETATION a 1-km de resolución espacial en el periodo 1999-2010. Los patrones espaciales de la fenología estimada a partir de datos de satélite muestran una gran consistencia con la distribución de biomas y factores climáticos. La comparación de la fenología SPOT VEGETATION con medidas in-situ para las fenofases del abedul común (Betula pendula) en Europa muestra un gran acuerdo en el gradiente latitudinal de temperatura con un descenso en la duración de la estación de crecimiento de 5 días por grado de latitudWe characterized the phenology of the vegetation at the global scale from the mean seasonal leaf area index (LAI) estimated from 1-km SPOT VEGETATION time series for 1999-2010. The satellite-derived phenology was spatially consistent with the global distributions of climatic drivers and biome land cover. The rate of change of phenological leaf development from VEGETATION data and in-situ observations for the date of phenophases of European birch forests agreed very well with latitudinal temperature with a decrease in the length of season of approximately five days per degree of latitude

Human population growth offsets climate-driven increase in woody vegetation in sub-Saharan Africa

The rapidly growing human population in sub-Saharan Africa generates increasing demand for agricultural land and forest products which presumably leads to deforestation. Conversely, a greening of African drylands has been reported, but this has been difficult to associate with changes in woody vegetation. There is thus an incomplete understanding of how woody vegetation responds to socio-economic and environmental change. Here we used a passive microwave Earth Observation data set to document two different trends in woody cover land area for 1992-2011: an 36% increase (6,870,000 km²), largely in drylands, and an 11% decrease (2,150,000 km²), mostly in humid zones. Increases in woody cover were associated with low population growth and driven by increases in CO2 in the humid zones and by increases in precipitation in drylands, whereas decreases in woody cover were associated with high population growth. The spatially distinct pattern of these opposing trends reflects (1) the natural response of vegetation to precipitation and atmospheric CO2 and (2) deforestation in humid areas, minor in size but important for ecosystem services, such as biodiversity and carbon stocks. This nuanced picture of changes in woody cover challenges widely held views of a general and ongoing reduction of the woody vegetation in Africa

A threshold method for robust and fast estimation of land-surface phenology using Google Earth Engine

Cloud-based platforms are changing the way of analyzing remotely sensed data by providing high computational power and rapid access to massive volumes of data. Several types of studies use cloud-based platforms for global-scale analyses, but the number of land-surface phenology (LSP) studies that use cloud-based platforms is low. We analyzed the performance of the state-of-the-art LSP algorithms and propose a new threshold-based method that we implemented in Google Earth Engine (GEE). This new LSP method, called maximum separation (MS) method, applies a moving window that estimates the ratio of observations that exceed a given threshold before and after the central day. The start and end of the growing season are the days of the year when the difference between the ratios before and after the central day are minimal and maximal. The MODIS phenology metrics estimated with the MS method showed similar performances as traditional threshold methods when compared with ground estimations derived from the PhenoCam dataset, a network of digital cameras that provides near-surface remotely sensed observations of vegetation phenology. The main advantage of the MS method is that it can be directly applied to daily nonsmoothed time series without any additional preprocessing steps. The implementation of the proposed method in GEE allowed the processing of global phenological maps derived from MODIS. The distribution of code in GEE allows the reproducibility of results and the rapid processing of LSP metrics by the scientific community

Improved estimates of arctic land surface phenology using Sentinel-2 time series

The high spatial resolution and revisit time of Sentinel-2A/B tandem satellites allow a potentially improved retrieval of land surface phenology (LSP). The biome and regional characteristics, however, greatly constrain the design of the LSP algorithms. In the Arctic, such biome-specific characteristics include prolonged periods of snow cover, persistent cloud cover, and shortness of the growing season. Here, we evaluate the feasibility of Sentinel-2 for deriving high-resolution LSP maps of the Arctic. We extracted the timing of the start and end of season (SoS and EoS, respectively) for the years 2019 and 2020 with a simple implementation of the threshold method in Google Earth Engine (GEE). We found a high level of similarity between Sentinel-2 and PhenoCam metrics; the best results were observed with Sentinel-2 enhanced vegetation index (EVI) (root mean squared error (RMSE) and mean error (ME) of 3.0 d and −0.3 d for the SoS, and 6.5 d and −3.8 d for the EoS, respectively), although other vegetation indices presented similar performances. The phenological maps of Sentinel-2 EVI compared well with the same maps extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) in homogeneous landscapes (RMSE and ME of 9.2 d and 2.9 d for the SoS, and 6.4 and −0.9 d for the EoS, respectively). Unreliable LSP estimates were filtered and a quality flag indicator was activated when the Sentinel-2 time series presented a long period (>40 d) of missing data; discontinuities were lower in spring and early summer (9.2%) than in late summer and autumn (39.4%). The Sentinel-2 high-resolution LSP maps and the GEE phenological extraction method will support vegetation monitoring and contribute to improving the representation of Artic vegetation phenology in land surface models

Land surface phenology from VEGETATION and PROBA-V data. Assessment over deciduous forests

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 Servic

Local interpretation of machine learning models in remote sensing with SHAP : the case of global climate constraints on photosynthesis phenology

