This study integrates a dynamic plant growth model with a three-dimensional (3D) radiative transfer model (RTM) for maize traits retrieval using high spatial-spectral resolution airborne data. The research combines the Discrete Anisotropic Radiative Transfer (DART) model with the Dynamic L-System-based Architectural maize (DLAmaize) growth model to simulate field reflectance. Comparison with the 1D RTM SAIL revealed limitations in representing row structure effects, field slope, and complex light-canopy interactions. Novel Global Sensitivity Analyses (GSA) were carried out using dependence-based methods to overcome limitations traditional variance-based approaches, enabling better characterization of hyperspectral sensitivity to changes in leaf biochemistry, canopy architecture, and soil moisture. GSA provided complementary results to assess estimation uncertainties of the proposed traits retrieval method across growth stages. A hybrid inversion framework combining DART simulations with an active learning strategy using Kernel Ridge Regression was implemented for traits estimation. The approach was validated using ground data and HyPlant-DUAL airborne hyperspectral images from two field campaigns in 2018 and achieved high retrieval accuracy of key maize traits: leaf area index (LAI, R2=0.91, RMSE=0.42 m2/m2), leaf chlorophyll content (LCC, R2=0.61, RMSE=3.89 mu g/cm2), leaf nitrogen content (LNC, R2=0.86, RMSE=1.13 x 10-2 mg/cm2), leaf dry matter content (LMA, R2=0.84, RMSE=0.15 mg/cm2), and leaf water content (LWC, R2=0.78, RMSE=0.88 mg/cm2). The validated models were used to generate two-date 10 m resolution maps, showing good spatial consistency and traits dynamics. The findings demonstrate that integrating 3D RTMs with dynamic growth models is suited for maize trait mapping from hyperspectral data in varying growing conditions.

The wheat powdery mildew (WPM) is one of the most severe crop diseases worldwide, affecting wheat growth and causing yield losses. The WPM was a bottom-up disease that caused the loss of cell integrity, leaf wilting, and canopy structure damage with these symptoms altering the crop's functional traits (CFT) and canopy spectra. The unmanned aerial vehicle (UAV)-based hyperspectral analysis became a mainstream method for WPM detection. However, the CFT changes experienced by infected wheats, the relationship between CFT and canopy spectra, and their role in WPM detection remained unclear, which might blur the understanding for the WPM infection. Therefore, this study aimed to propose a new method that considered the role of CFT for detecting WPM and estimating disease severity. The UAV hyperspectral data used in this study were collected from the Plant Protection Institute's research demonstration base, Xinxiang city, China, covering a broad range of WPM severity (0-85 %) from 2022 to 2024. The potential of eight CFT [leaf structure parameter (N), leaf area index (LAI), chlorophyll a + b content (Cab), carotenoids (Car), Car/Cab, anthocyanins (Ant), canopy chlorophyll content (CCC) and average leaf angle (Deg)] obtained from a hybrid method combining a radiative transfer model and random forest (RF) and fifty-five narrow-band hyperspectral indices (NHI) was explored in WPM detection. Results indicated that N, Cab, Ant, Car, LAI, and CCC showed a decreasing trend with increasing disease severity, while Deg and Car/Cab exhibited the opposite pattern. There were marked differences between healthy samples and the two higher infection levels (moderate and severe infection) for Cab, Car, LAI, Deg, CCC, and Car/Cab. N and Ant only showed significant differences between the healthy samples and the highest infection level (severe infection). As Cab, Car, and Ant decreased, the spectral reflectance in the visible light region increased. The decrease in N and LAI was accompanied by a reduction in reflectance across the entire spectral range and the near-infrared area, which was exactly the opposite of Deg. The introduction of CFT greatly improved the accuracy of the WPM severity estimation model with R2 of 0.92. Features related to photosynthesis, pigment content, and canopy structure played a decisive role in estimating WPM severity. Also, results found that the feature importance showed a remarkable interchange as increasing disease levels. Using features that described canopy structure changes, such as optimized soil adjusted vegetation index, LAI, visible atmospherically resistant indices, and CCC, the mild infection stage of this disease was most easily distinguished from healthy samples. In contrast, most severe impacts of WPM were best characterized by features related to photosynthesis (e.g., photochemical reflectance index 515) and pigment content (e.g., normalized phaeophytinization index). This study help deepen the understanding of symptoms and spectral responses caused by WPM infection.

