Canopy Soil Research Articles

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.

In situ bioaccumulation of metals by Prosopis juliflora and its detoxification potential at the metal contaminated sites

Heavy metals emanate from diverse anthropogenic activities and the top soil in the vicinity of these activities acts as an immediate sink and facilitates diffusion of heavy metals into the food chain. In the semi-arid plains of India, Prosopis juliflora is the most common and dominant weed along the motorways and barren lands including industrial environs. This investigation hypothesizes the adaptive nature of Prosopis juliflora in the metal enriched soils and attempts to understand its hyper-accumulating potential of metals besides bioconversion/detoxification capability. Prosopis juliflora samples (root, stem, leaves, and pods) from 100 sites in the environs of anthropogenic activities (vehicular emissions and industrial operations) were analyzed for heavy metal concentrations (Cu, Fe, Cr, Cd, Ni, Pb). Prosopis juliflora accumulate metals at the rate of 0.138 mg/kg/day DW for Copper (Cu), Fe: 0.142 mg/kg/day DW, Cr: 0.114 mg/kg/day DW, Ni: 0.048 mg/kg/day DW, Pb: 0.052 mg/kg/day DW, Cd: 0.009 mg/kg/day DW. Furthermore, X-ray Photoelectron Spectroscopy (XPS) metal oxidation state analysis revealed that in the pods of Prosopis juliflora heavy metals (Fe, Cr, Pb) largely existed in non-toxic form (toxic:non-toxic - 3:6), while in the under canopy soil, metals predominantly existed in toxic form (toxic:non-toxic - 7:2); conclusively XPS results ascertains the heavy metal bioconversion/detoxification potential of the plant. These findings suggest that presence of Prosopis juliflora coppice in the barren landscapes across the transportation corridors and metal based industrial zones may ideally favor phyto-remediation of heavy metals.

Soil classification and prediction model for critical land on the slopes of Mount Raung in Indonesia

Indonesia is in the ring of fire, which has an impact on the characteristics of volcanoes, one of which is Mount Raung. Mount Raung, part of the Iyang-Argopura mountains, has a role in influencing the development of soil types and the environment; it was recorded in 1586, 1987, 1597, and 1638. Soil types and environmental parameters have an influence on critical land conditions. Remote sensing technology has been used in various fields, one of which is land evaluation. This study examined the distribution of soil characteristics and the mapping of critical lands through remote sensing approaches. The method used to identify the morphological characteristics of soil classification and the potential for critical land was a descriptive-exploratory method. The results of the research on soil types are divided into three orders, namely Andisols, Inceptisols, and Alfisols. The three land orders are divided into nine great groups, with Lithic Eutrudepts having the highest area of 38.02%, followed by Typic Hapludalfs (21.70%), Typic Eutrudepts (9.79%), Typic Epiaquepts (7.84%), Aquic Eutrudepts (7.71%), Aquic Eutrudepts (5.64%), Fluventic Epiaquepts (5.30%), Typic Udivitrands (2.16%), and Vitric Hapludands (1.83%). Critical land based on the analysis of five factors of erosion, land use, slope, rainfall, and soil canopy density, is divided into four criteria i.e., critical area of 895.88 ha, medium critical 9,027.69 ha, and lightly critical of 14,096.89 ha. Land use, slope, and plant canopy density play a major role in the potential for critical land with a strong-very-strong level of closeness (0.350-0.610).

Impact of atmospheric dryness on solar-induced chlorophyll fluorescence: Tower-based observations at a temperate forest

Solar-induced chlorophyll fluorescence (SIF) is widely accepted as a proxy for gross primary productivity (GPP). Among the various SIF measurements, tower-based SIF measurements allow for continuous monitoring of SIF variation at a canopy scale with high temporal resolution, making it suitable for monitoring highly variable plant physiological responses to environmental changes. However, because of the strong and close relationship between SIF and absorbed photosynthetically active radiation (aPAR), it may be difficult to detect the influence of environmental drivers other than light conditions. Among the drivers, atmospheric dryness (vapor pressure deficit, VPD) is projected to increase as drought becomes more frequent and severe in the future, negatively impacting plants. In this study, we evaluated the tower-based high-frequency SIF measurement as a tool for detecting plant response to highly variable VPD. The study was performed in a mixed temperate forest in Virginia, USA, where a 40-m-tall flux tower has been measuring gas and energy exchanges and ancillary environmental drivers, and the Fluospec 2 system has been measuring SIF. We show that a proper definition of light availability to vegetation can reproduce SIF response to changing VPD that is comparable to GPP response as estimated from eddy covariance measurement: GPP decreased with rising VPD regardless of how aPAR was defined, whereas SIF decreased only when aPAR was defined as the PAR absorbed by chlorophyll (aPARchl) or simulated by a model (Soil Canopy Observation, Photochemistry and Energy fluxes, SCOPE). We simulated the effect of VPD on SIF with two different simulation modes of fluorescence emission representing contrasting moisture conditions, ‘Moderate’ and ‘Soil Moisture (SM) Stress’ modes. The decreasing SIF to rising VPD was only found in the SM Stress mode, implying that the SIF-VPD relationship depends on soil moisture conditions. Furthermore, we observed a similar response of SIF to VPD at hourly and daily scales, indicating that satellite measurements can be used to study the effects of environmental drivers other than light conditions. Finally, the definition of aPAR emphasizes the importance of canopy structure research to interpret remote sensing observations properly.

