Spatial-temporal dynamics of land surface phenology over Africa for the period of 1982–2015

Knowledge of the dynamics of vegetation phenology is essential for the understanding of vegetation-climate interactions. Although the interest in phenology study is growing, vegetation phenology in Africa received far less attention compared to the Northern Hemisphere. Africa straddles the northern and southern hemispheres, and the climate has a clear latitudinal gradient, which facilitates the study of the interaction between phenology and climate. In this study, the latitudinal and longitudinal gradients and temporal trends of start of growing season (SOS), peak of growing season (POS), and end of growing season (EOS) were examined using long-term satellite dataset during 1982–2015. The latitudinal variations in these phenology metrics were larger in the northern than those in the southern Africa, especially from 6°N northwards to 16°N. The latitudinal variations in southern Africa had no clear patterns due to the more complex climate systems. For the longitudinal variation, the temporal trends in POS and EOS exhibited a gradient-decreasing rate in northern Africa. Over the period from 1982 to 2015, the overall trends of the phenology in Africa were ‘later SOS’, ‘later POS’, and ‘later EOS’. The faster rate of delay in EOS than in SOS resulted in a prolonged length of growing season (LOS) with 0.50 days/year on average in northern Africa, while a slower rate of delay in EOS than in SOS resulted in a shorter LOS with −0.12 days/year in southern Africa. The prolonged LOS in northern Africa contributes to the increase in the yearly-averaged Normalized Difference Vegetation Index (NDVI) from 1982 to 2000. Nevertheless, the NDVI appeared to have reached saturation around the 2000s, although the LOS was still extending after 2000s. Overall, the findings of this study provide an overall view of the spatial and temporal patterns of land surface phenology in the African continent, and a necessary component for future studies on the response of phenology to climate.</p

SCOPE model inversion for Sentinel-3 data retrieval

The SCOPE is a coupled radiative transfer and energy balance model used for simulation of vegetation optical properties and temperature at leaf and canopy level over a spectral range from 0.4 to 50 μm. Inversion of the model allows retrieving a number of plant traits: pigments (Cab, Car, Cant), dry matter content (Cdm), water content (Cw), leaf area index (LAI) and others. Subsequent forward simulation can calculate photosynthesis, evapotranspiration (ET) and a fraction of absorbed photosynthetically active radiation (fAPAR) that can be used further for integrated water use efficiency (WUE) and light use efficiency (LUE) calculations, respectively. The higher the accuracy in retrieved parameters is achieved the higher precision in calculated ecosystem functional properties will be. This work aimed to develop a model-based retrieval algorithm from multispectral satellite data. The initial retrieval algorithm used numerical optimization of residuals squared sum and operated over the spectral range from 0.4 to 2.4 μm. First, the algorithm was extended to the thermal domain (up to 50 μm) and validated against open-source spectral measurement datasets (SPECCHIO). As the SCOPE model operates at both leaf and canopy levels, we had to use different cost functions and constraints for each level. Having validated the hyperspectral retrieval algorithm, we tried to make a convolution to the multispectral case of Sentinel-3 satellite sensors: ocean and land colour instrument (OLCI) and sea and land surface temperature radiometer (SLTR). Finally, parameter retrieved with the algorithm from Sentinel-3 images were used for a forward simulation of the SCOPE model and calculation of integrated WUE and LUE at few selected FLUXNET towers. The results of the simulation were validated against data from FLUXNET eddy-covariance towers

Bayesian integration of flux tower data into a process-based simulator for quantifying uncertainty in simulated output

Parameters of a process-based forest growth simulator are difficult or impossible to obtain from field observations. Reliable estimates can be obtained using calibration against observations of output and state variables. In this study, we present a Bayesian framework to calibrate the widely used process-based simulator Biome-BGC against estimates of gross primary production (GPP) data. We used GPP partitioned from flux tower measurements of a net ecosystem exchange over a 55-year-old Douglas fir stand as an example. The uncertainties of both the Biome-BGC parameters and the simulated GPP values were estimated. The calibrated parameters leaf and fine root turnover (LFRT), ratio of fine root carbon to leaf carbon (FRC : LC), ratio of carbon to nitrogen in leaf (C : Nleaf), canopy water interception coefficient (Wint), fraction of leaf nitrogen in RuBisCO (FLNR), and effective soil rooting depth (SD) characterize the photosynthesis and carbon and nitrogen allocation in the forest. The calibration improved the root mean square error and enhanced Nash–Sutcliffe efficiency between simulated and flux tower daily GPP compared to the uncalibrated Biome-BGC. Nevertheless, the seasonal cycle for flux tower GPP was not reproduced exactly and some overestimation in spring and underestimation in summer remained after calibration. We hypothesized that the phenology exhibited a seasonal cycle that was not accurately reproduced by the simulator. We investigated this by calibrating the Biome-BGC to each month's flux tower GPP separately. As expected, the simulated GPP improved, but the calibrated parameter values suggested that the seasonal cycle of state variables in the simulator could be improved. It was concluded that the Bayesian framework for calibration can reveal features of the modelled physical processes and identify aspects of the process simulator that are too rigid