Component temperature and emissivity are crucial for understanding plant physiology and urban thermal dynamics. However, existing thermal infrared unmixing methods face challenges in simultaneous retrieval and multicomponent analysis. We propose Thermal Remote sensing Unmixing for Subpixel Temperature and emissivity with the Discrete Anisotropic Radiative Transfer model (TRUST-DART), a gradient-based multi-pixel physical method that simultaneously separates component temperature and emissivity from non-isothermal mixed pixels over urban areas. TRUST-DART utilizes the DART model and requires inputs including at-surface radiance imagery, downwelling sky irradiance, a 3D mock-up with component classification, and standard DART parameters (e.g., spatial resolution and skylight ratio). This method produces maps of component emissivity and temperature. The accuracy of TRUST-DART is evaluated using both vegetation and urban scenes, employing Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images and DART-simulated pseudo-ASTER images. Results show a residual radiance error is approximately 0.05 W/(m2 & sdot;sr). In absence of the co-registration and sensor noise errors, the median residual error of emissivity is approximately 0.02, and the median residual error of temperature is within 1 K. This novel approach significantly advances our ability to analyze thermal properties of urban areas, offering potential breakthroughs in urban environmental monitoring and planning. The source code of TRUSTDART is distributed together with DART (https://dart.omp.eu).

Remotely sensed top-of-the-canopy (TOC) SIF is highly impacted by non-physiological structural and environmental factors that are confounding the photosystems' emitted SIF signal. Our proposed method for scaling TOC SIF down to photosystems' (PSI and PSII) level uses a three-dimensional (3D) modeling approach, capable of accounting physically for the main confounding factors, i.e., SIF scattering and reabsorption within a leaf, by canopy structures, and by the soil beneath. Here, we propose a novel SIF downscaling method that separates the structural component from the functional physiological component of TOC SIF signal by using the 3D Discrete Anisotropic Radiative Transfer (DART) model coupled with the leaf-level fluorescence model Fluspect-CX, and estimates the Fluorescence Quantum Efficiency (FQE) at photosystem level. The method was first applied on in- situ diurnal measurements acquired at the top of the canopy of an alfalfa crop with a near-distance point- measuring FloX system. The retrieved photosystem-level FQE diurnal courses correlated significantly with photosynthetic yield of PSII measured by an active leaf florescence instrument MiniPAM (R = 0.87, R2 = 0.76 before and R =-0.82, R2 = 0.67 after 2.00 pm local time). Diurnal FQE trends of both photosystems jointly were descending from late morning 9.00 am till afternoon 4.00 pm. A slight late-afternoon increase, observed for three days between 4.00 and 7.00 pm, could be attributed to an increase in FQE of PSI that was retrieved separately from PSII. The method was subsequently extended and applied to airborne SIF images acquired with the HyPlant imaging spectrometer over the same alfalfa field. While the input canopy SIF radiance computed by two different methods, i) a spectral fitting method (SFM) and ii) a spectral fitting method neural network (SFMNN), produce broad and irregularly shaped (skewed) histograms (spatial coefficients of variation: CV = 29-35 % and 14-20 %, respectively), the retrieved HyPlant per-pixel FQE estimates formed significantly narrower and regularly bell- shaped near-Gaussian histograms (CV = 27-34 % and 14-17 %, respectively). The achieved spatial homogeneity of resulting FQE maps confirms successful removal of the TOC SIF radiance confounding impacts. Since our method is based on direct matching of measured and physically modelled canopy SIF radiance, simulated by 3D radiative transfer, it is versatile and transferable to other canopy architectures, including structurally complex canopies such as forest stands.