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

Abstract. 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.

Remote sensing estimation of sugar beet SPAD based on un-manned aerial vehicle multispectral imagery.

Accurate, non-destructive and cost-effective estimation of crop canopy Soil Plant Analysis De-velopment(SPAD) is crucial for precision agriculture and cultivation management. Unmanned aerial vehicle (UAV) platforms have shown tremendous potential in predicting crop canopy SPAD. This was because they can rapidly and accurately acquire remote sensing spectral data of the crop canopy in real-time. In this study, a UAV equipped with a five-channel multispectral camera (Blue, Green, Red, Red_edge, Nir) was used to acquire multispectral images of sugar beets. These images were then combined with five machine learning models, namely K-Nearest Neighbor, Lasso, Random Forest, RidgeCV and Support Vector Machine (SVM), as well as ground measurement data to predict the canopy SPAD of sugar beets. The results showed that under both normal irrigation and drought stress conditions, the SPAD values in the normal ir-rigation treatment were higher than those in the water-limited treatment. Multiple vegetation indices showed a significant correlation with SPAD, with the highest correlation coefficient reaching 0.60. Among the SPAD prediction models, different models showed high estimation accuracy under both normal irrigation and water-limited conditions. The SVM model demon-strated a good performance with a correlation coefficient (R2) of 0.635, root mean square error (Rmse) of 2.13, and relative error (Re) of 0.80% for the prediction and testing values under normal irrigation. Similarly, for the prediction and testing values under drought stress, the SVM model exhibited a correlation coefficient (R2) of 0.609, root mean square error (Rmse) of 2.71, and rela-tive error (Re) of 0.10%. Overall, the SVM model showed good accuracy and stability in the pre-diction model, greatly facilitating high-throughput phenotyping research of sugar beet canopy SPAD.

Greater differentiation in nitrogen sources is essential for co-occurring epiphytes under low-nitrogen canopy conditions

Acquiring enough nitrogen (N) to support their life processes in nutrient-poor canopies is a crucial challenge for epiphytes. When different epiphytes coexist in the same environment, they may use different strategies to acquire N. In this study, we employed stable isotope analysis to explore N acquisition and differentiation among co-occurring epiphytes in a subtropical forest in Southwest China. We found that functional group was the most important factor influencing N and 15N natural abundance (δ15N) of epiphytes, and that different epiphyte groups used different N acquisition, uptake, and utilization pathways. Our analysis of the enrichment factors suggested that biological nitrogen fixation, phorophytes, canopy soil and ground soil could participate in N acquisition of epiphyte groups to varying degrees. However, epiphytes were more affected by precipitation and other canopy N sources than soil. Our structural equation models (SEMs) further showed that the potential N sources of plants and substrates affected throughfall and stemflow, thereby influenced N acquisition and preference of N forms in epiphytes. Overall, our findings indicate that there is differentiation in N sources among co-occurring epiphytes.

