Global vegetation variability and its response to elevated CO2, global warming, and climate variability – a study using the offline SSiB4/TRIFFID model and satellite data

Abstract. The climate regime shift during the 1980s had a substantial impact on the terrestrial ecosystems and vegetation at different scales. However, the mechanisms driving vegetation changes, before and after the shift, remain unclear. In this study, we used a biophysical-dynamic vegetation model to estimate large-scale trends in terms of carbon fixation, vegetation growth, and expansion during the period 1958–2007, and to attribute these changes to environmental drivers including elevated atmospheric CO2 concentration (hereafter eCO2), global warming, and climate variability (hereafter CV). Simulated Leaf Area Index (LAI) and Gross Primary Product (GPP) were evaluated against observation-based data. Significant spatial correlations are found (correlations > 0.87), along with regionally varying temporal correlations of 0.34–0.80 for LAI and 0.45–0.83 for GPP. More than 40 % of the global land area shows significant trends in LAI and GPP since the 1950s: 11.7 % and 19.3 % of land has consistently positive LAI and GPP trends, respectively; while 17.1 % and 20.1 % of land, saw LAI and GPP trends respectively, reverse during the 1980s. Vegetation fraction cover (FRAC) trends, representing vegetation expansion/shrinking, are found at the edges of semi-arid areas and polar areas. Overall, eCO2 consistently contributes to positive LAI and GPP trends in the tropics. Global warming is shown to mostly affected LAI, with positive effects in high latitudes and negative effects in subtropical semi-arid areas. CV is found to dominate the variability of FRAC, LAI, and GPP in the semi-humid and semi-arid areas. The eCO2 and global warming effects increased after the 1980s, while the CV effect reversed during the 1980s. In addition, plant competition is shown to have played an important role in determining which driver dominated the regional trends. This paper presents a new insight into ecosystem variability and changes in the varying climate since the 1950s

Revisiting the contribution of transpiration to global terrestrial evapotranspiration

Even though knowing the contributions of transpiration (T), soil and open water evaporation (E), and interception (I) to terrestrial evapotranspiration (ET=T+E+I) is crucial for understanding the hydrological cycle and its connection to ecological processes, the fraction of T is unattainable by traditional measurement techniques over large scales. Previously reported global mean T/(E+T+I) from multiple independent sources, including satellite-based estimations, reanalysis, land surface models, and isotopic measurements, varies substantially from 24% to 90%. Here we develop a new ET partitioning algorithm, which combines global evapotranspiration estimates and relationships between leaf area index (LAI) and T/(E+T) for different vegetation types, to upscale a wide range of published site-scale measurements. We show that transpiration accounts for about 57.2% (with standard deviation6.8%) of global terrestrial ET. Our approach bridges the scale gap between site measurements and global model simulations,and can be simply implemented into current global climate models to improve biological CO2 flux simulations

Examining spatiotemporal changes in the phenology of Australian mangroves using satellite imagery

Nicolás Younes investigated the phenology of Australian mangroves using satellite imagery, field data, and generalized additive models. He found that satellite-derived phenology changes with location, frequency of observation, and spatial resolution. Nicolás challenges the common methods for detecting phenology and proposes a data-driven approach

Advances in Land–Ocean Heat Fluxes Using Remote Sensing

Advanced remote sensing technology has provided spatially distributed variables for estimating land–ocean heat fluxes, allowing for practical applications in drought monitoring, water resources management, and climate assessment. This Special Issue includes several research studies using state-of-the-art algorithms for estimating downward longwave radiation, surface net radiation, latent heat flux, columnar atmospheric water vapor, fractional vegetation cover, and grassland aboveground biomass. This Special Issue intends to help scientists involved in global change research and practices better comprehend the strengths and disadvantages of the application of remote sensing for monitoring surface energy, water, and carbon budgets. The studies published in this Special Issue can be applied by natural resource management communities to enhance the characterization and assessment of land–ocean biophysical variables, as well as for more accurately partitioning heat flux into soil and vegetation based on the existing and forthcoming remote sensing data

Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors

The ability to accurately and rapidly acquire leaf area index (LAI) is an indispensable component of process-based ecological research facilitating the understanding of gas-vegetation exchange phenomenon at an array of spatial scales from the leaf to the landscape. However, LAI is difficult to directly acquire for large spatial extents due to its time consuming and work intensive nature. Such efforts have been significantly improved by the emergence of optical and active remote sensing techniques. This paper reviews the definitions and theories of LAI measurement with respect to direct and indirect methods. Then, the methodologies for LAI retrieval with regard to the characteristics of a range of remotely sensed datasets are discussed. Remote sensing indirect methods are subdivided into two categories of passive and active remote sensing, which are further categorized as terrestrial, aerial and satellite-born platforms. Due to a wide variety in spatial resolution of remotely sensed data and the requirements of ecological modeling, the scaling issue of LAI is discussed and special consideration is given to extrapolation of measurement to landscape and regional levels

Quantifying Carbon and Water Dynamics of Terrestrial Ecosystems At High Temporal And Spatial Resolutions Using Process-Based Biogeochemistry Models And In Situ And Satellite Data

To better understand the role of terrestrial ecosystems in the global carbon cycle and their feedbacks to the global climate system, process-based ecosystem models that are used for quantifying net carbon exchanges between the terrestrial biosphere and the atmosphere need to be improved. My research objective is to improve the model from following aspects: 1) Improving parameterization and model structure for carbon and water dynamics, 2) improving regional model simulations at finer spatial resolutions (from 0.5 degree to 0.05 degree or finer), 3) developing faster spin-up algorithms, and 4) evaluating high performance model simulations using fast spin-up technique deployed on various computing platforms. I improved the leaf area index (LAI) modeling in a terrestrial ecosystem model (TEM) for North America. The evaluated TEM was used to estimate ET at site and regional scales in North America from 2000 to 2010. The estimated annual ET varies from 420 to 450 mm yr-1 with the improved model, close to MODIS monthly data with root-mean-square-error less than 10 mmmonth-1 for the study period. Alaska, Canada, and the conterminous US accounts for 33%, 6% and 61% of the regional ET, respectively. I then used new algorithm for a fast spin-up for TEM. With the new spin-up algorithm, I showed that the model reached a steady state in less than 10 years of simulation time, while the original method requires more than 200 years on average of model run. Lastly, I conducted simulations under both original resolution and high resolution in the conterminous US. The high-resolution simulation predicts slightly higher average annual gross primary production (GPP) (~2%) from 2000 to 2015 in the conterminous US than original version of TEM. From the improved TEM simulation, I estimated that regional GPP is between 7.12 and 7.69 Pg C yr-1 and NEP is between 0.09 and 0.75 Pg C yr-1

Earth Observations for Addressing Global Challenges

"Earth Observations for Addressing Global Challenges" presents the results of cutting-edge research related to innovative techniques and approaches based on satellite remote sensing data, the acquisition of earth observations, and their applications in the contemporary practice of sustainable development. Addressing the urgent tasks of adaptation to climate change is one of the biggest global challenges for humanity. As His Excellency António Guterres, Secretary-General of the United Nations, said, "Climate change is the defining issue of our time—and we are at a defining moment. We face a direct existential threat." For many years, scientists from around the world have been conducting research on earth observations collecting vital data about the state of the earth environment. Evidence of the rapidly changing climate is alarming: according to the World Meteorological Organization, the past two decades included 18 of the warmest years since 1850, when records began. Thus, Group on Earth Observations (GEO) has launched initiatives across multiple societal benefit areas (agriculture, biodiversity, climate, disasters, ecosystems, energy, health, water, and weather), such as the Global Forest Observations Initiative, the GEO Carbon and GHG Initiative, the GEO Biodiversity Observation Network, and the GEO Blue Planet, among others. The results of research that addressed strategic priorities of these important initiatives are presented in the monograph

Assessing crop water requirements and irrigation scheduling at different spatial scales in Mediterranean orchards using models, proximal and remotely sensed data