Ability of remotely sensed solar-induced chlorophyll fluorescence (SIF) to serve as a vegetation productivity and stress indicator is impaired by confounding factors, such as varying crop-specific canopy structure, changing solar illumination angles, and SIF-soil optical interactions. This study investigates two normalisation approaches correcting diurnal top-of-canopy SIF observations retrieved from the O2-A absorption feature at 760 nm (F 760 hereafter) of summer barley crops for these confounding effects. Nadir SIF data was acquired over nine breeding experimental plots simultaneously by an airborne imaging spectrometer (HyPlant) and a drone-based highperformance point spectrometer (AirSIF). Ancillary measurements, including leaf pigment contents retrieved from drone hyperspectral imagery, destructively sampled leaf area index (LAI), and leaf water and dry matter contents, were used to test the two normalisation methods that are based on: i) the fluorescence correction vegetation index (FCVI), and ii) three versions of the near-infrared reflectance of vegetation (NIRV). Modelling in the discrete anisotropic radiative transfer (DART) model revealed close matches for NIRv-based approaches when corrected canopy SIF was compared to simulated total chlorophyll fluorescence emitted by leaves (R2 = 0.99). Normalisation with the FCVI also performed acceptably (R2 = 0.93), however, it was sensitive to variations in LAI when compared to leaf emitted chlorophyll fluorescence efficiency. Based on the results modelled in DART, the NIRvH1 normalisation was found to have a superior performance over the other NIRv variations and the FCVI normalisation. Comparison of the SIF escape fractions suggests that the escape fraction estimated with NIRvH1 matched escape fraction extracted from DART more closely. When applied to the experimental drone and airborne nadir canopy SIF data, the agreement between NIRvH1 and FCVI produced chlorophyll fluorescence efficiency was very high (R2 = 0.93). Nevertheless, NIRvH1 showed higher uncertainties for areas with low vegetation cover indicating an unaccounted contribution of SIF-soil interactions. The diurnal courses of chlorophyll fluorescence efficiency for both approaches differed not significantly from simple normalisation by incoming and apparent photosynthetically active radiation. In conclusion, SIF normalisation with NIRvH1 more accurately compensates the effects of canopy structure on top of canopy far red SIF, but when applied to top of canopy in-situ data of spring barley, the effects of NIRvH1 and FCVI on the diurnal course of SIF had a similar influence.

Shortcomings and uncertainties in the model representation of atmospheric transformations (the aging) of organic aerosol (OA) have long been identified as one of the potential sources of considerable uncertainty in OA simulations with both global and regional models. However, the impact of this uncertainty on predictions of radiative and climate effects of both anthropogenic and biomass burning (BB) aerosol yet needs to be understood. This study examines the importance of the model representation of OA for simulating the direct radiative effect (DRE) of Siberian BB aerosol in the eastern Arctic. We employ a regional coupled chemistry-meteorology model and a global fire emission database to simulate the optical properties and DRE of BB aerosol emitted from intense Siberian fires in July 2016 and compare the DRE estimates that were obtained using two alternative representations of Siberian BB OA. One of them is a default OA representation that predicts very little secondary OA (SOA), and another involves a simple original OA parameterization that has been developed previously within the volatility basis set (VBS) framework and features a strong production of SOA. The simulations of the aerosol optical properties are evaluated against satellite observations of the aerosol optical depth (AOD) in Siberia and the Arctic as well as against values of the single scattering albedo derived from in situ observations of the aerosol absorption and scattering coefficients at four Arctic sites. While the simulations with the default OA representation are found to strongly underestimate AOD both in Siberia and the eastern Arctic, the use of the VBS parameterization considerably improves the agreement between the AOD simulations and observations in both regions. Simulations of the single scattering albedo are found to be overall rather adequate with both representations. Differences in the OA representations are found to result in major differences in the estimates of the DRE of Siberian BB aerosol in the eastern Arctic. Specifically, although the simulations with both representations predict that the DRE is predominantly negative at the top of the atmosphere (TOA), the magnitude of the mean DRE is found to be more than twice as large (6.0 W m-2) with the VBS parameterization than with the default OA representation (2.8 W m-2). An even larger difference (by a factor of 3.5) is found between the estimates of the DRE over the snow-or ice-covered areas. The different treatments of the BB OA evolution are associated also with considerably different contributions of black and brown carbon to the DRE estimates. Overall, our results indicate that model estimates of the DRE of Siberian BB aerosol in the eastern Arctic are strongly sensitive to the assumptions regarding the evolution of OA in Siberian BB plumes and that the SOA formation in these plumes is one of the major factors determining the magnitude of the radiative effects of Siberian BB aerosol in the real atmosphere.

