Modeling effects of the bidirectional reflectance distribution function on remote sensing in the LWIR

2014 ◽  
Author(s):  
Samuel D. Butler ◽  
Michael A. Marciniak ◽  
Joseph Meola
Keyword(s):  
Remote Sensing ◽  
Distribution Function ◽  
Bidirectional Reflectance Distribution Function ◽  
Bidirectional Reflectance ◽  
Bidirectional Reflectance Distribution

scholarly journals Retrieval and validation of forest background reflectivity from daily Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) data across European forests

2021 ◽  
Vol 18 (2) ◽  
pp. 621-635
Author(s):  
Jan Pisek ◽  
Angela Erb ◽  
Lauri Korhonen ◽  
Tobias Biermann ◽  
Arnaud Carrara ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Distribution Function ◽  
Boreal Forests ◽  
Bidirectional Reflectance Distribution Function ◽  
Bidirectional Reflectance ◽  
Modis Data ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Bidirectional Reflectance Distribution

Abstract. Information about forest background reflectance is needed for accurate biophysical parameter retrieval from forest canopies (overstory) with remote sensing. Separating under- and overstory signals would enable more accurate modeling of forest carbon and energy fluxes. We retrieved values of the normalized difference vegetation index (NDVI) of the forest understory with the multi-angular Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/albedo data (gridded 500 m daily Collection 6 product), using a method originally developed for boreal forests. The forest floor background reflectance estimates from the MODIS data were compared with in situ understory reflectance measurements carried out at an extensive set of forest ecosystem experimental sites across Europe. The reflectance estimates from MODIS data were, hence, tested across diverse forest conditions and phenological phases during the growing season to examine their applicability for ecosystems other than boreal forests. Here we report that the method can deliver good retrievals, especially over different forest types with open canopies (low foliage cover). The performance of the method was found to be limited over forests with closed canopies (high foliage cover), where the signal from understory becomes too attenuated. The spatial heterogeneity of individual field sites and the limitations and documented quality of the MODIS BRDF product are shown to be important for the correct assessment and validation of the retrievals obtained with remote sensing.

Polarized Bidirectional Reflectance Spectra of Polished Granite

2012 ◽  
Vol 500 ◽  
pp. 193-197
Author(s):  
Lei Yan ◽  
Yun Xiang ◽  
Wei Chen ◽  
Yun Sheng Zhao
Keyword(s):  
Remote Sensing ◽  
Distribution Function ◽  
Mineral Exploration ◽  
Reflectance Spectra ◽  
Bidirectional Reflectance Distribution Function ◽  
Bidirectional Reflectance ◽  
Building Recognition ◽  
Outdoor Sculpture ◽  
Bidirectional Reflection ◽  
Bidirectional Reflectance Distribution

The granite is widely used for city architectural decoration, outdoor sculpture. Study on granite reflection is important for artificial building recognition and rock or mineral exploration. The Bidirectional Reflectance Distribution Function (BRDF) is used in conventional remote sensing to characterize the geometrical scatter of materials. Polarization occurs with multi-angle scatter. 90°and 0°polarized bidirectional reflection along the waveband of 300~2500nm for various view azimuth and zenith angles on polished granite are acquired. DOP then is calculated.

Analysis on Bidirectional Reflectance Distribution Function of Rough Earth Surface in Remote Sensing Imaging

2014 ◽  
Vol 575 ◽  
pp. 825-828
Author(s):  
Qi Li ◽  
Ke Cheng Pan ◽  
Kun Xing ◽  
Ning Juan Ruan ◽  
Hua Jun Feng
Keyword(s):  
Remote Sensing ◽  
Distribution Function ◽  
Measurement Data ◽  
Theoretical Models ◽  
Earth Surface ◽  
Bidirectional Reflectance Distribution Function ◽  
Bidirectional Reflectance ◽  
Typical Experiment ◽  
Ground Object ◽  
Bidirectional Reflectance Distribution

