Canopy reflectance (CR) models describe the transfer and interaction of radiation from the soil background to the canopy layer and play a vital role in the retrieval of biophysical variables. However, few efforts have focused on estimating soil background scattering operators, resulting in uncertainties in CR modelling, especially over sloping terrain. This study developed a canopy reflectance model for simulating CR over sloping terrain, which combines the general spectral vector (GSV) model, the PROSPECT model, and 4SAIL model coupled with topography (GSV-PROSAILT). The canopy reflectance simulated by GSV-PROSAILT was validated against two datasets: discrete anisotropic radiative transfer (DART) simulations and remote sensing observations. A comparison with DART simulations under various conditions revealed that the GSV-PROSAILT model captures terrain-induced CR distortion with high accuracy (red band: coefficient of determination $\lpar {\rm R 2} \rpar = 0.731$(R2)=0.731, root-mean-square error (RMSE) = 0.007; near infrared (NIR) band: $\rm R2 = 0.8319$R2=0.8319, RMSE = 0.0098). The results of remote sensing observation verification revealed that the GSV-PROSAILT model can be successfully used in CR modelling. These validations confirmed the performance of GSV-PROSAILT in soil and canopy reflectance modelling over sloping terrain, indicating that it can provide a potential tool for biophysical variable retrieval over mountainous areas.
In recent years, increasing wildfire activity in the western United States has led to significant emissions of smoke aerosols, impacting the atmospheric energy balance through their absorption and scattering properties. Single scattering albedo (SSA) is a key parameter that governs these radiative effects, but accurately retrieving SSA from satellites remains challenging due to limitations in sensor resolution, low sensitivity of traditional remote sensing methods, and uncertainties in radiative transfer modeling, particularly from surface reflectance and aerosol characterization. Smoke optical properties evolve rapidly after emission, influenced by fuel type, combustion conditions, and chemical aging. Accurate SSA retrieval near the source thus requires high-temporal-resolution satellite observations. Critical Reflectance (CR) method provides this capability by identifying a unique reflectance value at which top-of-atmosphere (TOA) reflectance becomes insensitive to aerosol loading and primarily reflects aerosol absorption. SSA can be retrieved from this critical reflectance. This study presents a geostationary-based CR method using the Advanced Baseline Imager (ABI) on GOES-R satellites. The approach leverages ABI's high temporal (5-10 min) and spatial (3 km) resolution, consistent viewing geometry, and wide coverage. A tailored look-up table, based on an AOD-dependent smoke model for North America, links CR to SSA. Case studies show strong agreement with AERONET measurements, with retrieval differences mostly within 0.01-well below AERONET's +/- 0.03 uncertainty. The method captures temporal and spatial variations in smoke absorption and demonstrates robustness across daylight hours. This GEO-based CR approach offers an effective tool for high-resolution SSA retrieval, contributing to improved aerosol radiative forcing estimates and climate modeling.
Corn is a vital global crop, yet its cultivation demands extensive agrochemical inputs, prompting the need for sustainable alternatives. This study investigates the impact of vermicompost (VC) and vermicompost tea (VCT) applications on corn growth, physiology, and resistance to Fall Armyworm (FAW) infestation using advanced optical plant sensors. Six treatments were employed: V0 (control), VC1, VCT100, VC1 + VCT50, VC3, and VC3 + VCT50. During the growing season, plant growth parameters, such as height, chlorophyll content, and spectral reflectance were measured using a chlorophyll meter, fluorometer, porometer, and spectroradiometer. Results indicated that VC-treated plants exhibited superior growth and higher chlorophyll content than control or untreated plants. The VC1 + VCT50-treated plants showed robust resistance to FAW, with no infestation throughout the season, while VC1-treated plants showed delayed attack by FAW. Soil chemical analysis showed that VC and VCT treatments had similar nutrient concentrations as the control. Plant nutrient content was higher in VCT100 compared to all treatments. These findings suggest that the combined application of VC and VCT, particularly at specific application rates, can enhance corn plant health, mitigate pest damage, and optimize yield potential.
Snow algae darken the surface of snow, reducing albedo and accelerating melt. However, the impact of subsurface snow algae (e.g., when cells are covered by recent snowfall) on albedo is unknown. Here, we examined the impact of subsurface snow algae on surface energy absorption by adding up to 2 cm of clean snow to surface algal blooms and measuring reflectivity. Surprisingly, snow algae still absorb significant energy across an array of wavelengths when snow-covered. Furthermore, the scale of this effect correlates with algal cell densities and chlorophyll-a concentrations. Collectively, our results suggest that darkening by subsurface snow algae lowers albedo and thus potentially accelerates snowmelt even when the algae is snow-covered. Impacts of subsurface algae on melt await assessment. This implies that snow algae play a larger role in cryosphere melt than investigations of surface-only reflectance would suggest. IMPORTANCE This study addresses a gap in research by examining the impact of subsurface snow algae on snow albedo, which affects snowmelt rates. Previous studies have focused on visible surface blooms, leaving the effects of hidden algae unquantified. Our findings reveal that snow algae beneath the surface can still absorb energy across various wavelengths, accelerating melt even when not visible to the naked eye. This suggests that spectral remote sensing can detect these hidden algae, although their biomass might be underestimated. Understanding how subsurface snow algae influence albedo and snowmelt is crucial for accurate predictions of meltwater runoff, which impacts alpine ecosystems, glacier health, and water resources. Accurate projections are essential for managing freshwater supplies for agriculture, drinking water, and other vital uses. Thus, further investigation into subsurface snow algae is necessary to improve our understanding of their role in snow albedo reduction and water resource management.
