Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km² resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e., offset) between in-situ soil temperature measurements, based on time series from over 1200 1-km² pixels (summarized from 8500 unique temperature sensors) across all the world’s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in-situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Global maps of soil temperature.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Assimilation of satellite observations for the estimation of savanna gross primary production

Monitoring vegetation conditions is a critical activity for assessing food security in Africa. Rural populations relying on rain-fed agriculture and livestock grazing are highly exposed to large seasonal and inter-annual fluctuations in water availability [1]. Timely monitoring of the state, evolution, and productivity of vegetation (crops and pastures in particular) is important to conduct food emergency responses and plan for a long-term, resilient, development strategy [2]. In the last decades, a number of process-based and crop growth models has be used to simulate carbon and water fluxes, vegetation productivity and crop yield. Complex deterministic models are often constrained by the large number of parameters and input data needed, resulting in large uncertainties when applied over large areas where reliable parameterization is not available [3]. An alternative approach is represented by those simpler models that are able to capture the main biological processes and use a reduced and empirical parametrization that can be fine-tuned using remote sensing observations (e.g. [4-5]). In this contribution we explore the performances in tracking the gross primary production (GPP) in a semi-arid environment of a simple model assimilating remote sensing observations. Preliminary results of GPP modelling are presented for three years (2007-2009) over a sparse savanna ecosystem in the Sudan.JRC.H.4-Monitoring Agricultural Resource

Modelling Daily Gross Primary Productivity with Sentinel-2 Data in the Nordic Region-Comparison with Data from MODIS

The high-resolution Sentinel-2 data potentially enable the estimation of gross primary productivity (GPP) at finer spatial resolution by better capturing the spatial variation in a heterogeneous landscapes. This study investigates the potential of 10 m resolution reflectance from the Sentinel-2 Multispectral Instrument to improve the accuracy of GPP estimation across Nordic vegetation types, compared with the 250 m and 500 m resolution reflectance from the Moderate Resolution Imaging Spectroradiometer (MODIS). We applied linear regression models with inputs of two-band enhanced vegetation index (EVI2) derived from Sentinel-2 and MODIS reflectance, respectively, together with various environmental drivers to estimate daily GPP at eight Nordic eddy covariance (EC) flux tower sites. Compared with the GPP from EC measurements, the accuracies of modelled GPP were generally high (R-2 = 0.84 for Sentinel-2; R-2 = 0.83 for MODIS), and the differences between Sentinel-2 and MODIS were minimal. This demonstrates the general consistency in GPP estimates based on the two satellite sensor systems at the Nordic regional scale. On the other hand, the model accuracy did not improve by using the higher spatial-resolution Sentinel-2 data. More analyses of different model formulations, more tests of remotely sensed indices and biophysical parameters, and analyses across a wider range of geographical locations and times will be required to achieve improved GPP estimations from Sentinel-2 satellite data

Modelling spatial and temporal dynamics of GPP in the Sahel from earth observation based photosynthetic capacity and quantum efficiency

International audienceIt has been shown that vegetation growth in semi-arid regions is an important sink for human induced fossil fuel emissions of CO2, which indicates the strong need for improved understanding, and spatially explicit estimates of CO2 uptake (gross primary productivity (GPP)) in semi-arid ecosystems. This study has three aims: 1) to evaluate the MOD17A2H GPP (collection 6) product against eddy covariance (EC) based GPP for six sites across the Sahel. 2) To find evidence on the relationships between spatial and temporal variability in EC based photosynthetic capacity (Fopt) and quantum efficiency (α) and earth observation (EO) based vegetation indices 3) To study the applicability of EO up-scaled Fopt and α for GPP modelling purposes. MOD17A2H GPP (collection 6) underestimated GPP strongly, most likely because the maximum light use efficiency is set too low for semi-arid ecosystems in the MODIS algorithm. The intra-annual dynamics in Fopt was closely related to the shortwave infrared water stress index (SIWSI) closely coupled to equivalent water thickness, whereas α was closely related to the renormalized difference vegetation index (RDVI) affected by chlorophyll abundance. Spatial and inter-annual dynamics in Fopt and α were closely coupled to the normalized difference vegetation index (NDVI) and RDVI, respectively. Modelled GPP based on Fopt and α up-scaled using EO based indices reproduced in situ GPP well for all but a cropped site. The cropped site was strongly impacted by intensive anthropogenic land use. This study indicates the strong applicability of EO as a tool for parameterising spatially explicit estimates of photosynthetic capacity and efficiency; incorporating this into dynamic global vegetation models could improve global estimations of vegetation productivity, ecosystem processes and biochemical and hydrological cycles

Recent divergence in the contributions of tropical and boreal forests to the terrestrial carbon sink

International audienceAnthropogenic land use and land cover changes (LULCC) have a large impact on the global terrestrial carbon sink, but this effect is not well characterized according to biogeographical region. Here, using state-of-the-art Earth observation data and a dynamic global vegetation model, we estimate the impact of LULCC on the contribution of biomes to the terrestrial carbon sink between 1992 and 2015. Tropical and boreal forests contributed equally, and with the largest share of the mean global terrestrial carbon sink. CO2 fertilization was found to be the main driver increasing the terrestrial carbon sink from 1992 to 2015, but the net effect of all drivers (CO2 fertilization and nitrogen deposition, LULCC and meteorological forcing) caused a reduction and an increase, respectively, in the terrestrial carbon sink for tropical and boreal forests. These diverging trends were not observed when applying a conventional LULCC dataset, but were also evident in satellite passive microwave estimates of aboveground biomass. These datasets thereby converge on the conclusion that LULCC have had a greater impact on tropical forests than previously estimated, causing an increase and decrease of the contributions of boreal and tropical forests, respectively, to the growing terrestrial carbon sink. Combining Earth observation data and dynamic global vegetation models, the authors show that anthropogenic land use and land cover change has caused a reduction in the contribution to the terrestrial carbon sink for tropical forests but an increase for boreal forests between 1992 and 2015

