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Donghui Xie
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

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Journal article
Published: 27 April 2021 in Remote Sensing
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Optical remote sensing indices play an important role in vegetation information extraction and have been widely serving ecology, agriculture and forestry, urban monitoring, and other communities. Remote sensing indices are constructed from individual bands depending on special characteristics to enhance the typical spectral features for the identification or distinction of surface land covers. With the development of quantitative remote sensing, there is a rapid increasing requirement for accurate data processing and modeling. It is well known that the geometry-induced variation observed in surface reflectance is not ignorable, but the situation of uncertainty thereby introduced into these indices still needs further detailed understanding. We adopted the ground multi-angle hyperspectrum, spectral response function (SRF) of Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), Operational Land Imager (OLI), Moderate-Resolution Imaging Spectroradiometer (MODIS), and Multi-Spectral Instrument (MSI) optical sensors and simulated their sensor-like spectral reflectance; then, we investigated the potential angle effect uncertainty on optical indices that have been frequently involved in vegetation monitoring and examined the forward/backward effect over both the ground-based level and the actual Landsat TM/ETM+ overlapped region. Our results on the discussed indices and sensors show as following: (1) Identifiable angle effects exist with a more elevated influence than that introduced by band difference among sensors; (2) The absolute difference between forward and backward direction can reach up to −0.03 to 0.1 within bands of the TM/ETM+ overlapped region; (3) The investigation at ground level indicates that there are different variations of angle effect transmitted to each remote sensing index. Regarding cases of crop canopy at various growth phases, most of the discussed indices have more than a 20% relative difference to nadir value except Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) with the magnitude lower than 10%, and less than 16% of Normalized Burn Ratio (NBR). For the case of wax maturity stage, the relative difference to nadir value of Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), Ratio Vegetation Index (RVI), Char Soil Index (CSI), NBR, Normalized Difference Moisture Index (NDMI), and SWIR2/NIR exceeded 50%, while the values for NBR and NDMI can reach up to 115.8% and 206.7%, respectively; (4) Various schemes of index construction imply different propagation of angle effect uncertainty. The “difference” indices can partially suppress the directional influence, while the “ratio” indices show high potential to amplify the angle effect. This study reveals that the angle-induced uncertainty of these indices is greater than that induced by the spectrum mismatch among sensors, especially under the case of senescence. In addition, based on this work, indices with a suppressed potential of angle effect are recommended for vegetation monitoring or information retrieval to avoid unexpected effects.

ACS Style

Lingxiao Gu; Yanmin Shuai; Congying Shao; Donghui Xie; Qingling Zhang; Yaoming Li; Jian Yang. Angle Effect on Typical Optical Remote Sensing Indices in Vegetation Monitoring. Remote Sensing 2021, 13, 1699 .

AMA Style

Lingxiao Gu, Yanmin Shuai, Congying Shao, Donghui Xie, Qingling Zhang, Yaoming Li, Jian Yang. Angle Effect on Typical Optical Remote Sensing Indices in Vegetation Monitoring. Remote Sensing. 2021; 13 (9):1699.

Chicago/Turabian Style

Lingxiao Gu; Yanmin Shuai; Congying Shao; Donghui Xie; Qingling Zhang; Yaoming Li; Jian Yang. 2021. "Angle Effect on Typical Optical Remote Sensing Indices in Vegetation Monitoring." Remote Sensing 13, no. 9: 1699.

Journal article
Published: 12 April 2021 in Journal of Remote Sensing
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Both leaf inclination angle distribution (LAD) and leaf area index (LAI) dominate optical remote sensing signals. The G-function, which is a function of LAD and remote sensing geometry, is often set to 0.5 in the LAI retrieval of coniferous canopies even though this assumption is only valid for spherical LAD. Large uncertainties are thus introduced. However, because numerous tiny leaves grow on conifers, it is nearly impossible to quantitatively evaluate such uncertainties in LAI retrieval. In this study, we proposed a method to characterize the possible change of G-function of coniferous canopies as well as its effect on LAI retrieval. Specifically, a Multi-Directional Imager (MDI) was developed to capture stereo images of the branches, and the needles were reconstructed. The accuracy of the inclination angles calculated from the reconstructed needles was high. Moreover, we analyzed whether a spherical distribution is a valid assumption for coniferous canopies by calculating the possible range of the G-function from the measured LADs of branches of Larch and Spruce and the true G-functions of other species from some existing inventory data and three-dimensional (3D) tree models. Results show that the constant G assumption introduces large errors in LAI retrieval, which could be as large as 53% in the zenithal viewing direction used by spaceborne LiDAR. As a result, accurate LAD estimation is recommended. In the absence of such data, our results show that a viewing zenith angle between 45 and 65 degrees is a good choice, at which the errors of LAI retrieval caused by the spherical assumption will be less than 10% for coniferous canopies.

ACS Style

Guangjian Yan; Hailan Jiang; Jinghui Luo; Xihan Mu; Fan Li; Jianbo Qi; Ronghai Hu; Donghui Xie; Guoqing Zhou. Quantitative Evaluation of Leaf Inclination Angle Distribution on Leaf Area Index Retrieval of Coniferous Canopies. Journal of Remote Sensing 2021, 2021, 1 -15.

AMA Style

Guangjian Yan, Hailan Jiang, Jinghui Luo, Xihan Mu, Fan Li, Jianbo Qi, Ronghai Hu, Donghui Xie, Guoqing Zhou. Quantitative Evaluation of Leaf Inclination Angle Distribution on Leaf Area Index Retrieval of Coniferous Canopies. Journal of Remote Sensing. 2021; 2021 ():1-15.