Studies on optical properties of aerosols can reduce the uncertainty for modelling direct radiative forcing (DRF) and improve the accuracy for discussing aerosols effects on the Tibetan Plateau (TP) climate. This study investigated the spatiotemporal variation of aerosol optical and microphysical properties over TP based on OMI and MERRA2, and assessed the influence of aerosol optical properties on DRF at NamCo station (30 degrees 46.440N, 90 degrees 59.310E, 4730 m) in the central TP from 2006 to 2017 based on a long measurement of AERONET and the modelling of SBDART model. The results show that aerosol optical depth (AOD) exhibits obvious seasonal variation over TP, with higher AOD500nm (>0.75) during spring and summer, and lower value (<0.25) in autumn and winter. The aerosol concentrations show a fluctuated rising from 1980 to 2000, significant increasing from 2000 to 2010 and slight declining trend after 2013. Based on sensitivity experiments, it is found that AOD and single scattering albedo (SSA) have more important impact on the DRF compared with a values and ASY. When AOD440nm increases by 60%, DRF at the TOA and ATM is increased by 57.2% and 60.2%, respectively. When SSA440nm increases by 20%, DRF at the TOA and ATM decreases by 121% and 96.7%, respectively. (c) 2022 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

The uncertainty of passive microwave retrievals of snowfall is notoriously high where high-frequency surface emissivity is significantly reduced and varies markedly in response to the changes in snowpack physical properties. Using the dense media radiative transfer theory, this article studies the potential effects of terrestrial snow-cover depth, density, and grain size on high-frequency channels 89 and 166 GHz of the radiometer onboard the Global Precipitation Measurement (GPM) core satellite, which are commonly used to capture snowfall scattering signals. Integrating the inference across all feasible grain sizes, ranges of snowpack density and depth are identified over which snowfall scattering signatures can be time-varying and potentially obscured. Using ten years of reanalysis data, the seasonal vulnerability of snowfall retrievals to the changes in snowpack emissivity in the Northern Hemisphere is mapped in a probabilistic sense and connections are made with the uncertainties of the GPM passive microwave snowfall retrievals. It is found that among different snow classes, relatively light Arctic tundra snow in fall, with a density below 260 kg m(-3), and shallow prairie snow during the winter, with a depth of less than 40 cm, can reduce the surface emissivity and obscure the snowfall passive microwave signatures. It is demonstrated that during winter, the highly vulnerable areas are over Kazakhstan and Mongolia with taiga and prairie snow. In the fall, these areas are largely over tundra and taiga snow in north of Russia and the Arctic Archipelagos as well as prairies in Canada and the Great Plains in the United States.

We present the first box model simulation results aimed at identification of possible effects of the atmospheric photochemical evolution of the organic component of biomass burning (BB) aerosol on the aerosol radiative forcing (ARF) and its efficiency (ARFE). The simulations of the dynamics of the optical characteristics of the organic aerosol (OA) were performed using a simple parameterization developed within the volatility basis set framework and adapted to simulate the multiday BB aerosol evolution in idealized isolated smoke plumes from Siberian fires (without dilution). Our results indicate that the aerosol optical depth can be used as a good proxy for studying the effect of the OA evolution on the ARF, but variations in the scattering and absorbing properties of BB aerosol can also affect its radiative effects, as evidenced by variations in the ARFE. Changes in the single scattering albedo (SSA) and asymmetry factor, which occur as a result of the BB OA photochemical evolution, may either reduce or enhance the ARFE as a result of their competing effects, depending on the initial concentration OA, the ratio of black carbon to OA mass concentrations and the aerosol photochemical age in a complex way. Our simulation results also reveal that (1) the ARFE at the top of the atmosphere is not significantly affected by the OA oxidation processes compared to the ARFE at the bottom of the atmosphere, and (2) the dependence of ARFE in the atmospheric column and on the BB aerosol photochemical ages almost mirrors the corresponding dependence of SSA.

Using a balance model of the snow layer, we estimate the concentration of black carbon (BC) in the snow; then, with the help of radiative transfer model SNICAR, we calculate the snow albedo and radiative forcing (RF) from snow darkening with BC. Data from an ensemble simulation with INMCM5, the 5th version of the climate model of Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences for the period 1998-2002 are used as input, which include snow both on land and on sea ice. The regionally averaged results are compared with other model data and field measurements.