Shengbiao Wu

Extracting Leaf Area Index by Sunlit Foliage Component from Downward-Looking Digital Photography under Clear-Sky Conditions

The development of near-surface remote sensing requires the accurate extraction of leaf area index (LAI) from networked digital cameras under all illumination conditions. The widely used directional gap fraction model is more suitable for overcast conditions due to the difficulty to discriminate the shaded foliage from the shadowed parts of images acquired on sunny days. In this study, a new LAI extraction method by the sunlit foliage component from downward-looking digital photography under clear-sky conditions is proposed. In this method, the sunlit foliage component was extracted by an automated image classification algorithm named LAB2, the clumping index was estimated by a path length distribution-based method, the LAD and G function were quantified by leveled digital images and, eventually, the LAI was obtained by introducing a geometric-optical (GO) model which can quantify the sunlit foliage proportion. The proposed method was evaluated at the YJP site, Canada, by the 3D realistic structural scene constructed based on the field measurements. Results suggest that the LAB2 algorithm makes it possible for the automated image processing and the accurate sunlit foliage extraction with the minimum overall accuracy of 91.4%. The widely-used finite-length method tends to underestimate the clumping index, while the path length distribution-based method can reduce the relative error (RE) from 7.8% to 6.6%. Using the directional gap fraction model under sunny conditions can lead to an underestimation of LAI by (1.61; 55.9%), which was significantly outside the accuracy requirement (0.5; 20%) by the Global Climate Observation System (GCOS). The proposed LAI extraction method has an RMSE of 0.35 and an RE of 11.4% under sunny conditions, which can meet the accuracy requirement of the GCOS. This method relaxes the required diffuse illumination conditions for the digital photography, and can be applied to extract LAI from downward-looking webcam images, which is expected for the regional to continental scale monitoring of vegetation dynamics and validation of satellite remote sensing products.

Characterizing Land Surface Anisotropic Reflectance over Rugged Terrain: A Review of Concepts and Recent Developments

Rugged terrain, including mountains, hills, and some high lands are typical land surfaces around the world. As a physical parameter for characterizing the anisotropic reflectance of the land surface, the importance of the bidirectional reflectance distribution function (BRDF) has been gradually recognized in the remote sensing community, and great efforts have been dedicated to build BRDF models over various terrain types. However, on rugged terrain, the topography intensely affects the shape and magnitude of the BRDF and creates challenges in modeling the BRDF. In this paper, after a brief introduction of the theoretical background of the BRDF over rugged terrain, the status of estimating land surface BRDF properties over rugged terrain is comprehensively reviewed from a historical perspective and summarized in two categories: BRDFs describing solo slopes and those describing composite slopes. The discussion focuses on land surface reflectance retrieval over mountainous areas, the difference in solo slope and composite slope BRDF models, and suggested future research to improve the accuracy of BRDFs derived with remote sensing satellites.

Simulation and Analysis of the Topographic Effects on Snow-Free Albedo over Rugged Terrain

Topography complicates the modeling and retrieval of land surface albedo due to shadow effects and the redistribution of incident radiation. Neglecting topographic effects may lead to a significant bias when estimating land surface albedo over a single slope. However, for rugged terrain, a comprehensive and systematic investigation of topographic effects on land surface albedo is currently ongoing. Accurately estimating topographic effects on land surface albedo over a rugged terrain presents a challenge in remote sensing modeling and applications. In this paper, we focused on the development of a simplified estimation method for snow-free albedo over a rugged terrain at a 1-km scale based on a 30-m fine-scale digital elevation model (DEM). The proposed method was compared with the radiosity approach based on simulated and real DEMs. The results of the comparison showed that the proposed method provided adequate computational efficiency and satisfactory accuracy simultaneously. Then, the topographic effects on snow-free albedo were quantitatively investigated and interpreted by considering the mean slope, subpixel aspect distribution, solar zenith angle, and solar azimuth angle. The results showed that the more rugged the terrain and the larger the solar illumination angle, the more intense the topographic effects were on black-sky albedo (BSA). The maximum absolute deviation (MAD) and the maximum relative deviation (MRD) of the BSA over a rugged terrain reached 0.28 and 85%, respectively, when the SZA was 60° for different terrains. Topographic effects varied with the mean slope, subpixel aspect distribution, SZA and SAA, which should not be neglected when modeling albedo.

A Multi-Scale Validation Strategy for Albedo Products over Rugged Terrain and Preliminary Application in Heihe River Basin, China

The issue for the validation of land surface remote sensing albedo products over rugged terrain is the scale effects between the reference albedo measurements and coarse scale albedo products, which is caused by the complex topography. This paper illustrates a multi-scale validation strategy specified for coarse scale albedo validation over rugged terrain. A Mountain-Radiation-Transfer-based (MRT-based) albedo upscaling model was proposed in the process of multi-scale validation strategy for aggregating fine scale albedo to coarse scale. The simulated data of both the reference coarse scale albedo and fine scale albedo were used to assess the performance and uncertainties of the MRT-based albedo upscaling model. The results showed that the MRT-based model could reflect the albedo scale effects over rugged terrain and provided a robust solution for albedo upscaling from fine scale to coarse scale with different mean slopes and different solar zenith angles. The upscaled coarse scale albedos had the great agreements with the simulated coarse scale albedo with a Root-Mean-Square-Error (RMSE) of 0.0029 and 0.0017 for black sky albedo (BSA) and white sky albedo (WSA), respectively. Then the MRT-based model was preliminarily applied for the assessment of daily MODerate Resolution Imaging Spectroradiometer (MODIS) Albedo Collection V006 products (MCD43A3 C6) over rugged terrain. Results showed that the MRT-based model was effective and suitable for conducting the validation of MODIS albedo products over rugged terrain. In this research area, it was shown that the MCD43A3 C6 products with full inversion algorithm, were generally in agreement with the aggregated coarse scale reference albedos over rugged terrain in the Heihe River Basin, with the BSA RMSE of 0.0305 and WSA RMSE of 0.0321, respectively, which were slightly higher than those over flat terrain.