Accurate estimations of crop water requirements are necessary to improve water use in agriculture and to optimize the use of available freshwater resource. To this aim, the Agro-Hydrological models represent useful tools to quantify the crop actual evapotranspiration. To define the upper boundary condition of the Agro-Hydrological models it is essential to assess the atmospheric water demand, expressed as crop reference evapotranspiration, ETo. In literature several methods, different in terms of input data requirement and climate variables combinations, have been developed to estimate ETo. Among these methods it is commonly used the well-known FAO56 Penman-Monteith (FAO56-PM) thermodynamic approach. Implementing this method requires access to climate data usually measured by ground weather stations. Unfortunately, these instruments are not always available, in this case recent climate reanalysis databases are useful solution to overcome this limitation. Direct measurements of actual evapotranspiration, ETa, are important to validate the results of the model’s application. These measurements, especially for large scale use, can be time consuming and economically expensive. Moreover, improper installation of the sensors or incorrect calibrations could cause outliers in time series or compromise the continuity of the data time series. Recently Machine Learning (ML) algorithm have been developed to predict and fill the gaps in time series of ETa. The joint use of Agro-Hydrological models with proximity and remotely sensed data is one of the possible ways to accurately estimate crop water requirements. The remote observations of the land surface represent a reliable strategy to identify the spatial distribution of vegetation biophysical parameters, such as, crop coefficient Kc under actual field conditions. The general objective of the research was to assess the crop water requirements in two typical crops (citrus and olive) of the Mediterranean region, using FAO56 Agro-Hydrological model based on functional relationships Kc(VIs) between crop coefficient, Kc, and Vegetation Indices (VIs) calibrate using in situ measurements and VIs obtained by multispectral remotely sensed data. Moreover, it was evaluated the reliability of the reanalysis climate variables provided by ERA5-Land database to assess ETo in Sicily (Italy). The performance of the ERA5-Land reanalysis weather data to estimate ETo, was assessed considering 39 ground weather station distributed in Sicily region. The ETo values estimated on the basis of climate variables from ERA5-L database encourage the use of reanalysis database to assess ETo. In general, the results were in agreement with those obtained from ground measurement, with average Root Mean Square Error (RMSE) equal to 0.73 mm d-1 and corresponding Mean Bias Error (MBE) equal to -0.36 mm d-1. The research activities were carried out in two experimental fields. The first experimental field is a citrus orchard located near the Villabate town whereas the second one was the irrigation district 1/A, managed by “Consorzio di Bonifica della Sicilia” ex “Consorzio di Bonifica Agrigento 3”, Castelvetrano, Sicily (Italy), characterized mainly by olives orchards. The time series of ETa, acquired by the Eddy Covariance (EC) tower installed in the citrus experimental field was processed using the Gaussian Process Regression (GPR) algorithm in order to fill the gaps. The performances were evaluated in terms of Nash Sutcliffe Efficiency (NSE) coefficient and RMSE. The values of NSE ranging between 0.74 and 0.88, whereas the RMSE values lower or equal to 0.55 mm d-1 confirm the suitability of the GPR model, to predict time ETa series. FAO56 Agro-Hydrological model was applied for the irrigation seasons 2018, 2019 and 2020 (Villabate) and for the irrigation seasons 2018 and 2019 (Castelvetrano). For each study areas, using VIs obtained from Sentinel-2 Multi Spectral Images (MSI) level 2A, a Kc(VIs) relationship was developed and then implemented in the model. The model was used to estimates spatial and temporal variability of the actual evapotranspiration, soil water content (SWC), in the root zone, crop coefficient and stress coefficient, as well as, to irrigation scheduling. For the citrus orchard a non-linear Kc(VIs) relationship was identified after assuming that the sum of two VIs, such as Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI), is suitable to represent the spatio-temporal dynamics of the investigated environment. The application of the FAO56 Agro-Hydrological model indicated that the estimated ETa was characterized by RMSE, and MBE, of 0.48 and -0.13 mm d−1 respectively, while the estimated SWC, were characterized by RMSE = 0.01 cm3 cm−3 and the absence of bias, then confirming that the suggested procedure can produce highly accurate results in terms of dynamics of SWC and ETa under the investigated field conditions. In the Castelvetrano irrigation district 1/A, a linear Kc(VI) relationship was identified following the Allen and Pereira (A&P) procedure which was based on the height of the canopy and the fraction of vegetation cover, the last was estimated by the NDVI. The differences between simulated and measured seasonal values was encouraging for the 2018, with value equal to 3%, while for the 2019 it was equal to 17%. These results highlight that the proposed model, with further improvements, and more accurate information such as the effective depth of root zone and the real volumes delivered by the hydrants, can be a useful tool for supporting the decision in the management of the irrigation demands in the irrigation district