Ziti Jiao

Relationship of MISR RPV parameters and MODIS BRDF shape indicators to surface vegetation patterns in an Australian tropical savanna

The global coverage of bidirectional reflectance distribution function (BRDF) products from the Multi-angle Imaging Spectroradiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) has the potential to provide quantitative information on surface vegetation structure for input to process modelling and model–data assimilation schemes for regional and biome-scale assessment of carbon dynamics. The relationship of MISR Rahman–Pinty–Verstraete (RPV) model parameters, derived from inversion of MISR 275 m fine mode data, and BRDF shape indicators calculated from the latest MODIS 500 m MCD43 BRDF product to vegetation patterns in an Australian tropical savanna was examined for a time series covering the dry season period from April to October 2005. The bidirectional reflectance products were compared with geographical information system (GIS) data coverage combining floristic polygons with Landsat thematic mapper (TM) based estimates of canopy cover and height classes. The analysis showed that both the MISR RPV asymmetry parameter Θ and several MODIS BRDF shape indicators constructed using the red band were sensitive to local-scale anisotropic scattering and thus vegetation structure. The MISR RPV asymmetry parameter Θ showed consistent variation between grasslands, forest (closed canopies), and more open tree–grass mixtures over time. The MODIS indicators such as NDHD-R and ANIF-R produced distinctly different temporal profiles for major vegetation types such as rainforest, Melaleuca woodland, and Dichanthium grassland. These indices also showed evidence of consistent discrimination between eucalypt savanna types that varied in canopy cover and tree height. A clumping index calculated from NDHD-R for a single period (day 177 in a time series) showed good correspondence with savanna vegetation canopy properties but was insensitive to dense canopy rainforest vegetation. These results indicate there is potential for both MISR and MODIS BRDF products to provide a quantitative description of vegetation types in global tree–grass systems. However, there is a pressing need for further study to calibrate the responses with fine-scale structural data derived from both field measurement and light detection and ranging (lidar).

Improving MODIS land cover classification by combining MODIS spectral and angular signatures in a Canadian boreal forest

This study explores the use of reflectance anisotropy as described by the Bidirectional Reflectance Distribution Function (BRDF) as an additional source of information to improve land surface classification accuracies in a Canadian boreal forest region through the use of a decision tree classifier (C4.5). This effort primarily uses a daily rolling version of the operational algorithm developed for Direct Broadcast to generate 500 m 16-day daily rolling data sets in the study region. Descriptive statistic and statistically rigorous techniques are used to assess classification accuracies based on confusion matrices and a 10-fold cross-validation method. The results show that the inclusion of additional 7-band model anisotropic parameter group (volumetric (VOL) plus geometric (GEO)) with spectral feature group (nadir BRDF-adjusted reflectance (NABR) plus Enhanced Vegetation Index (EVI)) is most useful in classification, increasing overall accuracies by 5.68%. The most improvements of per-class accuracies are seen for Wetland shrub class with users and producers accuracies increasing by up to 17.7% and 11.3%, respectively. Increases on the order of 5% to 15% are encountered for the classes of Wetland herb, Wetland tree, Coniferous dense, and Coniferous open with no detriments to other candidate classes. The inclusion of the 2-band BRDF shape indicator group in the classification is, however, not as useful as inclusion of the 7-band model anisotropic parameter group in improving the classification accuracies. A further investigation of the classification accuracies regarding reflectance anisotropy for the sampling pixels within each class shows that land cover types that are dominated by geometric-optical scattering type or a mixture of scattering types are relatively difficult to be classified with spectral feature group alone, and the inclusion of additional BRDF features can significantly improve classification accuracies for these land cover types. However, despite their use as ancillary data, this study also confirms that the spectral feature group provided with NBAR and EVI captures the major information content regarding land cover types, exceeding the information content contained in the model anisotropic parameter group provided with the 7-band VOL and GEO parameters of RossThick-LiSparse-Reciprocal (RTLSR) models.

