Soil Canopy Observation , Photochemistry And Energy Fluxes Research Articles

Potential of Solar-Induced Chlorophyll Fluorescence for Monitoring Gross Primary Productivity and Evapotranspiration in Tidally-Influenced Coastal Salt Marshes

Solar-induced chlorophyll fluorescence (SIF) offers significant potential as a novel approach for quantifying carbon and water cycling in coastal wetland ecosystems across multiple spatial scales. However, the mechanisms governing these biogeochemical processes remain insufficiently understood, largely due to the periodic influence of tidal inundation. In this study, we investigated the effects and underlying mechanisms of meteorological and tidal factors on the relationships between canopy-level solar-induced chlorophyll fluorescence at 760 nm (SIF760) and key ecosystem processes, including gross primary productivity (GPP) and evapotranspiration (ET), in coastal wetlands. These processes are critical components of the ecosystem carbon and water cycles. Our approach involved a comparative analysis of simulations from the Soil Canopy Observation, Photochemistry and Energy Fluxes (SCOPE) model with field measurements. The results showed that: (1) simulations of SIF760 improved following observation-based calibration of the fluorescence photosynthesis module in the SCOPE model; (2) under optimal moisture and temperature conditions (VPD 1.2–1.4 kPa and temperatures of 20–23 °C for air, soil, and water), the simulations of GPP, ET, and SIF760 were most accurate, although salinity stress reduced performance. GPP simulations tended to overestimate under drought stress but improved at higher air temperatures (30–32 °C); (3) during tidal inundation, the SIF760-GPP relationship weakened while the SIF760-ET strengthened. The range of significant correlations between SIF760, water levels, and temperature narrowed, with both relationships becoming more complex due to salinity stress. These findings suggest that tidal inundation can alleviate temperature stress on photosynthesis and transpiration; however, it also decreases photosynthetic efficiency and alters radiative transfer processes due to elevated salinity and water levels. These factors are critical considerations when using SIF to monitor GPP and ET dynamics in coastal wetlands. This study demonstrated that the tidal dynamics significantly affected the SIF760-GPP and SIF760-ET relationships, underscoring the necessity of incorporating tidal influences in the application of SIF remote sensing for monitoring GPP and ET dynamics. The results of this study not only contribute to a deeper understanding of the mechanisms linking SIF760 with GPP and ET but also provide new insights into the development and refinement of SIF-based remote sensing for carbon quantification in coastal blue-carbon ecosystems on a large-scale domain.

Improving the accuracy of SIF quantified from moderate spectral resolution airborne hyperspectral imager using SCOPE: assessment with sub-nanometer imagery

Hyperspectral imaging of solar-induced chlorophyll fluorescence (SIF) is required for plant phenotyping and stress detection. However, the most accurate instruments for SIF quantification, such as sub-nanometer (≤1-nm full-width at half-maximum, FWHM) airborne hyperspectral imagers, are expensive and uncommon. Previous studies have demonstrated that standard narrow-band hyperspectral imagers (i.e., 4–6-nm FWHM) are more cost-effective and can provide far-red SIF quantified at 760 nm (SIF760), which correlates strongly with precise sub-nanometer resolution measurements. Nevertheless, narrow-band SIF760 quantifications are subject to systematic overestimation owing to the influence of the spectral resolution (SR). In this study, we propose a modelling approach based on the Soil Canopy Observation, Photochemistry and Energy Fluxes (SCOPE) model with the objective of enhancing the accuracy of absolute SIF760 levels derived from standard airborne hyperspectral imagers in practical settings. The performance of the proposed method was evaluated using airborne imagery acquired from two airborne hyperspectral imagers (FWHM ≤ 0.2-nm and 5.8-nm) flown in tandem on board an aircraft that collected data from two different wheat and maize phenotyping trials. Leaf biophysical and biochemical traits were first estimated from airborne narrow-band reflectance imagery and subsequently used as SCOPE model inputs to simulate a range of top-of-canopy (TOC) radiance and SIF spectra at 1-nm FWHM. The SCOPE simulated radiance spectra were then convolved to match the spectral configuration of the narrow-band imager to compute the 5.8-nm FWHM SIF760. A site-specific model was constructed by employing the convolved 5.8-nm SR SIF760 as the independent variable and the 1-nm SR SIF760 directly simulated by SCOPE as the dependent variable. When applied to the airborne dataset, the estimated SIF760 at 1-nm SR from the standard narrow-band hyperspectral imager matched the reference sub-nanometer quantified SIF760 with root mean square error (RMSE) less than 0.5 mW/m2/nm/sr, yielding R2 = 0.93–0.95 from the two experiments. These results suggest that the proposed modelling approach enables the interpretation of SIF760 quantified using standard hyperspectral imagers of 4–6 nm FWHM for stress detection and plant physiological condition assessment.