An Iterative BRDF/NDVI Inversion Algorithm Based on A Posteriori Variance Estimation of Observation Errors

Current bidirectional reflectance distribution function (BRDF) inversions using ordinary least squares (OLS) criterion can be easily contaminated by observations with residual cloud and undetected high aerosols, which leads to abrupt fluctuations in the normalized difference vegetation index (NDVI) time series. The OLS criterion assumes the noise has Gaussian distribution, which is often violated due to positive noise biases caused by clouds and high aerosols. A changing-weight iterative BRDF/NDVI inversion algorithm (CWI) based on a posteriori variance estimation of observation errors is presented to explicitly consider the asymmetrically distributed noise and observations with unequal accuracy in the BRDF retrieval. CWI employs a posteriori variance estimation and an NDVI-based indicator to iteratively adjust the weight of each observation according to its noise level. The validation results suggest CWI performs better than the Li-Gao and OLS approaches. The rmse was reduced from 0.074 to 0.028, and the relative error decreased from 13.4% to 3.8% at the U.S. Department of Agriculture Beltsville Agricultural Research Center site. Similarly, at the Harvard Forest site, the rmse was reduced from 0.086 to 0.031, and the relative error decreased from 9.5% to 2.7%. The average noise and relative noise of the CWI NDVI time series over ten EOS Land Validation Core Sites from 2003-2009 was smaller (0.028, 3.7%) than those of MOD13A2 (0.041, 5.2%), MYD13A2 (0.039, 4.9%) and MCD43B4 (0.030, 4.4%). The results demonstrate the robustness of the CWI approach in suppressing the influence of contaminated observations in BRDF retrievals by producing results that are less affected by undetected clouds and high aerosols.

Spectral Invariant Provides a Practical Modeling Approach for Future Biophysical Variable Estimations

This paper presents a simple radiative transfer model based on spectral invariant properties (SIP). The canopy structure parameters, including the leaf angle distribution and multi-angular clumping index, are explicitly described in the SIP model. The SIP model has been evaluated on its bidirectional reflectance factor (BRF) in the angular space at the radiation transfer model intercomparison platform, and in the spectrum space by the PROSPECT+SAIL (PROSAIL) model. The simulations of BRF by SIP agreed well with the reference values in both the angular space and spectrum space, with a root-mean-square-error (RMSE) of 0.006. When compared with the widely-used Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model on fPAR, the RMSE was 0.006 and the R-2 was 0.99, which shows a high accuracy. This study also suggests the newly proposed vegetation index, the near-infrared (NIR) reflectance of vegetation (NIRv), was a good linear approximation of the canopy structure parameter, the directional area scattering factor (DASF), with an R-2 of 0.99. NIRv was not influenced much by the soil background contribution, but was sensitive to the leaf inclination angle. The sensitivity of NIRv to canopy structure and the robustness of NIRv to the soil background suggest NIRv is a promising index in future biophysical variable estimations with the support of the SIP model, especially for the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) observations near the hot spot directions.

Modeling anisotropic bidirectional reflectance of sloping forest

A well understanding of topography effect on the forest reflectance is critical for biophysical parameters retrieval over rugged area. In this paper, a new hybrid bidirectional reflectance distribution function (BRDF) model coupled the geometric optical mutual shadowing (GOMS) and scattering from arbitrarily inclined leaves (SAIL) models with topography consideration (GOSAILT) for sloping forest was proposed.

GOFP: A Geometric-Optical Model for Forest Plantations

Geometric-optical (GO) model suitable for forest plantation (GOFP) is a GO model for forest plantations at the stand level developed in this study based on a four-scale GO model a Geometric-Optical Model for Sloping Terrains-II (GOST2), which simulates the bidirectional reflectance distribution function (BRDF) for natural forest canopies. In most previous GO models, tree distributions are often assumed to meet the Poisson or Neyman model in a forest; therefore, these models are suitable for simulating BRDF for natural forest canopies. However, in forest plantations, tree distributions are proven to meet the hypergeometric model rather than the Poisson or Neyman model at the stand level. GOFP, in which the tree distributions are described using the hypergeometric model, is proposed to simulate the bidirectional reflectance factor (BRF) of forest plantations at the stand level. The area ratios of the four scene components (sunlit foliage, sunlit ground, shaded foliage, and shaded ground) of GOFP compare well with those simulated by a 3-D canopy visualization technique. A comparison is also made against discrete anisotropic radiative transfer, showing that GOFP has the ability to simulate BRF of forest plantations. Another comparison is made against operational land imager and Moderate Resolution Imaging Spectroradiometer surface reflectance, showing that GOFP with the hypergeometric model outperforms GOST2 with the Poisson and Neyman models. The results further show that the differences in BRFs between GOFP and GOST2 pronouncedly increase with the difference between the reflectance of sunlit foliage (

Modeling Canopy Reflectance Over Sloping Terrain Based on Path Length Correction

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PLC: A simple and semi-physical topographic correction method for vegetation canopies based on path length correction

) and the reflectance of sunlit ground (