Monitoring surface albedo at a fine spatial resolution in forests can enrich process understanding and benefit ecosystem modeling and climate-oriented forest management. Direct estimation of surface albedo using 10 m reflectance imagery from Sentinel-2 is a promising research avenue to this extent, although questions remain regarding the representativeness of the underlying model of surface reflectance anisotropy originating from coarser-resolution imagery (e.g., MODIS). Here, using Fennoscandia (Norway, Sweden, Finland) as a case region, we test the hypothesis that systematic stratification of the forested landscape into similar species compositions and physical structures prior to the step of carrying out angular bin regressions can lead to improved albedo estimation accuracy of direct estimation algorithms. We find that such stratification does not lead to statistically meaningful improvement over stratification based on conventional land cover classification, suggesting that factors other than forest structure (e.g., soils, understory vegetation) may be equally important in explaining within-forest variations in surface reflectance anisotropy. Nevertheless, for Sentinel-2-based direct estimation based on conventional forest classification, we document total-sky surface albedo errors (RMSE) during snowfree and snow-covered conditions of 0.015 (15 %) and 0.037 (21 %), respectively, which align with those of the coarser spatial resolution products in current operation.
A critical comprehension of the impact of snow cover on urban bidirectional reflectance is pivotal for precise assessments of energy budgets, radiative forcing, and urban climate change. This study develops a numerical model that employs the Monte Carlo ray-tracing technique and a snow anisotropic reflectance model (ART) to simulate spectral albedo and bidirectional reflectance, accounting for urban structure and snow anisotropy. Validation using three flat surfaces and MODIS data (snow-free, fresh snow, and melting snow scenarios) revealed minimal errors: the maximum domain-averaged BRDF bias was 0.01% for flat surfaces, and the overall model-MODIS deviation was less than 0.05. The model's performance confirmed its accuracy in reproducing the reflectance spectrum. A thorough investigation of key factors affecting bidirectional reflectance in snow-covered urban canyons ensued, with snow coverage found to be the dominant influence. Urban coverage, building height, and soot pollutant concentration significantly impact visible and infrared reflectance, while snow grain size has the greatest effect on shortwave infrared. The bidirectional reflectance at backward scattering angles (0.5-0.6) at 645 nm is lower than forward scattering (around 0.8) in the principal plane as snow grain size increases. These findings contribute to a deeper understanding of snow-covered urban canyons' reflectance characteristics and facilitate the quantification of radiation interactions, cloud-snow discrimination, and satellite-based retrieval of aerosol and snow parameters.
The non-Lambertian surface features varying particle size and discrete distribution, resulting in reflectance to be unevenly distributed in different directions. Mine soil with a high content of coarse particles and non-uniform particle distribution exhibits significant non-Lambertian properties on its surface. Consequently, not only vertical observation of the reflectance spectra but also multi-angle reflectance spectra are related to the physical and chemical properties (e.g. soil organic carbon, moisture content and particle size) of mine soil. Understanding the bidirectional reflectance distribution of mine soil with various particle sizes is essential for accurately estimating soil properties using spectroscopy. Current estimations of soil properties using spectroscopy mainly focus on vertical observations, overlooking the bidirectional reflectance characteristics. This study reports the bidirectional reflectance distribution of mine soil with various particle sizes. Furthermore, the performance of different bidirectional reflectance distribution function (BRDF) models in simulating the bidirectional reflectance of mine soil with various particle sizes was evaluated. Soil samples from three typical mine areas were collected and sieved into seven particle sizes ranging from 25 to 3500 mu m. The bidirectional reflectance in the Vis-NIR wavelength region was measured in a laboratory using the Northeastern University bidirectional reflectance measurement system. The performance of five BRDF models (isotropic multiple scattering approximation, anisotropic multiple scattering approximation, H2008, H2012 and SOILSPECT) in modelling the bidirectional reflectance distribution of mine soil with different particle sizes was compared. Sobol's sensitivity indices were used to quantify the contributions of the parameters in the BRDF models. The results showed that (1) small mine soil particles (25 mu m) exhibited greater reflectance than large particles (3500 mu m). Large particles (3500 mu m) exhibited backward scattering, whereas small particles (25 mu m) exhibited extremely forward scattering characteristics because of the high silicon dioxide content; (2) the SOILSPECT model outperformed the other BRDF models in simulating the bidirectional reflectance of mine soil and had the smallest root mean square error (0.004-0.04); (3) the single-scattering albedo (omega) parameter had the greatest contribution in the SOILSPECT model. Four parameters in the phase function (b, b ', c and c ') effectively indicated the scattering behaviour of mine soil with different particle sizes. These findings improve our understanding of the scattering characteristics of mine soil with various particle sizes and can be used to improve the accuracy of extracting particle size and other soil properties from mine soil.