Evaluation of BRDF Archetypes for Representing Surface Reflectance Anisotropy Using MODIS BRDF Data

Bidirectional reflectance distribution function (BRDF) archetypes extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product over the global Earth Observing System Land Validation Core Sites can be used to simplify BRDF models. The present study attempts to evaluate the representativeness of BRDF archetypes for surface reflectance anisotropy. Five-year forward-modeled MODIS multi-angular reflectance (MCD-ref) and aditional actual MODIS multi-angular observations (MCD-obs) in four growing periods in 2008 over three tiles were taken as validation data. First, BRDF archetypes in the principal plane were qualitatively compared with the time-series MODIS BRDF product of randomly sampled pixels. Secondly, BRDF archetypes were used to fit MCD-ref, and the average root-mean-squared errors (RMSEs) over each tile were examined for these five years. Finally, both BRDF archetypes and the MODIS BRDF were used to fit MCD-obs, and the histograms of the fit-RMSEs were compared. The consistency of the directional reflectance between the BRDF archetypes and MODIS BRDFs in nadir-view, hotspot and entire viewing hemisphere at 30° and 50° solar geometries were also examined. The results confirm that BRDF archetypes are representative of surface reflectance anisotropy for available snow-free MODIS data.

An Algorithm for Retrieval of Surface Albedo From Small View-Angle Airborne Observations Through the Use of BRDF Archetypes as Prior Knowledge

Land surface albedo, qualifying the ratio of the radiant flux reflected from the land surface to the incident flux, is a key forcing parameter controlling the Earths energy budget. Previously, several BRDF archetypes were distilled from high-quality MODIS BRDF/Albedo products. In this study, we propose a method that largely relies on matching observed multiangular reflectances with the most appropriate of these prior BRDF archetypes to determine the amplitude and shape of the actual surface BRDFs, when directional signatures are insufficient. This method is first evaluated using an assortment of multisource BRDF data sets to demonstrate its viability for surface albedo estimates, and then is applied to airborne wide-angle infrared dual-mode line/area array scanner (WIDAS) from the Watershed Allied Telemetry Experimental Research (WATER) campaign in the Heihe River Basin of China in 2008. This algorithm makes use of the linear MODIS BRDF model to determine the BRDF archetypes needed as prior knowledge for intrinsic spectral albedo estimates. The intrinsic spectral albedos are then used to estimate actual spectral albedos by considering the proportion of direct and diffuse solar radiation. A spectral-to-broadband conversion is performed to generate the broadband albedo at shortwave regimes through the use of conversion coefficients derived from extensive radiative transfer simulations. A further validation confirms that the estimated albedos are consistent with in situ field measured albedos over available corn crop sites. This method provides a major advantage on utilizing generalized BRDF information derived from MODIS in conjunction with other instrument data that are acquired with less angular variation.

Variational retrieval of leaf area index from MODIS time series data: examples from the Heihe river basin, north-west China

Leaf area index (LAI) products retrieved from observations acquired on one occasion have obvious discontinuity in the time series owing to cloud coverage and other factors, and the accuracy may not meet the needs of many applications. Effectively utilizing data assimilation techniques to retrieve biophysical parameters from the time series of remote-sensing data has attracted special interest. The data assimilation technique is based on a reasonable consideration of dynamic change rules of biophysical parameters and time series observational quantities, thereby improving the quality of retrieved profiles. In this article, a variational assimilation procedure for retrieving LAI from the time series of remote-sensing data is developed. The procedure is based on the formulation of an objective function. A dynamic model is constructed based on the climatology from multi-year Moderate Resolution Imaging Spectroradiometer (MODIS) LAI data to evolve LAI in time, and a radiative transfer model is coupled with the dynamic model to simulate a time series of surface reflectances. A shuffled complex evolution method (developed at the University of Arizona; SCE-UA) optimization algorithm is then used to minimize the objective function and estimate the dynamic model states and the parameters of the coupled model from the MODIS reflectance data with a higher quality in a given time window. The variational assimilation method is applied to the MODIS surface reflectance data for the whole of 2008 at the Heihe river basin to produce regional LAI mapping results. The ground LAI data measured in situ are used to develop algorithms to estimate LAI from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) surface reflectance, and ASTER LAI maps are produced for each ASTER scene using the algorithms developed. Then the ASTER LAI maps are aggregated to compare with the new LAI products. It is found that the variational assimilation method is able to produce temporal continuous LAI products and that accuracy has been improved over the MODIS LAI standard product.