Improving the Estimation of Canopy Fluorescence Escape Probability in the Near-Infrared Band by Accounting for Soil Reflectance

Solar-induced chlorophyll fluorescence (SIF) has been found to be a useful indicator of vegetation’s gross primary productivity (GPP). However, the directional SIF observations obtained from a canopy only represent a portion of the total fluorescence emitted by all the leaf photosystems because of scattering and reabsorption effects inside the leaves and canopy. Hence, it is crucial to downscale the SIF from canopy level to leaf level by modeling fluorescence escape probability (fesc) for improved comprehension of the relationship between SIF and GPP. Most methods for estimating fesc rely on the assumption of a “black soil background,” ignoring soil reflectance and the effect of scattering between soils and leaves, which creates significant uncertainties for sparse canopies. In this study, we added a correction factor considering soil reflectance, which was modeled using the Gaussian process regression algorithm, to the semi-empirical NIRv/FAPAR model and obtained the improved fesc model accounting for soil reflectance (called the fesc_GPR-SR model), which is suitable for near-infrared SIF downscaling. The evaluation results using two simulation datasets from the Soil–Canopy–Observation of Photosynthesis and the Energy Balance (SCOPE) model and the Discrete Anisotropic Radiative Transfer (DART) model showed that the fesc_GPR-SR model outperformed the NIRv/FAPAR model, especially for sparse vegetation, with higher accuracy for estimating fesc (R2 = 0.954 and RMSE = 0.012 for SCOPE simulations; R2 = 0.982 and RMSE = 0.026 for DART simulations) compared with the NIRv/FAPAR model (R2 = 0.866 and RMSE = 0.100 for SCOPE simulations; R2 = 0.984 and RMSE = 0.070 for DART simulations). The evaluation results using in situ observation data from multi-species canopies also suggested that the leaf-level SIF calculated by the fesc_GPR-SR model tracked better with photosynthetic active radiation absorbed by green components (APARgreen) for sparse vegetation (R2 = 0.937, RMSE = 0.656 mW/m2/nm) compared with the NIRv/FAPAR model (R2 = 0.921, RMSE = 0.904 mW/m2/nm). The leaf-level SIF calculated by the fesc_GPR-SR model was less sensitive to observation angles and differences in canopy structure among multiple species. These results emphasize the significance of accounting for soil reflectance in the estimation of fesc and demonstrate that the fesc_GPR-SR model can contribute to further exploring the physiological mechanism between SIF and GPP.

Climate, as well as branch-level processes, drive canopy soil abundance and chemistry

Canopy soils can be important to forest nutrient cycling, hydrology, and biodiversity, but the factors determining their distribution and properties are largely unknown. We surveyed canopy soils across gradients of temperature and precipitation in six primary forests in Costa Rica. We used solid-state 13C nuclear magnetic resonance (NMR) and mass spectrometry (MS) to understand how the composition of canopy soil organic matter varies within and across sites. Climate, particularly fog, appears to drive canopy soil abundance across forests, while tree size determines canopy soil abundance within a forest. Canopy soil chemistry mostly varied within sites, though temperature was associated with the carbon (C) to nitrogen ratio, total dissolved nitrogen, and alkyl-C abundance, while fog explained some of the variation in dissolved organic carbon and O-alkyl C abundance. This study is the first-ever glimpse into large- and small-scale drivers of canopy soil abundance and biochemical composition. Our results highlight the importance of tree size and fog in determining the quantity and quality of canopy soil organic matter, suggesting that canopy soil stocks may be particularly vulnerable to climate and land use change. Identifying how multi-scale factors influence canopy organic matter processes will enhance our ability to identify and predict how environmental change might affect the abundance and chemistry of canopy soils and thus biodiversity that they support.

Old-growth Acer macrophyllum trees host a unique suite of arbuscular mycorrhizal fungi and other root-associated fungal taxa in their canopy soil environment

ABSTRACT Canopy soils occur on tree branches throughout the temperate rainforests of the Pacific Northwest Coast and are recognized as a defining characteristic of these ecosystems. Certain tree species extend adventitious roots into these canopy soil environments. Yet, research on adventitious root-associated fungi remains limited. Our study used microscopy to compare fungal colonization intensity between canopy and forest floor roots of old-growth bigleaf maple (Acer macrophyllum) trees. Subsequently, two high-throughput sequencing platforms were used to explore the spatial and seasonal variation of root-associated fungi between the two soil environments over one year. We found that canopy and forest floor roots had similar colonization intensity and were associating with a diversity of arbuscular mycorrhizal fungi and other potential symbionts, many of which were resolved to species level. Soil environment and seasonality affected root-associated fungal community composition, and several fungal species were indicative of the canopy soil environment. In Washington State’s (USA) temperate old-growth rainforests, these canopy soil environments host a unique suite of root-associated fungi. The presence of arbuscular mycorrhizae provides further evidence that adventitious roots form fungal associations to exploit canopy soils for resources, and there may be novel relationships forming with other fungi. These soils may be providing a redundancy compartment (i.e., “nutrient reserve”), imparting a resiliency to disturbances for certain old-growth trees.