Simulation of ecosystem fluxes with the SCOPE model: Sensitivity to parametrization and evaluation with flux tower observations

Accurate estimates of carbon, water and energy fluxes between the Earth surface and the atmosphere are crucial for enhancing our understanding of ecosystem–climate interactions. Such estimates can be made by combining remote sensing derived land surface parameters with climate reanalysis data. We analysed to what degree generic (plant functional type (PFT)-independent) satellite-derived vegetation properties and climate reanalysis data can explain land surface fluxes and to what extent the PFT-specific information extends the flux simulations. For this purpose, we used the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model, which combines radiative transfer in plant leaves and vegetation canopies with photosynthesis and energy balance in a single model representation of the vegetation. We evaluated the performance of SCOPE in simulating fluxes by comparison to 63 eddy covariance sites representing 10 PFTs. We varied the sources of maximum carboxylation capacity (Vcmax25) and BallBerrySlope values (default vs literature), the seasonality of Vcmax25 and the meteorological forcing (locally measured and climate reanalysis). The average performance of daily flux in terms of root-mean-square error (RMSE) was 2.3 ± 0.8 μmol CO2 m−2 s−1 (R2= 0.74 ± 0.12) for gross primary productivity (GPP), 24 ± 8 W m−2 (R2= 0.68 ± 0.16) for latent heat flux (λE) and 50 ± 15 W m−2 (R20.47±0.17) for sensible heat flux (H). The inter-site variability of the annual accumulated GPP flux was captured well with seasonally varying PFT-specific Vcmax25 (R2= 0.74, RMSE = 308 g C m−2 yr−1 and bias = −68 g C m−2 yr−1). The annual accumulated evapotranspiration (ET) was overestimated (R2= 0.31, RMSE = 101 mm yr−1 and bias = 37 mm yr−1), mainly in the ecosystems with subtropical Mediterranean climate, for which the soil resistance to evaporation from porous space (rss) had to be constrained from soil moisture content (SMC) or land surface temperature (LST). Overall, the study demonstrates that SCOPE model can simulate ecosystem flux with high accuracy without site-specific calibration of its parameters.

An angular normalization method for temperature vegetation dryness index (TVDI) in monitoring agricultural drought

Characterizing drought is mandatory for a thorough monitoring of crop growth. Such information can be inferred from radiometric measurements in optical and thermal spectral ranges. The Temperature Vegetation Dryness Index (TVDI) presents the advantage to mix spectral information since it associates land surface temperature (LST) with normalized difference vegetation index (NDVI). NDVI is known to be weakly impact by directional effects, which is not the case of TVDI as the accuracy assessment of LST can be severely hampered directional anisotropy (DA). In this paper, DA feature of TVDI is analyzed using the kernel-driven model Vinnikov-Li (VinLi) that serves to perform through simulations and remove DA. The study uses both airborne and satellite datasets. TVDI was found to be angularly dependent, with a possible uncertainty larger than 15%. Compared against TVDI without angular correction, the normalized results displayed a better correlation with soil moisture content, and the root mean squared error (RMSE) of estimated soil moisture content decreased obviously, from 0.050 m3/m3 to 0.045 m3/m3 for airborne data over maize canopies, from approximately 0.036 m3/m3 to 0.030 m3/m3 for satellite data over cropland canopies, and from approximately 0.032 m3/m3 to 0.028 m3/m3 for satellite data over grassland canopies. The evaluation was carried out using the complied synthetic datasets based on the Soil Canopy Observation, Photochemistry and Energy Fluxes (SCOPE) model. These also confirmed the occurrence of significant DAs of TVDIs and good performance of VinLi in reducing DA of TVDI by >50%. Based on the VinLi method, an angular-independent TVDI appears necessary to reduce uncertainties originated from illuminating and viewing geometries for the purpose of agriculture monitoring with possible drought episodes. Although further evaluation is still needed using surface measurements, the outcomes support a confident use of TVDI for observations from satellite and airborne/unmanned aerial vehicle.