The roles of radiative, structural and physiological information of sun-induced chlorophyll fluorescence in predicting gross primary production of a corn crop at various temporal scales

Extensive research suggests that sun-induced chlorophyll fluorescence (SIF) and gross primary productivity (GPP) have a near-linear relationship, providing a promising avenue for estimating the carbon uptake of ecosystems. However, the factors influencing the relationship are not yet clear. This study examines the roles of SIF's radiative, structural, and physiological information in predicting GPP, based on four years of field observations of a corn canopy at various temporal scales. We quantified SIF's radiative component by measuring the intensity of incident photosynthetically active radiation (iPAR), and separated the structural and physiological components from SIF observations using the fluorescence correction vegetation index (FCVI). Our results show that the R2 values between SIF and GPP, as estimated by linear models, increased from 0.66 at a half-hour resolution to 0.86 at a one-month resolution. In comparison, the product of FCVI and iPAR, representing the non-physiological information of SIF, performed consistently well in predicting GPP with R2&gt;0.84 at various temporal scales, suggesting a limited contribution of the physiological information of SIF for GPP estimation. The results further reveal that SIF's radiative and structural components positively impacted the SIF-GPP linearity, while the physiological component had a negative impact on the linearity for most cases, changing from 0.6 % to -27.5 %. As for the temporal dependency, the controls of the SIF-GPP relationship moved from radiation at diurnal scales to structure at the seasonal scales. The structural contribution changed from 14.8 % at a half-hour resolution to 92.4 % at a one-month resolution, while the radiative contribution decreased from 118.0 % at a half-hour resolution to 11.7 % at a one-month resolution. This study contributes to enhancing our understanding of the physiological information conveyed by SIF and the factors influencing the temporal dependency of the SIF-GPP relationship.</p

Optimal inverse estimation of ecosystem parameters from observations of carbon and energy fluxes

Canopy structural and leaf photosynthesis parameterizations such as maximum carboxylation capacity (V_(cmax)), slope of the Ball–Berry stomatal conductance model (BB_(slope)) and leaf area index (LAI) are crucial for modeling plant physiological processes and canopy radiative transfer. These parameters are large sources of uncertainty in predictions of carbon and water fluxes. In this study, we develop an optimal moving window nonlinear Bayesian inversion framework to use the Soil Canopy Observation Photochemistry and Energy fluxes (SCOPE) model for constraining V_(cmax), BB_(slope) and LAI with observations of coupled carbon and energy fluxes and spectral reflectance from satellites. We adapted SCOPE to follow the biochemical implementation of the Community Land Model and applied the inversion framework for parameter retrievals of plant species that have both the C₃ and C₄ photosynthetic pathways across three ecosystems. We present comparative analysis of parameter retrievals using observations of (i) gross primary productivity (GPP) and latent energy (LE) fluxes and (ii) improvement in results when using flux observations along with reflectance. Our results demonstrate the applicability of the approach in terms of capturing the seasonal variability and posterior error reduction (40 %–90 %) of key ecosystem parameters. The optimized parameters capture the diurnal and seasonal variability in the GPP and LE fluxes well when compared to flux tower observations (0.95>R²>0.79). This study thus demonstrates the feasibility of parameter inversions using SCOPE, which can be easily adapted to incorporate additional data sources such as spectrally resolved reflectance and fluorescence and thermal emissions

Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations

Satellite remote sensing has been widely used in the last decades for agricultural applications, {both for assessing vegetation condition and for subsequent yield prediction.} Existing remote sensing-based methods to estimate gross primary productivity (GPP), which is an important variable to indicate crop photosynthetic function and stress, typically rely on empirical or semi-empirical approaches, which tend to over-simplify photosynthetic mechanisms. In this work, we take advantage of all parallel developments in mechanistic photosynthesis modeling and satellite data availability for advanced monitoring of crop productivity. In particular, we combine process-based modeling with the soil-canopy energy balance radiative transfer model (SCOPE) with Sentinel-2 {and Landsat 8} optical remote sensing data and machine learning methods in order to estimate crop GPP. Our model successfully estimates GPP across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites. This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms

Systematic Orbital Geometry-Dependent Variations in Satellite Solar-Induced Fluorescence (SIF) Retrievals

