Long-term time series of global vegetation products: challenges and lessons learnt from AVHRR to Sentinel-3

Verger, Aleixandre; Weiss, Marie; Descals, Adrià; Camacho, Fernando; Sánchez-Zapero, Jorge; Lacaze, Roselyne; Baret, Frédéric

doi:10.5194/egusphere-egu23-16447

Cited by 6 publications

(9 citation statements)

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“…The VC factor, as the ratio of vertically projected vegetation to the full extent of the area to be examined, can be expressed with the use of the fraction of VC (F Cover ), derived from the leaf area index and other canopy structural variables. Daily top of canopy reflectance is converted into estimates and in a second step filtered and smoothed by various inputs to distinguish between bare soil and the presence of vegetation (Verger et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal variability of the potential wind erosion risk in Southern Africa between 2005 and 2019

2023

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Regional assessments of the wind erosion risk are rare and vary due to the methods used and the available data to be included. The adaptation of existing methods has the advantage that the results can be compared directly. We adopted an already successfully applied methodology (ILSWE—applied in East Africa), to investigate the spatiotemporal variability of the wind erosion risk between 2005 and 2019 in Southern Africa. The approach integrates climatic variables, a vegetation index, and soil properties to describe the potential impact of wind erosion at the landscape scale. The annual and seasonal variability is determined by the vegetation cover, whereas droughts and strong El Niño events had only regional effects. We estimated that 8.3% of the study area experiences a moderate to elevated wind erosion risk over the 15‐year period with annual and inter‐annual fluctuations showing a slight upward trend. In general, the desert and drylands in the west have the highest proportion of risk areas, the moist forests in the east are characterized by a very low risk of wind erosion, while the grasslands, shrublands, and croplands in the interior most likely react to changes of climatic conditions. The validation process is based on a comparison with the estimated frequency of dust storms derived from the aerosol optical depth and angstrom exponent and revealed an overall accuracy of 65%. The results of this study identify regions and yearly periods prone to wind erosion to prioritize for further analysis and conservation policies for mitigation and adaptation strategies.

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Section: Methodsmentioning

confidence: 99%

Spatiotemporal variability of the potential wind erosion risk in Southern Africa between 2005 and 2019

2023

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“…For each of the first five modeling experiments in Table 1, we update one aspect of the LSM, that is, land cover, cover fraction, or LAI. We start from a baseline simulation (CONTROL) which is based on the land cover data set from the GLCCv1.2, static cover fraction and a prescribed LAI climatology (through lookup tables) and default model parameters, until we perform the LC_COV_LAI experiment in which we update all aspects including: the land cover data set using information with a 300m spatial resolution from ESA-CCI/C3S (Bontemps et al, 2017), the cover fraction interannual variability and the LAI interannual variability using 10-daily data from Sentinel-3 (Verger et al, 2022) and THEA GEOV2 (Verger et al, 2020) at 1 and 4 km spatial resolution respectively, but with default model parameters. The cover fraction and LAI interannual variability refers to monthly values that vary every year, in contrast to climatological monthly means, based on the monthly mean calculated over the period 1993-2019, that is, we do not use new lookup tables for LAI and cover fraction but we apply observational data directly to each grid cell.…”