Modelling sun-induced fluorescence for improved evaluation of forest carbon flux (GPP): Case study of tropical deciduous forest, India

Reliable estimation of forest carbon fluxes in space and time is a major challenge. Sun-Induced Chlorophyll Fluorescence (SIF) has been found closely linked to vegetation photosynthetic activity. But the application of SIF for direct estimation of Gross Primary Productivity (GPP) is not well investigated. In this study, we demonstrate the estimation of GPP for tropical deciduous forests located in North Western Himalayan Foothills in India using simulated SIF and fluorescence quantum yield (ФF) through a modified Light Use Efficiency (LUEf) model. SIF simulated using Soil Canopy Observation, Photochemistry, and Energy fluxes (SCOPE) model provided the estimate of Photosynthetically Active Radiation (PAR) intercepted by the canopy foliage and ФF. ФF, measured through the Pulse-Amplitude Modulated (PAM) fluorometer quantifies the emission rate of SIF per unit of the absorbed photon. The integration of these factors into the proposed LUEf model offered real-time photosynthetic efficiency and improved the estimate of GPP of the deciduous forest. The results show that GPP estimated from SIF (SIFTOC) was closely related to GPPEddy with R2 = 0.91 (p<0.05) with RMSE= 29.24 gC m−2 month−1 than GPP estimated from LUE model (R2=0. 79, p<0.05) and RMSE = 96.25 gC m−2 month−1). GPPMODIS underestimated the GPP with a non-significant correlation to GPPEddy for all the months with R2=0. 36 (p<0.05) and RMSE = 188.96 gC m−2 month−1. The results obtained are promising and the proposed model can be used for estimating carbon fluxes of forests over a larger spatial scale using satellite-derived SIF.

SIFT: Modeling Solar-Induced Chlorophyll Fluorescence Over Sloping Terrain

Solar-induced chlorophyll fluorescence (SIF) is found well correlated with gross primary productivity (GPP) and a good indicator of vegetation status. However, the influence of topography on SIF has not been studied, and SIF models with topographic consideration are needed to analyze this influence. Unfortunately, apart from computationally expensive 3-D models, current SIF models cannot work with sloping terrain. An efficient 1-D SIF model with topographic consideration (SIFT) is proposed in this study based on the well-known Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model. The evaluation of SIFT, by comparing with the 3-D Discrete Anisotropic Radiative Transfer (DART) model, demonstrates that it has high accuracy. This study also demonstrates that ignoring topography induces significant errors (exceeding 125% for a 60° slope) in canopy SIF simulations. The conclusion that the topography is an important factor for SIF and the proposed SIFT model will benefit those who are interested in SIF simulations and applications.

Quantifying vegetation biophysical variables from the Sentinel-3/FLEX tandem mission: Evaluation of the synergy of OLCI and FLORIS data sources

