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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (15 December 2021) | Viewed by 16233

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Special Issue Editors

Dr. Zhuosen Wang

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Guest Editor

Department of Geography and Environment, University of Maryland, College Park, MD 20742, USA
Interests: vegetation structure; BRDF; albedo; phenology dynamics and climate change; nighttime light; time series analysis
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Mark Chopping

E-Mail Website
Guest Editor

Montclair State University, Montclair, United States
Interests: BRDF and canopy reflectance modeling; terrestrial ecology/carbon cycle science; geographical information systems

Special Issue Information

Dear Colleagues,

Multi-angular remote sensing provides a unique way to infer information about Earth beyond nadir measurements alone. For land studies, surface reflectance is strongly anisotropic and governed by the bidirectional reflectance distribution function (BRDF). Surface albedo, generated by integrating hemisphere BRDF, is an essential climate variable for energy budget. Multi-angular measurements are sensitive to vegetation structure (e.g., clumping index, canopy height, vegetation fraction) and surface roughness and has been widely used for terrestrial applications such as biomass, photosynthesis, and lunar BRDF-corrected nighttime light. On the other hand, angular reflectance variation is one of the major sources of uncertainty for land cover classification, phenology, burned area, and time series analysis. Nadir BRDF-adjusted reflectance (NBAR), which adjusts the observation to nadir look, has significantly improved the accuracy of these applications. The soil vegetation system also shows strong anisotropy of emittance. Multi-angular thermal infrared measurements provide access to the radiative and convective processes of Earth’s surface. SAR imaging is highly impacted by the incidence angle. Multi-angular SAR observations obtain surface electromagnetic scattering properties from different angles and improve the retrieval of surface characteristics (e.g., soil moisture, urban area). Atmospheric aerosol affects the energy cycle and chemistry of the troposphere, and is one of the key parameters of atmospheric correction to produce surface reflectance. Previous studies indicate that multi-angle observations increase the accuracy of aerosol property retrieval. The aim of this Special Issue is to gather cutting-edge research on multi-angular remote sensing-related data collection, algorithm, calibration/validation, and application over the land, water, and atmosphere.

Dr. Zhuosen Wang
Prof. Dr. Mark Chopping
Guest Editors

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Published Papers (5 papers)

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Research

24 pages, 3926 KiB

Open AccessArticle

Retrieval of Leaf Area Index by Linking the PROSAIL and Ross-Li BRDF Models Using MODIS BRDF Data

by Xiaoning Zhang, Ziti Jiao, Changsen Zhao, Siyang Yin, Lei Cui, Yadong Dong, Hu Zhang, Jing Guo, Rui Xie, Sijie Li, Zidong Zhu and Yidong Tong

Remote Sens. 2021, 13(23), 4911; https://doi.org/10.3390/rs13234911 - 3 Dec 2021

Cited by 5 | Viewed by 1872

Abstract

Canopy structure parameters (e.g., leaf area index (LAI)) are key variables of most climate and ecology models. Currently, satellite-observed reflectances at a few viewing angles are often directly used for vegetation structure parameter retrieval; therefore, the information content of multi-angular observations that are sensitive to canopy structure in theory cannot be sufficiently considered. In this study, we proposed a novel method to retrieve LAI based on modelled multi-angular reflectances at sufficient sun-viewing geometries, by linking the PROSAIL model with a kernel-driven Ross-Li bi-directional reflectance function (BRDF) model using the MODIS BRDF parameter product. First, BRDF sensitivity to the PROSAIL input parameters was investigated to reduce the insensitive parameters. Then, MODIS BRDF parameters were used to model sufficient multi-angular reflectances. By comparing these reference MODIS reflectances with simulated PROSAIL reflectances within the range of the sensitive input parameters in the same geometries, the optimal vegetation parameters were determined by searching the minimum discrepancies between them. In addition, a significantly linear relationship between the average leaf angle (ALA) and the coefficient of the volumetric scattering kernel of the Ross-Li model in the near-infrared band was built, which can narrow the search scope of the ALA and accelerate the retrieval. In the validation, the proposed method attains a higher consistency (root mean square error (RMSE) = 1.13, bias = −0.19, and relative RMSE (RRMSE) = 36.8%) with field-measured LAIs and 30-m LAI maps for crops than that obtained with the MODIS LAI product. The results indicate the vegetation inversion potential of sufficient multi-angular data and the ALA relationship, and this method presents promise for large-scale LAI estimation. Full article

(This article belongs to the Special Issue Multi-Angular Remote Sensing)

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22 pages, 4249 KiB

Open AccessArticle

Influence of Varying Solar Zenith Angles on Land Surface Phenology Derived from Vegetation Indices: A Case Study in the Harvard Forest

by Yang Li, Ziti Jiao, Kaiguang Zhao, Yadong Dong, Yuyu Zhou, Yelu Zeng, Haiqing Xu, Xiaoning Zhang, Tongxi Hu and Lei Cui