Interactions between epiphytes during canopy soil formation: an experiment in a lower montane cloud forest of southeast Mexico.

In several montane forests around the world, epiphytes coexist in mats, sharing the rhizosphere and forming histosol-type soils rich in nutrients. The role of these epiphytes in the formation of canopy soil and the fitness costs that epiphytes face when cohabiting in these mats are unknown. In a lower montane cloud forest in central Veracruz, Mexico, a 2-year factorial experiment was carried out with the presence/absence of ramets of Phlebodium areolatum (Polypodiaceae), Tillandsia kirchhoffiana, T. multicaulis and T. punctulata (Bromeliaceae). We examined (i) which epiphyte species contribute to the formation of canopy soil, (ii) the role of epiphyte composition in the soil nutrient composition, and (iii) the fitness costs faced by epiphytes when cohabiting. Canopy soil formation highest when P. areolatum is present. Soil nutrient content does not change with epiphyte composition, is influenced by the microbiota, and P content decreases with the presence of epiphytes. The fitness costs show that the species compete, decreasing their survival and growth, but the competitive capacity differs between the species. We conclude that P. areolatum is an ecosystem engineer that promotes the creation of canopy soil but is a poor competitor. The results coincide with the model of succession by facilitation. Canopy soil is a slow-created component whose nutrient content does not depend on the epiphytic flora. In epiphyte mats, the dominant interactions are competitive, but there is also facilitation.

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.

Using the SCOPE model for potato growth, productivity and yield monitoring under different levels of nitrogen fertilization

Most applications of remote sensing in agricultural crop monitoring use multispectral imaging techniques, but with upcoming hyperspectral missions, the opportunity arises to better estimate pigment absorption and crop structure by exploiting the full solar reflective spectrum. In this study, we demonstrate how hyperspectral time series can be used with the Soil Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model to estimate crop yield variability among fields, crop varieties and nitrogen treatments generically, i.e. without a calibration with in situ , data. Field experiments were conducted in two potato fields in the Netherlands between May and September 2019. The fields were planted with five varieties of potato, under three nitrogen fertilization treatments. By fitting the model to the full VNIR-SWIR spectrum of measured hyperspectral reflectance, we retrieved the model input parameters of Leaf Area Index (LAI), leaf chlorophyll content (Cab) and leaf water content (Cw) and simulated the photosynthesis throughout the season using data of local Automatic Weather Stations (AWS). Statistical analysis of measured and retrieved traits of LAI, Cab and canopy water content showed that two fields responded differently to the treatments, exhibiting fewer classes than were expected based on the experimental design. Potato yield, which was estimated as the sum of photosynthesis flux multiplied by the harvest index of 0.64, correlated with the measured tuber dry weight with R 2 0.36 and RMSE 2.5 t ha −1 . This study demonstrates that even in the absence of crop or variety specific information, hyperspectral reflectance and local weather data ingested into SCOPE can explain a substantial part of the observed variability in yield among fields. • Time series of hyperspectral canopy reflectance were recorded in two potato fields. • The variation in traits was created by nitrogen fertilization treatments. • SCOPE model retrieved plant traits contained information about treatments. • SCOPE-simulated accumulated photosynthesis correlated with the final dry yield. • SCOPE model is more generic than dimensionality reduction methods.

An exploration of solar-induced chlorophyll fluorescence (SIF) factors simulated by SCOPE for capturing GPP across vegetation types

Solar-induced chlorophyll fluorescence (SIF) has been regarded as proxy data of vegetation photosynthesis; thus, it is assimilated into the terrestrial carbon cycle modeling. The Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model is one of the most utilized models of SIF simulation. However, the currently incomplete understanding of SCOPE SIF factors and the lack of exploring how SCOPE works under different vegetation types would deteriorate further carbon cycle research. Herein, this study disentangled decisive SIF factors in the SCOPE model; then, a sample SIF dataset (SynSIF), with spatial resolutions of both 0.02∘ and 0.05∘, was simulated through SCOPE model using factors above. Then this study validated how far SCOPE simulating SIF could capture GPP, compared with other SIF datasets. The results showed that: (1) There are five decisive SIF factors in SCOPE model, including plant status (leaf chlorophyll content and leaf area index) and meteorological parameters (incoming shortwave radiation, air temperature, and atmospheric vapor pressure). (2) The linear relationship of SynSIF-GPP outachieved other SIF datasets across all six vegetation types in southern South America, Asia, and Africa, improving R2 averagely by 0.33, 0.28, and 0.15, respectively. (3) SynSIF in Oceania and Europe, revealing GPP better in shrublands (with SynSIF-GPP R2 increasing by 0.15 and 0.16, respectively) and grasslands (with SynSIF-GPP coefficients increasing by 0.14 and 0.06, respectively), illustrated spatially complementary characteristics with GOSIF across varying vegetation types. Thus, we anticipate that this study could provide more complete information for SCOPE simulating SIF in different biome research when estimating the terrestrial carbon cycle.