Section: Modeling Experimentsmentioning

confidence: 99%

Impact of Updating Vegetation Information on Land Surface Model Performance

Ruiz‐Vásquez,

Arduini

et al. 2023

JGR Atmospheres

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Vegetation plays a fundamental role in modulating the exchange of water, energy, and carbon fluxes between the land and the atmosphere. These exchanges are modeled by Land Surface Models (LSMs), which are an essential part of numerical weather prediction and data assimilation. However, most current LSMs implemented specifically in weather forecasting systems use climatological vegetation indices, and land use/land cover data sets in these models are often outdated. In this study, we update land surface data in the European Centre for Medium‐range Weather Forecast (ECMWF) land surface modeling system (ECLand) using Earth observation‐based time varying leaf area index and land use/land cover data, and evaluate the impact of vegetation dynamics on model performance. The performance of the simulated latent heat flux and soil moisture is then evaluated against global gridded observation‐based data sets. Updating the vegetation information does not always yield better model performances because the model's parameters are adapted to the previously employed land surface information. Therefore we recalibrate key soil and vegetation‐related parameters at individual grid cells to adjust the model parameterizations to the new land surface information. This substantially improves model performance and demonstrates the benefits of updated vegetation information. Interestingly, we find that a regional parameter calibration outperforms a globally uniform adjustment of parameters, indicating that parameters should sufficiently reflect spatial variability in the land surface. Our results highlight that newly available Earth‐observation products of vegetation dynamics and land cover changes can improve land surface model performances, which in turn can contribute to more accurate weather forecasts.

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“…FAPAR provides a direct, bio-physically-based metric of land productivity. As a result, FAPAR plays a key role in primary productivity models (Fuster et al, 2020;Verger et al, 2023;Zhao, Heinsch, Nemani, & Running, 2005), and has been recognized as both an Essential Climate Variable (Zemp et al, 2022); as well as an Essential Biodiversity Indicator (Skidmore et al, 2021).…”

Section: General Workflowmentioning

confidence: 99%

“…However, comparison with GBOV ground reference measurements showed that our model predicts actual FAPAR with relatively high accuracy, despite the fact that we did not use surface reflectance values/indices. Verger et al (2023) have recently produced smoothed and gap-filled time series of global FAPAR at a spatially coarser resolution of 1 km. This data set overlaps a great deal with the work of Ma et al (2022) and could potentially also be used to perform a similar analysis.…”

Section: Limitations Of This Studymentioning

confidence: 99%

Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution

Hackländer,

Parente,

et al. 2023

Preprint

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The paper presents results of using remote sensing time series and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Monthly aggregated FAPAR time series of three percentiles (0.05, 0.50 and 0.95 probability) at 250 m spatial resolution were derived from the 8–day GLASS FAPAR V6 product for 2000–2021 and used to determine long–term trends in FAPAR, as well as to model potential FAPAR in the absence of human pressure. CCa 3 million training points sampled from 12,500 locations across the globe were overlaid with 68 bio–physical variables representing climate, terrain, land form, and vegetation cover, as well as several variables related to human pressure including: population count, cropland intensity, nightlights and a human footprint index. The training points were used in an ensemble machine learning model that stacks three base learners (Extremely Randomized Trees, Gradient Descended Trees and Artificial Neural Network) using a linear regressor as meta-learner. The potential FAPAR was then projected by removing the impact of urbanization and intensive agriculture in the covariate layers. The results of strict cross-validation show that the global distribution of FAPAR can be explained with an R2 of 0.89, with the most important covariates being growing season length, forest cover indicator and annual precipitation. From this model, a global map of potential monthly FAPAR for the recent year (2021) was produced, and used to predict gaps in actual vs. potential FAPAR. The produced global maps of actual vs potential FAPAR and long–term trends were each spatially matched with stable and transitional land cover classes. The assessment showed large negative FAPAR gaps (actual lower than potential) for classes urban, needle-leave deciduous trees, and flooded shrub or herbaceous cover, while strong negative FAPAR trends were found for classes urban, sparse vegetation and rainfed cropland. On the other hand, classes irrigated or post-flooded cropland, tree cover mixed leaf type, and broad-leave deciduous showed largely positive trends. The framework allows land managers to assess potential land degradation from two aspects: as an actual declining trend in observed FAPAR and as a difference between actual and potential vegetation FAPAR.

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Long-term time series of global vegetation products: challenges and lessons learnt from AVHRR to Sentinel-3

Cited by 6 publications

References 0 publications

Spatiotemporal variability of the potential wind erosion risk in Southern Africa between 2005 and 2019

Spatiotemporal variability of the potential wind erosion risk in Southern Africa between 2005 and 2019

Impact of Updating Vegetation Information on Land Surface Model Performance

Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution

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