Remote Sens. 2021, 13(20), 4126; https://doi.org/10.3390/rs13204126 - 15 Oct 2021

Cited by 3 | Viewed by 2120

Abstract

Vegetation indices are widely used to derive land surface phenology (LSP). However, due to inconsistent illumination geometries, reflectance varies with solar zenith angles (SZA), which in turn affects the vegetation indices, and thus the derived LSP. To examine the SZA effect on LSP, the MODIS bidirectional reflectance distribution function (BRDF) product and a BRDF model were employed to derive LSPs under several constant SZAs (i.e., 0°, 15°, 30°, 45°, and 60°) in the Harvard Forest, Massachusetts, USA. The LSPs derived under varying SZAs from the MODIS nadir BRDF-adjusted reflectance (NBAR) and MODIS vegetation index products were used as baselines. The results show that with increasing SZA, NDVI increases but EVI decreases. The magnitude of SZA-induced NDVI/EVI changes suggests that EVI is more sensitive to varying SZAs than NDVI. NDVI and EVI are comparable in deriving the start of season (SOS), but EVI is more accurate when deriving the end of season (EOS). Specifically, NDVI/EVI-derived SOSs are relatively close to those derived from ground measurements, with an absolute mean difference of 8.01 days for NDVI-derived SOSs and 9.07 days for EVI-derived SOSs over ten years. However, a considerable lag exists for EOSs derived from vegetation indices, especially from the NDVI time series, with an absolute mean difference of 14.67 days relative to that derived from ground measurements. The SOSs derived from NDVI time series are generally earlier, while those from EVI time series are delayed. In contrast, the EOSs derived from NDVI time series are delayed; those derived from the simulated EVI time series under a fixed illumination geometry are also delayed, but those derived from the products with varying illumination geometries (i.e., MODIS NBAR product and MODIS vegetation index product) are advanced. LSPs derived from varying illumination geometries could lead to a difference spanning from a few days to a month in this case study, which highlights the importance of normalizing the illumination geometry when deriving LSP from NDVI/EVI time series. Full article

(This article belongs to the Special Issue Multi-Angular Remote Sensing)

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31 pages, 6472 KiB

Open AccessArticle

Surface Albedo Retrieval from 40-Years of Earth Observations through the EUMETSAT/LSA SAF and EU/C3S Programmes: The Versatile Algorithm of PYALUS

by Dominique Carrer, Florian Pinault, Gabriel Lellouch, Isabel F. Trigo, Iskander Benhadj, Fernando Camacho, Xavier Ceamanos, Suman Moparthy, Joaquin Munoz-Sabater, Lothar Schüller and Jorge Sánchez-Zapero

Remote Sens. 2021, 13(3), 372; https://doi.org/10.3390/rs13030372 - 21 Jan 2021

Cited by 11 | Viewed by 3942

Abstract

Land surface albedo quantifies the fraction of the sunlight reflected by the surface of the Earth. This article presents the algorithm concepts for the remote sensing of this variable based on the heritage of several developments which were performed at Méteo France over the last decade and described in several papers by Carrer et al. The scientific algorithm comprises four steps: an atmospheric correction, a sensor harmonisation (optional), a BRDF (Bidirectional Reflectance Distribution Function) inversion, and the albedo calculation. At the time being, the method has been applied to 11 sensors in the framework of two European initiatives (Satellite Application Facility on Land Surface Analysis—LSA SAF, and Copernicus Climate Change Service—C3S): NOAA-7-9-11-14-16-17/AVHRR2-3, SPOT/VGT1-2, Metop/AVHRR-3, PROBA-V, and MSG/SEVIRI. This work leads to a consistent archive of almost 40 years of satellite-derived albedo data (available in 2020). From a single sensor, up to three different albedo products with different characteristics have been developed to address the requirements of both, near real-time (NRT) (weather prediction with a demand of timeliness of 1 h) and climate communities. The evaluation of the algorithm applied to different platforms was recently made by Lellouch et al. and Sánchez Zapero et al. in 2020 which can be considered as companion papers. After a summary of the method for the retrieval of these surface albedos, this article describes the specificities of each retrieval, lists the differences, and discusses the limitations. The plan of continuity with the next European satellite missions and perspectives of improvements are introduced. For example, Metop/AVHRR-3 albedo will soon become the medium resolution sensor product with the longest NRT data record, since MODIS is approaching the end of its life-cycle. Additionally, Metop-SG/METimage will ensure its continuity thanks to consistent production of data sets guaranteed till 2050 by the member states of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). In the end, the common strategy which we proposed through the different programmes may offer an unprecedented opportunity to study the temporal trends affecting surface properties and to analyse human-induced climate change. Finally, the access to the source code (called PYALUS) is provided through an open access platform in order to share with the community the expertise on the satellite retrieval of this variable. Full article