Exploring the Potential of SCOPE Model for Detection of Leaf Area Index and Sun-Induced Fluorescence of Peatland Canopy

The study of peatland is challenging due to the water saturation and evergreen mixed vegetation that ranges from simple forms of plants such as mosses to higher forms of plants such as cranberries, grasses, etc. The changing water level through the growing season makes the peatland vegetation very dynamic. In this work, we have used ground-level remote-sensing signals to understand the dynamic nature of peatland vegetation. We have also estimated the leaf area index (LAI) and Sun-Induced fluorescence (SIF) through the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model. The estimated LAI and SIF were compared with the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Near-Infrared Reflectance of vegetation (NIRv), and measured SIF. The modeled LAI was observed to be significantly correlated with NDVI, EVI, and NIRv, whereas a good correlation was observed between measured and modeled SIF. Along with showing the dynamic behavior of peatland vegetation, the study indicates that SCOPE in its inverted form can be used to estimate reflectance-based LAI for peatland, which can be more reliable to present biomass and productivity of peatland ecosystem in comparison to transmittance-based LAI measurement for such ecosystem. The good correlation between measured and modeled SIF at 760 nm indicates that a reliable SIF value can be estimated through the SCOPE model for a complex ecosystem such as peatland, which can be very helpful in the absence of high-resolution hyperspectral data (usually used for SIF measurements).

Comparison between Physical and Empirical Methods for Simulating Surface Brightness Temperature Time Series

Land surface temperature (LST) is a vital parameter in the surface energy budget and water cycle. One of the most important foundations for LST studies is a theory to understand how to model LST with various influencing factors, such as canopy structure, solar radiation, and atmospheric conditions. Both physical-based and empirical methods have been widely applied. However, few studies have compared these two categories of methods. In this paper, a physical-based method, soil canopy observation of photochemistry and energy fluxes (SCOPE), and two empirical methods, random forest (RF) and long short-term memory (LSTM), were selected as representatives for comparison. Based on a series of measurements from meteorological stations in the Heihe River Basin, these methods were evaluated in different dimensions, i.e., the difference within the same surface type, between different years, and between different climate types. The comparison results indicate a relatively stable performance of SCOPE with a root mean square error (RMSE) of approximately 2.0 K regardless of surface types and years but requires many inputs and a high computational cost. The empirical methods performed relatively well in dealing with cases either within the same surface type or changes in temporal scales individually, with an RMSE of approximately 1.50 K, yet became less compatible in regard to different climate types. Although the overall accuracy is not as stable as that of the physical method, it has the advantages of fast calculation speed and little consideration of the internal structure of the model.

Difference in seasonal peak timing of soybean far-red SIF and GPP explained by canopy structure and chlorophyll content

Recent advances in remotely sensed solar-induced chlorophyll fluorescence (SIF) have provided an exciting and promising opportunity for estimating gross primary production (GPP). Previous studies mainly focused on the linear correlation between SIF and GPP and the slope of the SIF-GPP relationship, both of which lack rigorous consideration of the seasonal trajectories of SIF and GPP. Here, we investigated the timing of seasonal peaks of far-red SIF and GPP in soybean fields by integrating tower data, satellite data, and process-based Soil Canopy Observation of Photosynthesis and Energy (SCOPE, v2.0) model simulations. We found inconsistency between the seasonal peak timing of far-red SIF and GPP in three of four soybean fields based on tower far-red SIF and eddy-covariance measurements. In particular, far-red SIF reached its seasonal maximum 14–17 days earlier than GPP. This far-red SIF-GPP difference in peak timing degraded the correlation between sunny-day far-red SIF and GPP at daily scale (Pearson r = 0.83–0.87 at the site with 14–17 days difference and Pearson r = 0.96 at the site with no difference), and it can be explained by a divergence in the seasonality between absorbed photosynthetic active radiation (APAR) and canopy chlorophyll content (ChlCanopy). We found that the seasonality of far-red SIF - a byproduct of the light reactions of photosynthesis - was primarily controlled by APAR, whereas GPP seasonality was dominated by ChlCanopy. Further, SCOPE model simulations showed that the seasonal patterns of leaf area index (LAI), leaf chlorophyll content (ChlLeaf) and leaf angle distribution (LAD) could affect the different peak timing of SIF and GPP and consequently the seasonal relationship between far-red SIF and GPP. A further increase in LAI after the fraction of light absorption (FPAR) saturates and a later peak of ChlLeaf compared to LAI results in a later peak of GPP compared to far-red SIF. More horizontal leaf angles can further exacerbate this difference. Our results advance mechanistic understanding of the SIF-GPP relationships and combining chlorophyll content information with SIF could potentially improve remote-sensing-based GPP estimation.