The ESA's forthcoming FLuorescence EXplorer (FLEX) mission is dedicated to the global monitoring of the vegetation's chlorophyll fluorescence by means of an imaging spectrometer, FLORIS. In order to properly interpret the fluorescence signal in relation to photosynthetic activity, essential vegetation variables need to be retrieved concomitantly. FLEX will fly in tandem with Sentinel-3 (S3), which conveys the Ocean and Land Colour Instrument (OLCI) that is designed to characterize the atmosphere and the terrestrial vegetation at a spatial resolution of 300 m. In this work we present the retrieval models of four essential biophysical variables: (1) Leaf Area Index (LAI), (2) leaf chlorophyll content (Cab), (3) fraction of absorbed photosynthetically active radiation (fAPAR), and (4) fractional vegetation cover (FCover). These variables can be operationally inferred by hybrid retrieval approaches, which combine the generalization capabilities offered by radiative transfer models (RTMs) with the flexibility and computational efficiency of machine learning methods. The RTM SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes) was used to generate a database of reflectance spectra corresponding to a large variety of canopy realizations, which served subsequently as input to train a Gaussian Process Regression (GPR) algorithm for each targeted variable. Three sets of GPR models were developed, based on different spectral band settings: (1) OLCI (21 bands between 400 and 1040 nm), (2) FLORIS (281 bands between 500 and 780 nm), and (3) their synergy. Their respective performances were assessed based on simulated reflectance scenes. Regarding the retrieval of Cab, the OLCI model gave good model performances (R2: 0.91; RMSE: 7.6 μg. cm−2), yet superior accuracies were achieved as a result of FLORIS' higher spectral resolution (R2: 0.96; RMSE: 4.8 μg. cm−2). The synergy of both datasets did not further enhance the variable retrieval. Regarding LAI, the improvement of the model performances by using only FLORIS spectra (R2: 0.87; RMSE: 1.05 m2.m−2) rather than only OLCI spectra (R2: 0.86; RMSE: 1.12 m2.m−2) was less evident but merging both data sets was more beneficial (R2: 0.88; RMSE: 1.01 m2.m−2). Finally, the three data sources gave good model performances for the retrieval of fAPAR and Fcover, with the best performing model being the Synergy model (fAPAR: R2: 0.99; RMSE: 0.02 and FCover: R2: 0.98; RMSE: 0.04). The ability of the models to process real data was subsequently demonstrated by applying the OLCI models to S3 surface reflectance products acquired over Western Europe and Argentina. Obtained maps showed consistent patterns and variable ranges, and comparison against corresponding Sentinel-2 products (coarsened to a 300 m spatial resolution) led to reasonable matches (R2: 0.5–0.7). Altogether, given the availability of the multiple data sources, the FLEX tandem mission will foster unique opportunities to quantify essential vegetation properties, and hence facilitate the interpretation of the measured fluorescence levels.

Simulating spatially distributed solar-induced chlorophyll fluorescence using a BEPS-SCOPE coupling framework

Remotely sensed solar-induced chlorophyll fluorescence (SIF) has been increasingly used to probe photosynthesis and model the gross primary productivity (GPP). Although SIF at the top of canopy (TOC) can be simulated using the coupled photosynthesis-fluorescence model SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes), simulating spatially distributed TOC SIF usually requires extensive calculations, entailing some challenges when applying the model to the regional and the global scales. This study puts forward a coupling framework that combines SIF and global terrestrial biosphere models (TBMs). The theory for fluorescence emissions and the fluorescence radiative transfer algorithm described in the SCOPE model were integrated with the “two-leaf”-based BEPS (Boreal Ecosystem Productivity Simulator) model. To simplify the fluorescence radiative transfer physics, we put forward a canopy-averaged leaf-level fluorescence to represent the fluorescence emitted from sunlit and shaded leaf groups and performed a sensitivity analysis to assess the determining factors in upscaling fluorescence from leaf scale to canopy scale. We found that the relationship between the leaf and canopy fluorescence at 740 nm was mainly affected by LAI. Although brown pigments and leaf inclination angle demonstrate some impacts on the scaling process, an LAI-based coefficient can well characterize the upscaling from leaf to canopy scale. Since our BEPS-SCOPE coupling model deploys the sunlit-shaded leaf separation strategy, we expect that it can efficiently characterize the nonlinear responses of photosynthesis and the associated fluorescence to environmental factors. The performance of our model was evaluated at both site and global scales, which demonstrated a good performance for most plant functional types (PFTs) except for needleleaf types that have a more clumped nature. Apart from these limitations, the presented model can contribute to efficiently simulating SIF at regional and global scales, and has the potential to reduce uncertainties in GPP estimation.

FLiES-SIF version 1.0: three-dimensional radiative transfer model for estimating solar induced fluorescence