In remote sensing imaging, bidirectional reflectance distribution function (BRDF) is powerful tool to describe light reflectance characteristic. Based on BRDF distribution of ground object, reflectance distribution in all directions will be calculated, and radiation difference of remote sensing imaging can be calibrated. This paper analyzes the several empirical / semi-empirical BRDF model suitable for the rough earth surface. The cement ground was selected as typical experiment topography, and the ground reflectance in all directions at different solar zenith angle was obtained. We compare measurement data to calculation results of three theoretical models, including five parameter model, Staylor & Suttles model and Walthall model, and analyze and evaluate effectiveness of various models.

scholarly journals Retrieval and validation of forest background reflectivity from daily MODIS bidirectional reflectance distribution function (BRDF) data across European forests

2020 ◽  
Author(s):  
Jan Pisek ◽  
Angela Erb ◽  
Lauri Korhonen ◽  
Tobias Biermann ◽  
Arnaud Carrara ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Distribution Function ◽  
Boreal Forests ◽  
Vegetation Index ◽  
Forest Understory ◽  
Bidirectional Reflectance Distribution Function ◽  
Bidirectional Reflectance ◽  
Biophysical Parameter ◽  
Modis Data ◽  
Bidirectional Reflectance Distribution

Abstract. Information about forest background reflectance is needed for accurate biophysical parameter retrieval from forest canopies (overstory) with remote sensing. Separating under and overstory signals would enable more accurate modeling of forest carbon and energy fluxes. We retrieved values of normalized difference vegetation index (NDVI) of forest understory with multi-angular Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/albedo data (gridded 500 meter daily Collection 6 product), using a method originally developed for boreal forests. The forest floor background reflectance estimates from MODIS data were compared with in situ understory reflectance measurements carried out at an extensive set of forest ecosystem experimental sites across Europe. The reflectance estimates from MODIS data were hence tested across diverse forest conditions and phenological phases during the growing season, to examine its applicability on ecosystems other than boreal forests. Here we report the method can deliver good retrievals especially over different forest types with open canopies (low foliage cover). The performance of the method was found limited over forests with closed canopies (high foliage cover), where the signal from understory gets much attenuated. The spatial heterogeneity of individual field sites as well as the limitations and documented quality of the MODIS BRDF product are shown to be important for correct assessment and validation of the retrievals obtained with remote sensing.

The Application of Bidirectional Reflectance Distribution Function Data to Recognize the Spatial Heterogeneity of Mixed Pixels in Vegetation Remote Sensing: A Simulation Study

2020 ◽  
Vol 86 (3) ◽  
pp. 161-167
Author(s):  
Yanan Yan ◽  
Lei Deng ◽  
XianLin Liu
Keyword(s):  
Remote Sensing ◽  
Distribution Function ◽  
Spatial Distribution ◽  
Spatial Heterogeneity ◽  
Spatial Aggregation ◽  
Bidirectional Reflectance Distribution Function ◽  
Bidirectional Reflectance ◽  
Mixed Pixels ◽  
End Members ◽  
Bidirectional Reflectance Distribution

Spectral decomposition of mixed pixels can provide information about the abundance of end members but fails to indicate the spatial distribution of end members in vegetation remote sensing. This work is a significant attempt to use the bidirectional reflectance distribution function (<small>BRDF</small>) characteristics of mixed pixels in the prediction of spatial-heterogeneity metrics. Data sets from this function with different spatial distributions were constructed by the discrete anisotropic radiative transfer model, and three spatial aggregation and dispersion metrics were calculated: percentage of like adjacencies, spatial division index, and aggregation index. A simple linear regression method was used to construct the prediction model of spatial aggregation and dispersion metrics. The potential of multiangle remote sensing model for identifying spatial patterns well was demonstrated, and its importance was found to differ for different spatial aggregation and dispersion metrics. Specifically, the precision of the model based on multiangle reflectance used for predicting the spatial division index could meet a minimum root mean square of 5.95%. The reflectance features from backward observation on the principal plane play the leading role in recognizing the spatial heterogeneity of mixed pixels. The prediction model is sufficiently robust to distinguish the same vegetation with different growth trends, but also performs well when the ground objects have a smaller reflectance difference in the mixed pixels in a certain band. This study is expected to offer a new thought for spatial-heterogeneity identification of ground objects and thus promote the development of remote sensing technology in assessing spatial distribution.

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