Altres ajuts: the Fundación Ramón Areces grant CIVP20A6621Data-driven models using machine learning have been widely used in remote-sensing applications such as the retrieval of biophysical variables and land cover classification. However, these models behave as a 'black box', meaning that the relationships between the input and predicted variables are hard to interpret. Recent regression models that downscale sun-induced fluorescence (SIF) with MODIS and weather variables are an example. The impact of weather variables on the predicted SIF in these models is unknown. The explanation of such weather-SIF relationships would aid in the understanding of climate-related constraints on photosynthesis phenology since SIF is a proxy of gross primary productivity. Here, we used SHapley Additive exPlanations (SHAP) - a novel technique based on game theory - for explaining the contribution of input variables to the individual predictions in a machine learning model. We explored the capabilities of this technique with a weather-SIF model. The regression model predicted ESA-TROPOSIF measurements from ERA5-Land air temperature, shortwave radiation, and vapour-pressure-deficit (VPD) data. The SHAP values of the model were estimated at the start and end of the growing season for the entire globe. These values depicted the global constraints of the three climate variables on the photosynthetically active season and confirmed existing knowledge on the limiting factors of terrestrial photosynthesis with unprecedented spatial detail. Radiation was the limiting factor in tropical rainforest and VPD constrained the start and end of the growing season in tropical dryland ecosystems. In extra-tropical regions, temperature was the main limiting factor during the start of the growing season, but both temperature and radiation constrained photosynthesis at the end of the growing season. This technique may help future remote sensing studies that require the use of non-interpretable machine-learning regression models and explain how input variables contribute to the model prediction in a spatiotemporally explicit manner

Divergent estimates of forest photosynthetic phenology using structural and physiological vegetation indices

The accurate estimation of photosynthetic phenology using vegetation indices (VIs) is important for measuring the interannual variation of atmospheric CO2 concentrations, but the relative performances of structural and physiological VIs remain unclear. We found that structural VIs (normalized difference VI, enhanced VI, and near-infrared reflectance of vegetation) were suitable for estimating the start of the photosynthetically active season in deciduous broadleaf forests using gross primary production measured by FLUXNET as a benchmark, and a physiological VI (chlorophyll/carotenoid index) was better at identifying the end of the photosynthetically active season for deciduous broadleaf forests and both the start and end of season for evergreen needleleaf forests. The divergent performances were rooted in the combined control of structural and physiological regulations of carbon uptake by plants. Most existing studies of photosynthetic phenology have been based on structural VIs, so we suggest revisiting the dynamics of photosynthetic phenology using physiological VIs, which has significant implications on global plant phenology and carbon uptake studies

Evaluation and normalization of topographic effects on vegetation indices

The normalization of topographic effects on vegetation indices (VIs) is a prerequisite for their proper use in mountainous areas. We assessed the topographic effects on the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the soil adjusted vegetation index (SAVI), and the near-infrared reflectance of terrestrial vegetation (NIRv) calculated from Sentinel-2. The evaluation was based on two criteria: the correlation with local illumination condition and the dependence on aspect. Results show that topographic effects can be neglected for the NDVI, while they heavily influence the SAVI, EVI, and NIRv: the local illumination condition explains 19.85%, 25.37%, and 26.69% of the variation of the SAVI, EVI, and NIRv, respectively, and the coefficients of variation across different aspects are, respectively, 8.13%, 10.46%, and 14.07%. We demonstrated the applicability of existing correction methods, including statistical-empirical (SE), sun-canopy-sensor with C-correction (SCS + C), and path length correction (PLC), dedicatedly designed for reflectance, to normalize topographic effects on VIs. Our study will benefit vegetation monitoring with VIs over mountainous areas

A broadband green-red vegetation index for monitoring gross primary production phenology

The chlorophyll/carotenoid index (CCI) is increasingly used for remotely tracking the phenology of photosynthesis. However, CCI is restricted to few satellites incorporating the 531nm band. This study reveals that the Moderate Resolution Imaging Spectroradiometer (MODIS) broadband green reflectance (band 4) is significantly correlated with this xanthophyll-sensitive narrowband (band 11) (R2 = 0:98, p < 0:001), and consequently, the broadband green-red vegetation index GRVI-computed with MODIS band 1 and band 4-is significantly correlated with CCI-computed with MODIS band 1 and band 11 (R2 = 0:97, p < 0:001). GRVI and CCI performed similarly in extracting phenological metrics of the dates of the start and end of the season (EOS) when evaluated with gross primary production (GPP) measurements from eddy covariance towers. For EOS extraction of evergreen needleleaf forest, GRVI even overperformed solar-induced chlorophyll fluorescence which is seen as a direct proxy of plant photosynthesis. This study opens the door for GPP and photosynthetic phenology monitoring from a wide set of sensors with broadbands in the green and red spectral regions

Spatiotemporally representative and cost-efficient sampling design for validation activities in wanglang experimental site

Altres ajuts: EC Copernicus Global Land Service (CGLOPS-1, 199494-JRC.Spatiotemporally representative Elementary Sampling Units (ESUs) are required for capturing the temporal variations in surface spatial heterogeneity through field measurements. Since inaccessibility often coexists with heterogeneity, a cost-efficient sampling design is mandatory. We proposed a sampling strategy to generate spatiotemporally representative and cost-efficient ESUs based on the conditioned Latin hypercube sampling scheme. The proposed strategy was constrained by multi-temporal Normalized Difference Vegetation Index (NDVI) imagery, and the ESUs were limited within a sampling feasible region established based on accessibility criteria. A novel criterion based on the Overlapping Area (OA) between the NDVI frequency distribution histogram from the sampled ESUs and that from the entire study area was used to assess the sampling efficiency. A case study inWanglang National Nature Reserve in China showed that the proposed strategy improves the spatiotemporally representativeness of sampling (mean annual OA = 74.7%) compared to the single-temporally constrained (OA = 68.7%) and the random sampling (OA = 63.1%) strategies. The introduction of the feasible region constraint significantly reduces in-situ labour-intensive characterization necessities at expenses of about 9% loss in the spatiotemporal representativeness of the sampling. Our study will support the validation activities in Wanglang experimental site providing a benchmark for locating the nodes of automatic observation systems (e.g., LAINet) which need a spatially distributed and temporally fixed sampling design