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.

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.

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.

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.

The broadband surface albedo of snow can greatly be reduced by the deposition of light-absorbing impurities, such as black carbon on or near its surface. Such a reduction increases the absorption of solar radiation and may initiate or accelerate snowmelt and snow albedo feedback. Coincident measurements of both black carbon concentration and broadband snow albedo may be difficult to obtain in field studies; however, using the relationship developed in this simple model sensitivity study, black carbon mass densities deposited can be estimated from changes in measured broadband snow albedo, and vice versa. Here, the relationship between the areal mass density of black carbon found near the snow surface to the amount of albedo reduction was investigated using the popular snow radiative transfer model Snow, Ice, and Aerosol Radiation (SNICAR). We found this relationship to be linear for realistic amounts of black carbon mass concentrations, such as those found in snow at remote locations. We applied this relationship to measurements of broadband albedo in the Chilean Andes to estimate how vehicular emissions contributed to black carbon (BC) deposition that was previously unquantified.

High-latitude areas are very sensitive to global warming, which has significant impacts on soil temperatures and associated processes governing permafrost evolution. This study aims to improve first-layer soil temperature retrievals during winter. This key surface state variable is strongly affected by snow's geophysical properties and their associated uncertainties (e.g., thermal conductivity) in land surface climate models. We used infrared MODIS land-surface temperatures (LST) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) brightness temperatures (Tb) at 10.7 and 18.7 GHz to constrain the Canadian Land Surface Scheme (CLASS), driven by meteorological reanalysis data and coupled with a simple radiative transfer model. The Tb polarization ratio (horizontal/vertical) at 10.7 GHz was selected to improve snowpack density, which is linked to the thermal conductivity representation in the model. Referencing meteorological station soil temperature measurements, we validated the approach at four different sites in the North American tundra over a period of up to 8 years. Results show that the proposed method improves simulations of the soil temperature under snow (Tg) by 64% when using remote sensing (RS) data to constrain the model, compared to model outputs without satellite data information. The root mean square error (RMSE) between measured and simulated Tg under the snow ranges from 1.8 to 3.5 K when using RS data. Improved temporal monitoring of the soil thermal state, along with changes in snow properties, will improve our understanding of the various processes governing soil biological, hydrological, and permafrost evolution.

The influence of Arctic vegetation on albedo, latent and sensible heat fluxes, and active layer thickness is a crucial link between boundary layer climate and permafrost in the context of climate change. Shrubs have been observed to lower the albedo as compared to lichen or graminoid-tundra. Despite its importance, the quantification of the effect of shrubification on summer albedo has not been addressed in much detail. We manipulated shrub density and height in an Arctic dwarf birch (Betula nana) shrub canopy to test the effect on shortwave radiative fluxes and on the normalized difference vegetation index (NDVI), a proxy for vegetation productivity used in satellite-based studies. Additionally, we parametrised and validated the 3D radiative transfer model DART to simulate the amount of solar radiation reflected and transmitted by an Arctic shrub canopy. We compared results of model runs of different complexities to measured data from North-East Siberia. We achieved comparably good results with simple turbid medium approaches, including both leaf and branch optical property media, and detailed object based model parameterisations. It was important to explicitly parameterise branches as they accounted for up to 71% of the total canopy absorption and thus contributed significantly to soil shading. Increasing leaf biomass resulted in a significant increase of the NDVI, decrease of transmitted photosynthetically active radiation, and repartitioning of the absorption of shortwave radiation by the canopy components. However, experimental and modelling results show that canopy broadband nadir reflectance and albedo are not significantly decreasing with increasing shrub biomass. We conclude that the leaf to branch ratio, canopy background, and vegetation type replaced by shrubs need to be considered when predicting feedbacks of shrubification to summer albedo, permafrost thaw, and climate warming. (C) 2014 Elsevier Inc. All rights reserved.

Aerosol radiative forcing (ARE) over intense mining area in Indian monsoon trough region, computed based on the aerosol optical properties obtained through Prede (POM-1L) sky radiometer and radiative transfer model, are analysed for the year 2011 based on 21 clear sky days spread through seasons. Due to active mining and varied minerals ARF is expected to be significantly modulated by single scattering albedo (SSA). Our studies show that radiative forcing normalized by aerosol optical depth (ADD) is highly correlated with SSA (0.96) while ARF at the surface with AOD by 0.92. Our results indicate that for a given AOD, limits or range of ARF are determined by SSA, hence endorses the need to obtain SSA accurately, preferably derived through observations concurrent with AOD. Noticeably, ARE at the top-of the atmosphere is well connected to SSA (r = 0.77) than AOD (r = 0.6). Relation between observed black carbon and SSA are investigated. A possible over estimation of SSA by the inversion algorithm, SKYRAD.pack 4.2, used in the current study is also discussed. Choice of atmospheric profiles deviating from tropical to mid altitude summer or winter does not appear to be sensitive in ARE calculation by SBDART. Based on the 21 clear sky days, a multiple linear regression equation is obtained for ARF(bot) as a function of AOD and SSA with a bias of +/- 2.7 Wm(-2). This equation is verified with an independent data set of seasonal mean AOD and SSA to calculate seasonal ARF that compares well with the modeled ARE within +/- 4 Wm(-2). (C) 2013 Elsevier Ltd. All rights reserved.