Abstract. Global terrestrial ecosystems control the atmospheric CO2 concentration through gross primary production (GPP) and ecosystem respiration processes. Chlorophyll fluorescence is one of the energy release pathways of excess incident light in the photosynthetic process. Over the last 10 years, extensive studies have revealed that canopy-scale Sun-induced chlorophyll fluorescence (SIF), which potentially provides a direct pathway to link leaf-level photosynthesis to global GPP, can be observed from satellites. SIF is used to infer photosynthetic capacity of plant canopy; however, it is not clear how the leaf-level SIF emission contributes to the top-of-canopy directional SIF. Plant canopy radiative transfer models are useful tools to understand the mechanism of anisotropic light interactions such as scattering and absorption in plant canopies. One-dimensional (1-D) plane-parallel layer models (e.g., the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model) have been widely used and are useful to understand the general mechanisms behind the temporal and seasonal variations in SIF. However, a 1-D model does not explain the complexity of the actual canopy structures. Three-dimensional models (3-D) have a potential to delineate the realistic directional canopy SIFs. Forest Light Environmental Simulator for SIF (FLiES-SIF) version 1.0 is a 3-D Monte Carlo plant canopy radiative transfer model to understand the biological and physical mechanisms behind the SIF emission from complex forest canopies. The FLiES-SIF model is coupled with leaf-level fluorescence and a physiology module so that users are able to simulate how the changes in environmental and leaf traits as well as canopy structure affect the observed SIF at the top of the canopy. The FLiES-SIF model was designed as three-dimensional model, yet the entire modules are computationally efficient: FLiES-SIF can be easily run by moderate-level personal computers with lower memory demands and public software. In this model description paper, we focused on the model formulation and simulation schemes, and showed some sensitivity analysis against several major variables such as view angle and leaf area index (LAI). The simulation results show that SIF increases with LAI then saturated at LAI&gt;2–4 depending on the spectral wavelength. The sensitivity analysis also shows that simulated SIF radiation may decrease with LAI at a higher LAI domain (LAI&gt;5). These phenomena are seen in certain Sun and view angle conditions. This type of nonlinear and nonmonotonic SIF behavior towards LAI is also related to spatial forest structure patterns. FLiES-SIF version 1.0 can be used to quantify the canopy SIF in various view angles including the contribution of multiple scattering which is the important component in the near-infrared domain. The potential use of the model is to standardize the satellite SIF by correcting the bidirectional effect. This step will contribute to the improvement of the GPP estimation accuracy through SIF.

Estimation of Seasonal Sun-Induced Fluorescence Dynamics of Indian Tropical Deciduous Forests using SCOPE and Sentinel-2 MSI

This paper presents a new approach to estimate spatial Sun-Induced Fluorescence (SIF) using the empirical relationship between simulated Canopy Chlorophyll Concentration (CCC) and simulated SIF. PROSAIL model [PROpriétésSPECTrales (PROSPECT) and Scattering by Arbitrarily Inclined Leaves (SAIL) models] was used to simulate CCC. CCC maps were generated through an Automated Radiative Transfer Model Operator (ARTMO) using the PROSAIL model and Sentinel-2 Multi-Spectral Imager (MSI) imagery. The Soil Canopy Observation, Photochemistry, and Energy fluxes (SCOPE) model was used to simulate SIF emitted at 740 nm (SIF740), at 760 nm (SIF760), and top of canopy (SIFTOC) (640-850 nm). The SCOPE model, configured with the specification of the Sentinel-2 sensor, simulates SIF within the spectrum range of 640-850 nm. A non-linear logarithmic relationship (R2>0.9, p < 0.05) was observed between simulated SIF and simulated CCC. Simulated CCC was linearly related to observed CCC with R2 0.88, 0.92 and 0.89 and RMSE = 0.04, 0.17 and 0.09 gm/m2 at p < 0.05 for summer, post-monsoon and early winter respectively. Whereas, the simulated CCC did not capture the full range of CCC variability for the post-monsoon season. Simulated SIF (SIF760) was well correlated with SIF from Orbiting Carbon Observatory-2 (OCO-2) satellite with R2 0.68, 0.73 and 0.73 (RMSE = <1 W/m2/sr/μm, p < 0.05) for the month of summer (April), pre-monsoon (May) and early winter season (November) respectively. Temporal SIFTOC effectively captured the seasonal variability associated with the phenology of deciduous tree species. Among various Sentinel-2 MSI derived VIs, Red Edge NDVI (RENDVI) exhibited maximum sensitivity with SIF (highest monthly average R2> 0.6, p < 0.05). The spatial SIF would serve as an useful link between airborne /satellite derived SIF and in-situ fluorescence measurements to understand multiscale SIF variability of terrestrial vegetation.

