Lizhao Wang

Direct-Estimation Algorithm for Mapping Daily Land-Surface Broadband Albedo From MODIS Data

Land surface albedo is a critical parameter in surface-energy budget studies. Over the past several decades, many albedo products are generated from remote-sensing data sets. The Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/Albedo algorithm is used to routinely produce eight day (16-day composite), 1-km resolution MODIS albedo products. When some natural processes or human activities occur, the land-surface broadband albedo can change rapidly, so it is necessary to enhance the temporal resolution of albedo product. We present a direct-estimation algorithm for mapping daily land-surface broadband albedo from MODIS data. The polarization and directionality of the Earths reflectance-3/polarization and anisotropy of reflectances for atmospheric sciences coupled with observations from a Lidar BRDF database is employed as a training data set, and the 6S atmospheric radiative transfer code is used to simulate the top-of-atmosphere (TOA) reflectances. Then a relationship between TOA reflectances and land-surface broadband albedos is developed using an angular bin regression method. The robustness of this method for different angular bins, aerosol conditions, and land-cover types is analyzed. Simulation results show that the absolute error of this algorithm is ~ 0.009 for vegetation, 0.012 for soil, and 0.030 for snow/ice. Validation of the direct-estimation algorithm against in situ measurement data shows that the proposed method is capable of characterizing the temporal variation of albedo, especially when the land-surface BRDF changes rapidly.

Multi-scale validation strategy for satellite albedo products and its uncertainty analysis

Coarse-resolution satellite albedo products are important for climate change and energy balance research because of their capability to characterize the spatiotemporal patterns of land surface parameters at both the regional and global scales. The accuracy of coarse-resolution products is usually assessed via comparison with in situ measurements. The key issue in the comparison of remote sensing observations with in situ measurements is scaling and uncertainty. This paper presents a strategy for validating 1-km-resolution remote sensing albedo products using field measurements and high-resolution remote sensing observations. Field measurements were collected to calibrate the high-resolution (30 m) albedo products derived from HJ-1a/b images. Then, the calibrated high-resolution albedo maps were resampled (i.e., upscaled) to assess the accuracy of the coarse-resolution albedo products. The samples of field measurements and high-resolution pixels are based on an uncertainty analysis. Two types of coarse-resolution albedo datasets, from global land surface satellite (GLASS) and moderate-resolution imaging spectroradiometer (MODIS), are validated over the middle reaches of the Heihe River in China. The results indicate that the upscaled HJ (Huan Jing means environment in Chinese and this refers to a satellite constellation designed for environment and disaster monitoring by China) albedo, which was calibrated using field measurements, can provide accurate reference values for validating coarse-resolution satellite albedo products. However, the uncertainties in the upscaled HJ albedo should be estimated, and pixels with large uncertainties should be excluded from the validation process.

A temporal filtering algorithm to reconstruct daily albedo series based on GLASS albedo product

GLASS albedo is a newly developed global daily land-surface broadband albedo product with 1-km spatial resolution. There are two main deficiencies in GLASS albedo products: 1) large areas of missing data mainly caused by cloud coverage; 2) sharp fluctuations in time series due to noise and uncertainties in inversion algorithm. This paper proposed a temporal filtering algorithm to reconstruct daily albedo series from GLASS albedo products. Validation results show that this algorithm can fill data gaps and smooth albedo series effectively.

Characterizing the Pixel Footprint of Satellite Albedo Products Derived from MODIS Reflectance in the Heihe River Basin, China

The adjacency effect and non-uniform responses complicate the precise delimitation of the surface support of remote sensing data and their derived products. Thus, modeling spatial response characteristics (SRCs) prior to using remote sensing information has become important. A point spread function (PSF) is typically used to describe the SRCs of the observation cells from remote sensors and is always estimated in a laboratory before the sensor is launched. However, research on the SRCs of high-order remote sensing products derived from the observations remains insufficient, which is an obstacle to converting between multi-scale remote sensing products and validating coarse-resolution products. This study proposed a method that combines simulation and validation to establish SRC models of coarse-resolution albedo products. Two series of commonly used 500-m/1-km resolution albedo products, which are derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data, were investigated using 30-m albedo products that provide the required sub-pixel information. The analysis proves that the size of the surface support of each albedo pixel is larger than the nominal resolution of the pixel and that the response weight is non-uniformly distributed, with an elliptical Gaussian shape. The proposed methodology is generic and applicable for analyzing the SRCs of other advanced remote sensing products.

Estimating Crop Albedo in the Application of a Physical Model Based on the Law of Energy Conservation and Spectral Invariants

Albedo characterizes the radiometric interface of land surfaces, especially vegetation, and the atmosphere. Albedo is a critical input to many models, such as crop growth models, hydrological models and climate models. For the extensive attention to crop monitoring, a physical albedo model for crops is developed based on the law of energy conservation and spectral invariants, which is derived from a prior forest albedo model. The model inputs have been efficiently and physically parameterized, including the dependency of albedo on the solar zenith/azimuth angle, the fraction of diffuse skylight in the incident radiance, the canopy structure, the leaf reflectance/transmittance and the soil reflectance characteristics. Both the anisotropy of soil reflectance and the clumping effect of crop leaves at the canopy scale are considered, which contribute to the improvement of the model accuracy. The comparison between the model results and Monte Carlo simulation results indicates that the canopy albedo has high accuracy with an RMSE < 0.005. The validation using ground measurements has also demonstrated the reliability of the model and that it can reflect the interaction mechanism between radiation and the canopy-soil system.