A Surface Temperature Initiated Closure (STIC) for surface energy balance fluxes

The use of Penman–Monteith (PM) equation in thermal remote sensing based surface energy balance modeling is not prevalent due to the unavailability of any direct method to integrate thermal data into the PM equation and due to the lack of physical models expressing the surface (or stomatal) and boundary layer conductances (gS and gB) as a function of surface temperature. Here we demonstrate a new method that physically integrates the radiometric surface temperature (TS) into the PM equation for estimating the terrestrial surface energy balance fluxes (sensible heat, H and latent heat, λE). The method combines satellite TS data with standard energy balance closure models in order to derive a hybrid closure that does not require the specification of surface to atmosphere conductance terms. We call this the Surface Temperature Initiated Closure (STIC), which is formed by the simultaneous solution of four state equations. Taking advantage of the psychrometric relationship between temperature and vapor pressure, the present method also estimates the near surface moisture availability (M) from TS, air temperature (TA) and relative humidity (RH), thereby being capable of decomposing λE into evaporation (λEE) and transpiration (λET). STIC is driven with TS, TA, RH, net radiation (RN), and ground heat flux (G). TS measurements from both MODIS Terra (MOD11A2) and Aqua (MYD11A2) were used in conjunction with FLUXNET RN, G, TA, RH, λE and H measurements corresponding to the MODIS equatorial crossing time. The performance of STIC has been evaluated in comparison to the eddy covariance measurements of λE and H at 30 sites that cover a broad range of biomes and climates. We found a RMSE of 37.79 (11%) (with MODIS Terra TS) and 44.27 W m− 2 (15%) (with MODIS Aqua TS) in λE estimates, while the RMSE was 37.74 (9%) (with Terra) and 44.72 W m− 2 (8%) (with Aqua) in H. STIC could efficiently capture the λE dynamics during the dry down period in the semi-arid landscapes where λE is strongly governed by the subsurface soil moisture and where the majority of other λE models generally show poor results. Sensitivity analysis revealed a high sensitivity of both the fluxes to the uncertainties in TS. A realistic response and modest relationship was also found when partitioned λE components (λEE and λET) were compared to the observed soil moisture and rainfall. This is the first study to report the physical integration of TS into the PM equation and finding analytical solution of the physical (gB) and physiological conductances (gS). The performance of STIC over diverse biomes and climates points to its potential to benefit future NASA and NOAA missions having thermal sensors, such as HyspIRI, GeoSTAR and GOES-R for mapping multi-scale λE and drought

Biophysical controls on evapotranspiration and water use efficiency in natural, semi-natural and managed African ecosystems

The effects of climatic factors and vegetation type on evapotranspiration (E) and water use efficiency (WUE) were analyzed using tower-based eddy-covariance (EC) data of eleven African sites (22 site years) located across a continental-scale transect. The seasonal pattern of E was closely linked to growing-season length and rainfall distribution. Although annual precipitation (P) was highly variable among sites (290 to 1650 mm), minimum annual E was not less than 250 mm and reached a maximum of 900 mm where annual P exceeded 1200 mm. Site-specific interannual variability in E could be explained by either changes in total P or variations in solar irradiance. At some sites, a highly positive linear correlation was found between monthly sums of E and net radiation (Rn), whereas a hysteretic relationship at other sites indicated that E lagged behind the typical seasonal progression of Rn. Results of a cross-correlation analysis between daily (24-h) E and Rn revealed that site-specific lag times were between 0 days and up to a few weeks depending on the lag of vapor pressure deficit (D) behind Rn and vegetation type. Physiological parameters (e.g. mean dry-foliage Priestley-Taylor alpha) implied that stomatal limitation to transpiration prevailed. During the rainy season, a strong linear correlation between monthly mean values of gross primary production (GPP) and E resulted in water use efficiency being constant with lower values at grass-dominated sites (~2 to ~3.5 g C kg-1 H2O) than at natural woodland sites and plantations (~4.5 to ~6 g C kg-1 H2O)

Coupling of ecosystem-scale plant water storage and leaf phenology observed by satellite

International audiencePlant water storage is fundamental to the functioning of terrestrial ecosystems by participating in plant metabolism, nutrient and sugar transport, and maintenance of the integrity of the hydraulic system of the plant. However, a global view of the size and dynamics of the water pools stored in plant tissues is still lacking. Here, we report global patterns of seasonal variations in ecosystem-scale plant water storage and their relationship with leaf phenology, based on space-borne measurements of L-band vegetation optical depth. We find that seasonal variations in plant water storage are highly synchronous with leaf phenology for the boreal and temperate forests, but asynchronous for the tropical woodlands, where the seasonal development of plant water storage lags behind leaf area by up to 180 days. Contrasting patterns of the time lag between plant water storage and terrestrial groundwater storage are also evident in these ecosystems. A comparison of the water cycle components in seasonally dry tropical woodlands highlights the buffering effect of plant water storage on the seasonal dynamics of water supply and demand. Our results offer insights into ecosystem-scale plant water relations globally and provide a basis for an improved parameterization of eco-hydrological and Earth system models