A semi-empirical approach for modeling the vegetation thermal infrared directional anisotropy of canopies based on using vegetation indices

Measurements of the surface thermal infrared (TIR) radiance provides an estimate of the land surface temperature (LST). However, any TIR measurement must be acquired under a certain geometry observation, which may refer to strong directional anisotropies. Although physical radiative transfer models can provide high precision directional brightness temperature simulation, they are too complex for processing large volumes of satellite data. With the objective to compare TIR measures acquired under different viewing angles, the topic of angular normalization issue for retrieved LSTs could be treated based on semi-empirical modelling. In this paper, we consider such category of models to simulate the directional anisotropy of surface brightness temperatures in combination with visible and near-infrared (VNIR) data. In these models, the vegetation fraction and the hot spot effect are depicted by a vegetation index and a brightness factor, respectively. An evaluation of the method is performed with both synthetic and measured datasets. The directional anisotropies that are fitted by this semi-empirical model demonstrate good agreement with an extensive synthetic dataset that is generated with the Soil Canopy Observation, Photochemistry and Energy Fluxes (SCOPE) soil-vegetation-atmosphere transfer model. An evaluation using airborne multi-angle TIR data also reveals that this model performs well when predicting BT directional anisotropies, with root mean square errors (RMSEs) of less than 0.31 °C over a maize-planted area. Relative to Roujean-Lagouarde (RL) and Vinnikov models using only TIR data, the proposed model offers better performances. In addition, for future use with satellite data, the proposed model using observations at different times and the combination with VNIR BRDF model are also evaluated, and good results are obtained. It yields a promising approach for the angular normalization of LST and mosaics of fine-scale images.

Global Sensitivity Analysis of the SCOPE Model in Sentinel-3 Bands: Thermal Domain Focus

Sentinel-3 satellite has provided simultaneous observations in the optical (visible, near infrared (NIR), shortwave infrared (SWIR)) and thermal infrared (TIR) domains since 2016, with a revisit time of 1–2 days. The high temporal resolution and spectral coverage make the data of this mission attractive for vegetation monitoring. This study explores the possibilities of using the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model together with Sentinel-3 to exploit the two sensors onboard of Sentinel-3 (the ocean and land color instrument (OLCI) and sea and land surface temperature radiometer (SLSTR)) in synergy. Sobol’ variance based global sensitivity analysis (GSA) of top of atmosphere (TOA) radiance produced with a coupled SCOPE-6S model was conducted for optical bands of OLCI and SLSTR, while another GSA of SCOPE was conducted for the land surface temperature (LST) product of SLSTR. The results show that in addition to ESA level-2 Sentinel-3 products, SCOPE is able to retrieve leaf area index (LAI), leaf chlorophyll content (Cab), leaf water content (Cw), leaf senescent material (Cs), leaf inclination distribution (LAD). Leaf dry matter content (Cdm) and soil brightness, despite being important, were not confidently retrieved in some cases. GSA of LST in TIR domain showed that plant biochemical parameters—maximum carboxylation rate (Vcmax) and stomata conductance-photosynthesis slope (Ball-Berry m)—can be constrained if prior information on near-surface weather conditions is available. We conclude that the combination of optical and thermal domains facilitates the constraint of the land surface energy balance using SCOPE.

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

Abstract. Canopy structural and leaf photosynthesis parameterizations such as maximum carboxylation capacity (Vcmax), slope of the Ball–Berry stomatal conductance model (BBslope) 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 Vcmax, BBslope 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&gt;R2&gt;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.

Combing both simulated and field-measured data to develop robust hyperspectral indices for tracing canopy transpiration in drought-tolerant plant.

Transpiration plays a key role in water and energy fluxes at various scales. While in recent remote sensing offers a fast and convenient method for tracing transpiration at multiple scales, the approach is mostly indirect and relies on energy balance. Although several hyperspectral indices have been reported to show potentials for tracing transpiration directly, both at leaf and canopy scales, they remain in pioneer stages and need extensive validations. In this study, we used the Soil, Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model calibrated to arid ecosystems in Central Asia, to generate a simulated dataset for validation. Furthermore, new and robust indices have been developed by combining both simulated and in situ measured datasets. Results suggested that the SR(1525, 2150), ND(1425, 2145), and previously reported index of dSR(660,1040) have significant relationships with both simulated and in situ measured transpiration. Further analyses revealed that the ND(1425,2145) shows consistent performance, even with different methodologies of combining simulation and field-measured datasets. Statistically significant results were obtained in this study, even for a dominant drought-tolerant species in arid land, a place that typically has weak vegetation reflectance under strong background radiation. We foresee the approach being conducted in other regions where vegetation reflectance dominates. This may lead to robust hyperspectral indices being developed for directly tracing transpiration at various scales.