Effects of multiple view angles on the classification of forward-modeled MODIS reflectance

This paper examines the effects of multiple view angles on the classification of forward-modeled, high-quality, multispectral reflectances in a Canadian boreal forest region using a decision tree classifier (C4.5). Bidirectional reflectance factors (BRFs) from the seven-band moderate resolution imaging spectroradiometer (MODIS) are reproduced from high-quality composite model parameter datasets that were retrieved using a daily rolling version of an operational algorithm developed for direct broadcast and that were successfully used in earlier research. To assess the classification accuracies, we adopted descriptive and statistically rigorous techniques based on a confusion matrix and a 10-fold cross-validation method. The results show that the classification accuracies derived from the modeled MODIS BRFs in the principal plane are not substantially different, with the exception of a few directions, relative to bi-hemispherical reflectances (the white sky albedo) in the MODIS bidirectional reflectance distribution function (BRDF) Albedo product. The highest and lowest overall classification accuracies are those acquired by the seven-band Nadir BRDF-Adjusted Reflectances (approx. 77.745% ± 3.036) and the seven-band hotspot reflectances (approx. 72.18% ± 2.27). Analysis of per-class accuracies of eight land cover classes with different structures shows that the herb class and the broadleaf dense class have high per-class accuracies (mostly greater than 90%) from various view angles; whereas, other classes have relatively low per-class accuracies that tend to change with the view zenith angle and that are somewhat higher in the close-to-nadir and backward directions than in the forward scattering directions. Further investigation reveals that the classification accuracies derived from the reproduced MODIS BRFs are negatively correlated with the within-class variances of these BRF input features. Moreover, such correlations are higher in backward scattering directions (including the nadir direction) than in forward scattering directions. In summary, the effects of multiple view angles on the classification of MODIS BRFs reproduced from the MODIS BRDF model using a decision tree classifier (C4.5) are mainly related to anisotropic variance patterns of the BRFs in the principal plane.

Validation of MODIS albedo product by using field measurements and airborne multi-angular remote sensing observations

Albedo is a key parameter in monitoring the energy exchanges between the solar radiations and the land surfaces. The MODIS team generates the albedo products every 16 days. The products need to be validated by ground truths under different environmental conditions. In this study, we developed a 3-step validation procedure. The Ambrals (Algorithm for Modeling Bidirectional Reflectance Anisotropies of the Land Surface) model inversion was used to retrieve the albedo from the measured BRDF data over the winter wheat fields at the point/plot scale. And then, as our second step, the albedo values from the Airborne Multiangular Thermal-infrared Imaging System (AMTIS) over the same target area were estimated and validated using the ground point measurements. Finally, the retrieved albedo from airborne data were aggregated and compared with the MODIS albedo products. Our validation procedure has demonstrated a practical method to validate that albedo from spacebrone remotely sensed data (e.g., MODIS). The validation results show that the MODIS albedo products are reasonably good. Albedo is a key parameter in monitoring the energy exchanges of land surfaces. The hemispherical albedo is traditionally observed by albedometer at local meteorological stations, where the observing targets are usually grassland in a specific environment. Because some applications require albedo over a large area, retrieving regional and global albedo products from remote sensing observations can be more productive. The MODIS albedo products are from the multi-angular remote sensing (MARS) observations of every 16-days accumulation. The production needs to be validated by ground truths. One of the main problems in the validation is that the field-measured albedo is different in scale from the albedo retrieval using remote sensing data. The albedometer field measurement is over a small area, less than 1m 2 , while the spatial resolution of the MODIS albedo product is about 1 km. Another problem is associated with the different wavebands between the albedometer and the MODIS sensors. As a possible solution, we created a 3-steps validation procedure. As the first step, we used the BRDF data measured in the field to retrieve the albedo by Ambrals model inversion. The observing target was winter wheat. The retrieved albedo is comparable with that one measured by albedometer since both measurements are in the same observing scale. The effect of the wavebands difference was also corrected at this step. In the second step, we retrieved the albedo from the airborne MARS observation data of the same target. The spatial resolution is 1.36m at nadir. The retrieved albedo from airborne AMTIS BRDF data can be validated by using our field measurement. Finally, the retrieved albedo from airborne data was compared with the MODIS albedo product. Scaling-up needs to be considered in the comparison. In this work, the field measurements and airborne data came from the large satellite-airborne-ground synchronous experiment in the April of 2001. The experimental region is in the Shunyi county, 50km northeast of the Beijing City, China.