While solar-induced fluorescence (SIF) shows promise as a remotely-sensed measurement directly related to photosynthesis, interpretation and validation of satellite-based SIF retrievals remains a challenge. SIF is influenced by the fraction of absorbed photosynthetically-active radiation at the canopy level that depends upon illumination geometry as well as the escape of SIF through the canopy that depends upon the viewing geometry. Several approaches to estimate the effects of sun-sensor geometry on satellite-based SIF have been proposed, and some have been implemented, most relying upon satellite reflectance measurements and/or other ancillary data sets. These approaches, designed to ultimately estimate intrinsic or physiological components of SIF related to photosynthesis, have not generally been applied globally to satellite measurements. Here, we examine in detail how SIF and related reflectance-based indices from wide swath polar orbiting satellites in low Earth orbit vary systematically due to the host satellite orbital characteristics. We compare SIF and reflectance-based parameters from the Global Ozone Mapping Experiment 2 (GOME-2) on the MetOp-B platform and from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel 5 Precursor satellite with a focus on high northern latitudes in summer where observations at similar geometries and local times occur. We show that GOME-2 and TROPOMI SIF observations agree nearly to within estimated uncertainties when they are compared at similar observing geometries. We show that the cross-track dependence of SIF normalized by PAR and related reflectance-based indices are highly correlated for dense canopies, but diverge substantially as the vegetation within a field-of-view becomes more sparse. This has implications for approaches that utilize reflectance measurements to help account for SIF geometrical dependences in satellite measurements. To further help interpret the GOME-2 and TROPOMI SIF observations, we simulated cross-track dependences of PAR normalized SIF and reflectance-based indices with the one dimensional Soil-Canopy Observation Photosynthesis and Energy fluxes (SCOPE) canopy radiative transfer model at sun–satellite geometries that occur across the wide swaths of these instruments and examine the geometrical dependencies of the various components (e.g., fraction of absorbed PAR, SIF yield, and escape of SIF from the canopy) of the observed SIF signal. The simulations show that most of the cross-track variations in SIF result from the escape of SIF through the scattering canopy and not the illumination

Understanding the effects of revegetated shrubs on fluxes of energy, water, and gross primary productivity in a desert steppe ecosystem using the STEMMUS-SCOPE model

Revegetation is one of the most effective ways to combat desertification and soil erosion in semiarid and arid regions. However, the impact of the perturbation of revegetation on ecohydrological processes, particularly its effects on the interplay between hydrological processes and vegetation growth under water stress, requires further investigation. This study evaluated the effects of revegetation on the energy, water, and carbon fluxes in a desert steppe in Yanchi County, Ningxia Province, northwest China, by simulating two vegetated scenarios (shrub-grassland ecosystem and grassland ecosystem) using the STEMMUS-SCOPE (Simultaneous Transfer of Energy, Mass and Momentum in Unsaturated Soil-Soil Canopy Observation of Photosynthesis and Energy fluxes) model. The model was validated by field observations from May to September of 2016-2019. The evaluation of revegetation effects relied on comparing simulated fluxes between two vegetated scenarios in 2016 and 2019. In both scenarios, turbulent energy was dominated by latent heat flux, which was stronger in the shrub-grassland ecosystem (+7%). A higher leaf area index and root water uptake of C3 shrubs (Caragana intermedia) resulted in increased carbon fixation (+83%) and transpiration (+72%) of the shrub-grassland ecosystem compared to the C3 grassland ecosystem. Accompanied by a marked increase in root water uptake (+123%), revegetation intensified water consumption beyond the levels of received precipitation. These results highlight the critical importance of considering both energy and water budgets in water-limited ecosystems during ecological restoration to avert soil water depletion.</p

Modelling sun-induced fluorescence and photosynthesis with a land surface model at local and regional scales in northern Europe

Recent satellite observations of sun-induced chlorophyll fluorescence (SIF) are thought to provide a large-scale proxy for gross primary production (GPP), thus providing a new way to assess the performance of land surface models (LSMs). In this study, we assessed how well SIF is able to predict GPP in the Fenno-Scandinavian region and what potential limitations for its application exist. We implemented a SIF model into the JSBACH LSM and used active leaf-level chlorophyll fluorescence measurements (Chl F) to evaluate the performance of the SIF module at a coniferous forest at Hyytiala, Finland. We also compared simulated GPP and SIF at four Finnish micrometeorological flux measurement sites to observed GPP as well as to satellite-observed SIF. Finally, we conducted a regional model simulation for the Fenno-Scandinavian region with JSBACH and compared the results to SIF retrievals from the GOME-2 (Global Ozone Monitoring Experiment-2) space-borne spectrometer and to observation-based regional GPP estimates. Both observations and simulations revealed that SIF can be used to estimate GPP at both site and regional scales. At regional scale the model was able to simulate observed SIF averaged over 5 years with r(2) of 0.86. The GOME-2-based SIF was a better proxy for GPP than the remotely sensed fA-PAR (fraction of absorbed photosynthetic active radiation by vegetation). The observed SIF captured the seasonality of the photosynthesis at site scale and showed feasibility for use in improving of model seasonality at site and regional scale.Peer reviewe