Influence of short-term surface temperature dynamics on tree orchards energy balance fluxes

Land surface temperature (LST) plays an essential role in developing and applying precision agriculture protocols, especially for calculating crop evapotranspiration (ETc) by surface energy balance (SEB) approaches; and for determining crop water status. However, LST is quite dependent on the meteorological conditions, which can rapidly vary. This variability, together with the limited meterological data acquisition frequency in most weather stations, can lead to the miscalculation of the SEB components, especially relevant when used for irrigation purposes.The present study assessed the temporal dynamic of LST in a very short period of time (20-minutes) through the acquisition of multiple thermal imagery. Additionally, a combination of SEB approach with Eddy Covariance technique was performed for quantifying the effect that LST variations have on the sensible (H) and latent (LE) heat fluxes.Even under steady meteorological conditions, temporal variations in LST of 3.5 and 4.0 K were observed for tree canopy and sunny bare soil surfaces, respectively. These LST oscillations reached values of about 7.8 and 17.9 K for tree canopies and bare soil when heterogeneous meteorological conditions were observed (i.e. cloud presence). Such LST differences translated into H and LE differences of about 26 and 19%, respectively; with variations up to 5 (for H) and 2.7 times (for LE) under fast-varying meteorological conditions.The obtained results suggest the necessity of acquiring thermal imagery when steady meteorological conditions exist or, otherwise, ensuring the collection of instantaneous meteorological data for applying post-processing corrections. This is of importance when incorporating the obtained ETc maps into precision irrigation protocols.

Prediction of Soil Water Content and Electrical Conductivity Using Random Forest Methods with UAV Multispectral and Ground-Coupled Geophysical Data

The volumetric water content (VWC) of soil is a critical parameter in agriculture, as VWC strongly influences crop yield, provides nutrients to plants, and maintains the microbes that are needed for the biological health of the soil. Measuring VWC is difficult, as it is spatially and temporally heterogeneous, and most agricultural producers use point measurements that cannot fully capture this parameter. Electrical conductivity (EC) is another soil parameter that is useful in agriculture, since it can be used to indicate soil salinity, soil texture, and plant nutrient availability. Soil EC is also very heterogeneous; measuring EC using conventional soil sampling techniques is very time consuming and often fails to capture the variability in EC at a site. In contrast to the point-based methods used to measure VWC and EC, multispectral data acquired with unmanned aerial vehicles (UAV) can cover large areas with high resolution. In agriculture, multispectral data are often used to calculate vegetation indices (VIs). In this research UAV-acquired VIs and raw multispectral data were used to predict soil VWC and EC. High-resolution geophysical methods were used to acquire more than 41,000 measurements of VWC and 8000 measurements of EC in 18 traverses across a field that contained 56 experimental plots. The plots varied by crop type (corn, soybeans, and alfalfa) and drainage (no drainage, moderate drainage, high drainage). Machine learning was performed using the random forest method to predict VWC and EC using VIs and multispectral data. Prediction accuracy was determined for several scenarios that assumed different levels of knowledge about crop type or drainage. Results showed that multispectral data improved prediction of VWC and EC, and the best predictions occurred when both the crop type and degree of drainage were known, but drainage was a more important input than crop type. Predictions were most accurate in drier soil, which may be due to the lower overall variability of VWC and EC under these conditions. An analysis of which multispectral data were most important showed that NDRE, VARI, and blue band data improved predictions the most. The final conclusions of this study are that inexpensive UAV-based multispectral data can be used to improve estimation of heterogenous soil properties, such as VWC and EC in active agricultural fields. In this study, the best estimates of these properties were obtained when the agriculture parameters in a field were fairly homogeneous (one crop type and the same type of drainage throughout the field), although improvements were observed even when these conditions were not met. The multispectral data that were most useful for prediction were those that penetrated deeper into the soil canopy or were sensitive to bare soil.