Land use and land cover changes have altered terrestrial ecosystem carbon storage, but their impacts on ecosystem sensitivity to drought and temperature fluctuations have not been evaluated spatially over the globe. We estimate drought and temperature sensitivities of ecosystems using vegetation greenness from satellite observations and vegetation biomass from dynamic global vegetation model (DGVM) simulations. Using a space-for-time substitution with satellite data, we first illustrate the effects of vegetation cover changes on drought and temperature sensitivity and compare them with the effects estimated from DGVMs. We also compare simulations forced by scenarios with and without land cover changes to estimate the historical land cover change effects. Satellite data and vegetation models both show that converting forests to grasslands results in a more negative or decreased positive sensitivity of vegetation greenness or biomass to drought. Significant variability exists among models for other types of land cover transitions. We identify substantial effects of historical land cover changes on drought sensitivity from model simulations with a generally positive direction globally. Deforestation can lead to either an increased negative sensitivity, as drought-tolerant forests are replaced by grasslands or croplands, or a decreased negative sensitivity since forests under current land cover are predicted to exhibit greater drought resistance compared to those under pre-industrial land cover. Overall, our findings emphasize the critical role of forests in maintaining ecosystem stability and resistance to drought and temperature fluctuations, thereby implying their importance in stabilizing the carbon stock under increasingly extreme climate conditions.

We present a comparison of atmospheric transport model simulations for carbonyl sulfide (COS), within the framework of the ongoing atmospheric tracer transport model intercomparison project “TransCom”. Seven atmospheric transport models participated in the inter-comparison experiment and provided simulations of COS mixing ratios in the troposphere over a 9-year period (2010–2018), using prescribed state-of-the-art surface fluxes for various components of the atmospheric COS budget: biospheric sink, oceanic source, sources from fire and industry. Since the biosphere is the largest sink of COS, we tested sink estimates produced by two different biosphere models. The main goals of TransCom-COS are (a) to investigate the impact of the transport uncertainty and emission distribution in simulating the spatio-temporal variability of COS mixing ratios in the troposphere, and (b) to assess the sensitivity of simulated tropospheric COS mixing ratios to the seasonal and diurnal variability of the COS biosphere fluxes. To this end, a control case with state-of-the-art seasonal fluxes of COS was constructed. Models were run with the same fluxes and without chemistry to isolate transport differences. Further, two COS flux scenarios were compared: one using a biosphere flux with a monthly time resolution and the other using a biosphere flux with a three-hourly time resolution. In addition, we investigated the sensitivity of the simulated concentrations to different biosphere fluxes and to indirect oceanic emissions through dimethylsulfide (DMS) and carbon disulfide (CS2). The modelled COS mixing ratios were assessed against in-situ observations from surface stations and aircraft.

Crop monitoring requires both high spatial resolution data (HSR) for observing within sub-parcel scale, and high temporal resolution (HTR) to monitor vegetation changes during along the crop cycle. However, these simultaneous requirements are difficult to fulfill by the same satellite. In temperate areas such as the Versailles Plain, near Paris, France, HRS data have at best a dozen images exploitable per year, even with Sentinel-2 because of cloud cover, while those with medium spatial resolution (MRS) provide daily images, but at the generally mixed pixel scale. In France, the Land Parcel Identification System (LPIS) is an information system of the crop types declared by farmers, providing reference information about the annual crops cultivated within each agricultural parcel. In this work, the objective was to monitor the phenology of annual crops recorded in the LPIS of 2016, using satellite image time series from HRS Sentinel-2 (10m) and MRS Proba-V (100m) acquired from early to end of 2016 over the Versailles Plain, a small agricultural region (221 km2) cultivated with annual crops. From the two types of time series, the temporal variations of vegetation indices (NDVI / EVI2) of crops were extracted in order to analyze the crop seasonal variations of winter wheat, winter oilseed rape and maize over 2857 parcels with average size of 6.88 ha. The linear method of spatial disaggregation was applied on the MRS data, using fractions of each crop type in the mixed pixels calculated from the 2016-LPIS. The temporal responses from HRS data were compared with those of the MRS sub-pixels. Comparisons between both time series revealed significant correlations for the three studied crops (winter wheat = 0.94, winter oilseed rape = 0.74 and maize = 0.79). By improving the temporal frequency of the monitoring, from13 images for HRS to 25 images for MRS, the disaggregated MRS time series enabled to distinguish the phenological stages of the three studied crops better than the HRS time series. In conclusion, our method of spatial disaggregation can be used to improve the exploitation of satellite data at MRS in seasonal crop monitoring, especially during the transition periods when the spectral indices of crops are likely to change quickly.