Analysis of Extracting Prior BRDF from MODIS BRDF Data

Many previous studies have attempted to extract prior reflectance anisotropy knowledge from the historical MODIS Bidirectional Reflectance Distribution Function (BRDF) product based on land cover or Normalized Difference Vegetation Index (NDVI) data. In this study, the feasibility of the method is discussed based on MODIS data and archetypal BRDFs. The BRDF is simplified into six archetypal BRDFs that represent different reflectance anisotropies. Five-year time series of MODIS BRDF data over three tiles are classified into six BRDF archetype classes according to the Anisotropy Flat indeX (AFX). The percentage of each BRDF archetype class in different land cover classes or every 0.1-NDVI interval is determined. Nadir BRDF-Adjusted Reflectances (NBARs) and NDVIs simulated from different archetypal BRDFs and the same multi-angular observations are compared to MODIS results to study the effectiveness of the method. The results show that one land cover type, or every 0.1-NDVI interval, contains all the potential BRDF shapes and that one BRDF archetypal class makes up no more than 40% of all data. Moreover, the differences between the NBARs and NDVIs simulated from different archetypal BRDFs are insignificant. In terms of the archetypal BRDF method and MODIS BRDF product, this study indicates that the land cover or NDVI is not necessarily related to surface reflectance anisotropy.

A visualization tool for the kernel-driven model with improved ability in data analysis and kernel assessment

Abstract The semi-empirical, kernel-driven Bidirectional Reflectance Distribution Function (BRDF) model has been widely used for many aspects of remote sensing. With the development of the kernel-driven model, there is a need to further assess the performance of newly developed kernels. The use of visualization tools can facilitate the analysis of model results and the assessment of newly developed kernels. However, the current version of the kernel-driven model does not contain a visualization function. In this study, a user-friendly visualization tool, named MaKeMAT, was developed specifically for the kernel-driven model. The POLDER-3 and CAR BRDF datasets were used to demonstrate the applicability of MaKeMAT. The visualization of inputted multi-angle measurements enhances understanding of multi-angle measurements and allows the choice of measurements with good representativeness. The visualization of modeling results facilitates the assessment of newly developed kernels. The study shows that the visualization tool MaKeMAT can promote the widespread application of the kernel-driven model.

Potential Investigation of Linking PROSAIL with the Ross-Li BRDF Model for Vegetation Characterization

Methods that link different models for investigating the retrieval of canopy biophysical/structural variables have been substantially adopted in the remote sensing community. To retrieve global biophysical parameters from multiangle data, the kernel-driven bidirectional reflectance distribution function (BRDF) model has been widely applied to satellite multiangle observations to model (interpolate/extrapolate) the bidirectional reflectance factor (BRF) in an arbitrary direction of viewing and solar geometries. Such modeled BRFs, as an essential information source, are then input into an inversion procedure that is devised through a large number of simulation analyses from some widely used physical models that can generalize such an inversion relationship between the BRFs (or their simple algebraic composite) and the biophysical/structural parameter. Therefore, evaluation of such a link between physical models and kernel-driven models contributes to the development of such inversion procedures to accurately retrieve vegetation properties, particularly based on the operational global BRDF parameters derived from satellite multiangle observations (e.g., MODIS). In this study, the main objective is to investigate the potential for linking a popular physical model (PROSAIL) with the widely used kernel-driven Ross-Li models. To do this, the BRFs and albedo are generated by the physical PROSAIL in a forward model, and then the simulated BRFs are input into the kernel-driven BRDF model for retrieval of the BRFs and albedo in the same viewing and solar geometries. To further strengthen such an investigation, a variety of field-measured multiangle reflectances have also been used to investigate the potential for linking these two models. For simulated BRFs generated by the PROSAIL model at 659 and 865 nm, the two models are generally comparable to each other, and the resultant root mean square errors (RMSEs) are 0.0092 and 0.0355, respectively, although some discrepancy in the simulated BRFs can be found at large average leaf angle (ALA) values. Unsurprisingly, albedos generated by the method are quite consistent, and 99.98% and 97.99% of the simulated white sky albedo (WSA) has a divergence less than 0.02. For the field measurements, the kernel-driven model presents somewhat better model-observation congruence than the PROSAIL model. The results show that these models have an overall good consistency for both field-measured and model-simulated BRFs. Therefore, there is potential for linking these two models for looking into the retrieval of canopy biophysical/structural variables through a simulation method, particularly from the current archive of the global routine MODIS BRDF parameters that were produced by the kernel-driven BRDF model; however, erectophile vegetation must be further examined.