(This article belongs to the Special Issue Multi-Angular Remote Sensing)

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22 pages, 24503 KiB

Open AccessArticle

An Anisotropic Scattering Analysis Method Based on the Statistical Properties of Multi-Angular SAR Images

by Fei Teng, Yun Lin, Yanping Wang, Wenjie Shen, Shanshan Feng and Wen Hong

Remote Sens. 2020, 12(13), 2152; https://doi.org/10.3390/rs12132152 - 5 Jul 2020

Cited by 14 | Viewed by 2956

Abstract

The scatterings of many targets are aspect dependent, which is called anisotropy. Multi-angular synthetic aperture radar (SAR) is a suitable means of detecting this kind of anisotropic scattering behavior by viewing targets from different aspect angles. First, the statistical properties of anisotropic and isotropic scatterings are studied in this paper. X-band chamber circular SAR data are used. The result shows that isotropic scatterings have stable distributions in different aspect viewing angles while the distributions of anisotropic scatterings are various. Then the statistical properties of single polarization high-resolution multi-angular SAR images are modeled by different distributions. G

^{0}

distribution performs best in all types of areas. An anisotropic scattering analysis method based on the multi-angular statistical properties is proposed. A likelihood ratio test based on G

^{0}

distribution is used to measure the anisotropy. Anisotropic scatterings can be discriminated from isotropic scatterings by thresholding. Besides, the scattering direction can also be estimated by our method. AHH polarization C-band circular SAR data are used to validate our method. The result of using G

^{0}

distribution is compared with the result of using Rayleigh distribution. The result of using G

^{0}

distribution is the better one. Full article

(This article belongs to the Special Issue Multi-Angular Remote Sensing)

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21 pages, 6415 KiB

Open AccessArticle

Estimating Forest Canopy Height Using MODIS BRDF Data Emphasizing Typical-Angle Reflectances

by Lei Cui, Ziti Jiao, Yadong Dong, Mei Sun, Xiaoning Zhang, Siyang Yin, Anxin Ding, Yaxuan Chang, Jing Guo and Rui Xie

Remote Sens. 2019, 11(19), 2239; https://doi.org/10.3390/rs11192239 - 26 Sep 2019

Cited by 22 | Viewed by 3762

Abstract

Forest-canopy height is an important parameter for the estimation of forest biomass and terrestrial carbon flux and climate-change research at regional and global scales. Currently, various methods combining Light Detection and Ranging (LiDAR) data with various auxiliary data, particularly satellite remotely sensed reflectances, have been widely used to produce spatially continuous canopy-height products. However, current methods in use for remote sensing reflectances mainly focus on the nadir view direction, while anisotropic reflectances, which are theoretically more sensitive to the forest canopy height in the multiangle remote sensing field, have rarely been explored. Here, we attempted to examine the potential of using modeled multiangle reflectances at three typical viewing angles (i.e., from the hotspot, darkspot, and nadir directions) to estimate forest-canopy height as auxiliary data sources. First, the sensitivities of the typical angular reflectances as a function of forest canopy height were fully examined using the Extended Fourier Amplitude Sensitivity Test (EFAST) method based on the 4-scale Bidirectional Reflectance Distribution Function (BRDF) model simulations. This indicated that reflectances in the off-nadir viewing directions are generally sensitive to canopy-height variations. Then, the canopy heights were extracted from airborne Laser Vegetation Imaging Sensor (LVIS) data, which were further divided into training and validation data. Moderate Resolution Imaging Spectroradiometer (MODIS) multiangle reflectances at typical viewing angles were calculated from the MODIS BRDF parameter product (MCD43A1, version 6) as partial training-input data, based on a hotspot-adjusted, kernel-driven linear BRDF model. Subsequently, the Random Forest (RF) machine learning model was trained to acquire the relationship between the extracted canopy heights and the corresponding MODIS typical viewing reflectances. The trained model was further applied to estimate the canopy height metrics in the study areas of Howland Forest, Harvard Forest, and Bartlett Forest. Finally, the estimated canopy heights were independently validated by canopy heights extracted from the LVIS data. The results indicate that the canopy heights modeled through this method exhibit generally high accordance with the LVIS-derived canopy heights (R = 0.65−0.67; RMSE = 3.63−5.78). The results suggest that the MODIS multiangle reflectance data at typical observation angles contain important information regarding forest canopy height and can, therefore, be used to estimate forest canopy height for various ecological applications. Full article

(This article belongs to the Special Issue Multi-Angular Remote Sensing)

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