Modeling the Directional Clumping Index of Crop and Forest

The Clumping Index (Ω) was introduced to quantify the spatial distribution pattern of vegetation elements. It is crucial to improve the estimation accuracy of vital vegetation parameters, such as Leaf Area Index (LAI) and Gross Primary Production (GPP). Meanwhile, the parameterization of Ω is challenging partly due to the varying observations of canopy gaps from different view angles. Many previous studies have shown the increase of Ω with view zenith angle through samples of gap size distribution from in situ measurements. In contrast, remote sensing retrieval algorithms only assign a constant value for each biome type to roughly correct the clumping effect as a compromise between the accuracy and efficiency. In this paper, analytical models are proposed that estimate the directional clumping index (Ω(θ)) of crop and forest at canopy level. The angular variation trend and magnitude of crop Ω(θ) was analyzed within row structure where vegetation elements are randomly spaced along rows. The forest model predicts Ω(θ) with tree density, distribution pattern, crown shape, trunk size, and leaf area and angle distribution function. The models take into account the main directional characteristics of clumping index using easy-to-measure parameters. Test cases showed that Ω(θ) magnitude variation for black spruce forest was 102.3% of the hemispherical average clumping index (Ω̃), whereas the Larch forest had 48.7% variation, and row crop variation reached 32.4%. This study provided tools to assess Ω(θ) of discontinuous canopies.

Modeling and validation of the angular clumping index of forest canopy

The clumping index (CI) is an important factor for deriving forest leaf area index (LAI) at both pixel and point scale. Due to the non-uniform distribution of leaves within trees and random layout of trees under natural conditions, the CI varies with view zenith angle (VZA) as well as the gap fraction. The variation tendency of gap fraction and CI with VZA dominates the potential uncertainty of deriving LAI from directional observations. In this research, the continuous change of gap fraction and CI with VZA was simulated under virtual tree scenarios. Meanwhile, the directional CI was calculated using an algebraic model developed based on previous researches on gap fraction. Both results were validated using ground measurements. The CI discrepancy distributes within ±0.06 coming from the random number distribution restriction and the influence from tree trunk. The analysis using the algebraic model shows that the forest CI reaches an asymptotic level after 55° for forest canopy dense enough. This is an interesting and important phenomenon, which reveal a stable feature of CI to be further used to extract forest information.

Crop specified albedo model based on the law of energy conservation and spectral invariants

A physical model for simulating crop albedo based on the law of energy conservation and spectral invariants was developed. The idea referred to the primary PARAS albedo model for forest. The developed model concerned two key problems for crop monitoring, one is the anisotropy of soil reflectance and the other is the clumping effect of crop leaves at canopy scale, which contributed to the improvement of the model accuracy. The comparison between the model results and those simulated using a Monte Carlo method shows that the calculated clumping index, soil absorptance and canopy albedo all have high accuracy, indicating that the model is able to reflect the interaction mechanism between radiation and the canopy-soil system.

Spatial distribution of Crofton weeds in southwest China with MODIS NDVI of long time series

Crofton weed, which was introduced to the southwest of China in the 1940s via the border between China and Burma, now has a seriously damaging effect on the biodiversity and the living environment in those regions, including Yunnan, Sichuan, Guizhou, Guangxi, Tibet, Chongqing, and Hubei. To help monitor this situation effectively, there have been many researches that utilize remote sensing images to identify the spread of Crofton weed. However, most of these researches found it difficult to identify Crofton weed because it is mixed and entangled with a large number of other vegetation. In our research, data processing includes three steps: 1) Retrieving the spectral feature values of Crofton weed at the different growth stages through long time regional sampling; 2) Comparing the NDVI of Crofton weeds, forests, crops, water bodies, residential areas in the typical sampling regions by using the time series data of remote sensing images; and finally, 3) Classifying the remote sensing images of the research regions according to the distinctive features of NDVI curves based on the time series data. The outcome of following these three data processing steps is a relatively accurate classification result, which produces a high-accuracy distribution map of the Crofton weed.

Assessment of harm degree of Crofton weeds invasion in southwest China with NDVI

The MOD13Q1 product from 2001 to 2007 has been utilized to generate NDVI background data of Yunnan-Guizhou plateau, then the vector composed of annually continuous NDVI values of each pixel was compared with the average value vector of pure pixels covering Crofton weeds and the similarity between each pair of vectors was calculated using SAM algorithm. Based on that, a normalized index to assess the harm degree of Crofton weeds was defined, which was named HCWI (Harm of Crofton Weeds Index). To evaluate the effectively of HCWI, its calculated results at some field investigation points were verified with the field investigation records of Crofton weeds. The results have proved the reliability of HCWI in the assessment of growth density and involving harm degree of Crofton weeds.