A Critical Evaluation on the Role of Aerodynamic and Canopy–Surface Conductance Parameterization in SEB and SVAT Models for Simulating Evapotranspiration: A Case Study in the Upper Biebrza National Park Wetland in Poland

Evapotranspiration (ET) estimation through the surface energy balance (SEB) and soil-vegetation-atmosphere-transfer (SVAT) models are uncertain due to the empirical parameterizations of the aerodynamic and canopy-substrate conductances (gA and gS) for heat and water vapor transfers. This study critically assessed the impact of conductance parameterizations on ET simulation using three structurally different SEB and SVAT models for an ecologically important North-Eastern European wetland, Upper Biebrza National Park (UBNP) in two consecutive years 2015 and 2016. A pronounced ET underestimation (mean bias −0.48 to −0.68 mm day−1) in SEBS (Surface Energy Balance System) was associated with an overestimation of gA due to uncertain parameterization of momentum roughness length and bare soil’s excess resistance to heat transfer (kB−1) under low vegetation cover. The systematic ET overestimation (0.65–0.80 mm day−1) in SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes) was attributed to the overestimation of both the conductances. Conductance parameterizations in SEBS and SCOPE appeared to be very sensitive to the general ecohydrological conditions, with a tendency of overestimating gA (gS) under humid (arid) conditions. Low ET bias in the analytical STIC (Surface Temperature Initiated Closure) model as compared to SEBS/SCOPE indicated the critical need for calibration-free conductance parameterizations for improved ET estimation.

Tracing water and energy fluxes and reflectance in an arid ecosystem using the integrated model SCOPE

Transpiration plays a critical role in the water cycle of terrestrial ecosystems, especially for arid ecosystems in which water availability is typically the main constraint for plant growth. Although remote sensing has provided insights into transpiration across different temporal and spatial scales, its potential has not been fully exploited. This is due to a lack of synchronous observations of fluxes and reflectance. Only a few models have attempted to incorporate both radiative transfer and physiological processes. In this study, we calibrated the newly developed Soil, Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model to trace synchronous fluxes of water, energy and reflectance, and thus, their interplays, in a typical arid ecosystem dominated by Haloxylon ammodendron based on long-term continuous field measurements. An initial global sensitivity analysis is conducted to identify parameters that have the greatest impact on model output before subsequent calibration with field data. The resulting calibrated model gives insight into the interplay between reflectance, energy and water fluxes in an arid ecosystem. The calibrated model is thus a useful tool to understand land surface fluxes and radiative transfer processes theoretically, from which additional reflectance information can be exploited to trace the physiological status of ecosystems.

Downscaling of solar-induced chlorophyll fluorescence from canopy level to photosystem level using a random forest model

Solar-induced chlorophyll fluorescence (SIF), an electromagnetic signal that can potentially indicate vegetation photosynthetic activity, can be retrieved from ground-based, airborne and satellite measurements. However, due to the scattering and re-absorption effects inside the leaves and canopy, SIF measured at the canopy level is only a small part of the total SIF emission at the photosystem level. Therefore, a downscaling mechanism of SIF from the canopy level to the photosystem level is important for better understanding the relationship between SIF and the vegetation gross primary production (GPP). In this study, firstly, we analyzed the canopy scattering effects using a simple parameterization model based on the spectral invariant theory. The probability for SIF photons to escape from the canopy was found to be related to the anisotropic spectral reflectance, canopy interception of the upward solar radiation, and leaf absorption. An empirical approach based on a Random Forest (RF) regression algorithm was applied to downscale SIF constrained by the red, red-edge and far-red anisotropic reflectance. The RF was trained using simulations conducted with the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model. The performance of the SIF downscaling method was evaluated with SCOPE and Discrete Anisotropic Radiative Transfer (DART) model simulations, ground measurements and airborne data. Results show that estimated SIF at the photosystem level matches well with simulated reference data, and the relationship between SIF and photosynthetically active radiation absorbed by chlorophyll is improved by SIF downscaling. This finding in combination with other evaluation criteria suggests the downscaling of canopy SIF as an efficient strategy to normalize species dependent effects of canopy structure and varying solar-view geometries. Based on our results for the SIF-APAR relationship, we expect that such normalization approaches can be helpful to improve estimates of photosynthesis using remote sensing measurements of SIF.

Potential of solar-induced chlorophyll fluorescence to estimate transpiration in a temperate forest

By utilizing continuous measurements of water fluxes and solar-induced chlorophyll fluorescence (SIF) over the entire growing season, we exploit the potential of broadband SIF in predicting plant transpiration (T) in a temperate forest. After reconstructing the full SIF spectrum from the selected absorption lines and simulations from the SCOPE (Soil Canopy Observation Photochemistry and Energy fluxes) model, linear regression (LR) and Gaussian processes regression (GPR) models are used to analyze the relation between T and combinations of different SIF bands. We find that SIF emissions in the near-infrared spectrum (at 720 nm, 740 nm and 760 nm) are more sensitive to T than SIF emissions in the red spectrum (at 685 nm and 687 nm). While conditions such as light and heat stress decouple the relationship between single-band SIF and T, the combination of different SIF bands allows the retrieval of reliable T estimates even in these conditions. Overall, we find that the use of SIF as a proxy for T yields estimates that are at least as accurate as those from traditional transpiration models such as the Penman-Monteith equation, which are input demanding and complex to apply to in situ and satellite data. Specifically, we find that (1) the SIF-T relationship deteriorates when Photosynthetically Active Radiation (PAR), vapor pressure deficit and air temperature exceed biological optimal thresholds; (2) a high leaf area index exerts a negative impact on the SIF-T correlation due to increasing scattering and (re)absorption of the SIF signal; (3) the SIF-T relationship does not change depending on the observation time during the day; and (4) temporal aggregation to days further enhanced the SIF-T correlations. Altogether, our results provide the first ground-based evidence that SIF emission has potential to be a close predictor of plant transpiration, especially when a combination of different SIF bands is considered.

A two parameter model to simulate thermal infrared directional effects for remote sensing applications

Abstract Measurements of land surface temperature (LST) performed in the thermal infrared (TIR) domain are prone to strong directional anisotropy. Instead of detailed analytical physical TIR models requiring too much input information and computational capacities, simplified parametric approaches capable to mimic and correct with precision the angular effects on LST will be deemed suitable for practical satellite applications. In this study, we present a simple two parameters model, so-called RL (Roujean-Lagouarde), which shows capabilities to properly depict the directional signatures of both urban and vegetation targets within an accuracy better than 1 °C. This latter value is the RMSE (root mean square error) obtained as the best adjustment of the RL model against in situ datasets. Then the RL approach was compared to a synthetic dataset generated by the model Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) in which large variability in meteorological scenarios, canopy structure and water status conditions was accounted for. Results indicate RMSE ≤ 0.6 °C which is a very hopeful result. Besides, the RL model performs even better than the popular parametric model of Vinnikov that encompasses two unknowns. The ability of RL model to better reproduce the hotspot phenomenon explains this feature. The RL model appears as a potential candidate for future operational processing chains of TIR satellite data because it fulfills the requirements of both simple analytical formulation and limited number of input parameters. Efforts nevertheless remain to be done on inversion methodologies.

Driving factors of the directional variability of thermal infrared signal in temperate regions

Abstract Land surface temperature (LST) is a good indicator of the land surface state. The measurement of LST is however prone to directional anisotropy which may severely affect the interpretation of the measurements if it is not corrected. This study aims at determining and describing the impact of various factors on anisotropy of continuous crops at mid-latitudes. The SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes) model is used as a data generator of directional anisotropy since it enables exploring a very large range of meteorological, biochemical and geometrical conditions. An original indicator, the standard deviation of anisotropy in principal plane, is used in order to investigate the impact of the tested variables and parameters. We found that anisotropy is, at first order, related to seasonal trends, in relation to the amount of incident radiation and the solar zenith angle. Then the geometrical structure of the canopy modifies the anisotropy (LAI, LADF, hot spot parameter) followed by the coupling between the water status of the soil and the stress of canopy. Wind speed which is known for having a significant impact on temperature level has a very limited influence on anisotropy. An analysis of the amplitude of anisotropy in the principal and perpendicular planes (from − 50° to 50° zenith) showed that anisotropy can reach up to 11 °C and ~ 3.5 °C respectively. The impact of satellite orbit on anisotropy is also discussed and it is found that, given the latitudes and the season, the anisotropy can severely affect measurements. This is particularly true when the satellite measurements are acquired in a configuration close to the solar principal plane, which often occur at low latitude. These results are of great help in the context of developing simple methods which could then be integrated into satellite data processing algorithms.