Chicago/Turabian Style

Guangjian Yan; Hailan Jiang; Jinghui Luo; Xihan Mu; Fan Li; Jianbo Qi; Ronghai Hu; Donghui Xie; Guoqing Zhou. 2021. "Quantitative Evaluation of Leaf Inclination Angle Distribution on Leaf Area Index Retrieval of Coniferous Canopies." Journal of Remote Sensing 2021, no. : 1-15.

Journal article
Published: 01 April 2021 in IEEE Transactions on Geoscience and Remote Sensing
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To improve our capacity to map long-term vegetation dynamics in heterogeneous landscapes, this study proposed a new prior knowledge-based spatiotemporal enhancement method, namely, PK-STEM, to fuse MODIS and Landsat FPAR products following the remote sensing trend surface framework. PK-STEM uses historical Landsat FPAR images as prior knowledge and fuses them with new satellite-derived FPAR data. PK-STEM can work in three modes: 1) using only MODIS data; 2) using only Landsat data; and 3) using both MODIS and Landsat data. This study retrieved FPAR from Landsat images using a scaling-based method and tested the performance of PK-STEM in a regional application. For the entire year of 2012, we compared the performance of PK-STEM in different modes and with that of two typical spatiotemporal fusion methods, the enhanced spatial and temporal adaptive reflectance model (ESTARFM) and unmixing-based linear mixing growth model (LMGM). Then, a long time series FPAR data set at 30-m resolution and eight-day intervals was generated for 13 years (2000-2012). Our results show that PK-STEM in mode III is the most robust and accurate (root mean squared error (RMSE) = 0.062; mean R = 0.851) among the three modes and more accurate than ESTARFM (mean RMSE = 0.065; mean R = 0.776) and LMGM (mean RMSE = 0.074; mean R = 0.734). For the 12 years (2000-2011), PK-STEM also achieves high accuracies with mean RMSE = 0.066 and R = 0.938. PK-STEM is very flexible with a continual update mechanism and is efficient for long time series applications.

ACS Style

Yiting Wang; Guangjian Yan; Donghui Xie; Ronghai Hu; Hu Zhang. Generating Long Time Series of High Spatiotemporal Resolution FPAR Images in the Remote Sensing Trend Surface Framework. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -15.

AMA Style

Yiting Wang, Guangjian Yan, Donghui Xie, Ronghai Hu, Hu Zhang. Generating Long Time Series of High Spatiotemporal Resolution FPAR Images in the Remote Sensing Trend Surface Framework. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-15.

Chicago/Turabian Style

Yiting Wang; Guangjian Yan; Donghui Xie; Ronghai Hu; Hu Zhang. 2021. "Generating Long Time Series of High Spatiotemporal Resolution FPAR Images in the Remote Sensing Trend Surface Framework." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-15.

Journal article
Published: 18 March 2021 in Remote Sensing
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Leaf angle distribution (LAD) is an important attribute of forest canopy architecture and affects the solar radiation regime within the canopy. Terrestrial laser scanning (TLS) has been increasingly used in LAD estimation. The point clouds data suffer from the occlusion effect, which leads to incomplete scanning and depends on measurement strategies such as the number of scans and scanner location. Evaluating these factors is important to understand how to improve LAD, which is still lacking. Here, we introduce an easy way of estimating the LAD using open source software. Importantly, the influence of the occlusion effect on the LAD was evaluated by combining the proposed complete point clouds (CPCs) with the simulated data of 3D tree models of Aspen, Pin Oak and White Oak. We analyzed the effects of the point density, the number of scans and the scanner height on the LAD and G-function. Results show that: (1) the CPC can be used to evaluate the TLS-based normal vector reconstruction accuracy without an occlusion effect; (2) the accuracy is slightly affected by the normal vector reconstruction method and is greatly affected by the point density and the occlusion effect. The higher the point density (with a number of points per unit leaf area of 0.2 cm−2 to 27 cm−2 tested), the better the result is; (3) the performance is more sensitive to the scanner location than the number of scans. Increasing the scanner height improves LAD estimation, which has not been seriously considered in previous studies. It is worth noting that relatively tall trees suffer from a more severe occlusion effect, which deserves further attention in further study.

ACS Style

Hailan Jiang; Ronghai Hu; Guangjian Yan; Shiyu Cheng; Fan Li; Jianbo Qi; Linyuan Li; Donghui Xie; Xihan Mu. Influencing Factors in Estimation of Leaf Angle Distribution of an Individual Tree from Terrestrial Laser Scanning Data. Remote Sensing 2021, 13, 1159 .

AMA Style

Hailan Jiang, Ronghai Hu, Guangjian Yan, Shiyu Cheng, Fan Li, Jianbo Qi, Linyuan Li, Donghui Xie, Xihan Mu. Influencing Factors in Estimation of Leaf Angle Distribution of an Individual Tree from Terrestrial Laser Scanning Data. Remote Sensing. 2021; 13 (6):1159.

Chicago/Turabian Style

Hailan Jiang; Ronghai Hu; Guangjian Yan; Shiyu Cheng; Fan Li; Jianbo Qi; Linyuan Li; Donghui Xie; Xihan Mu. 2021. "Influencing Factors in Estimation of Leaf Angle Distribution of an Individual Tree from Terrestrial Laser Scanning Data." Remote Sensing 13, no. 6: 1159.

Journal article
Published: 14 January 2021 in Remote Sensing
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Despite its wide applications, the spatiotemporal fusion of coarse- and fine-resolution satellite images is limited primarily to the availability of clear-sky fine-resolution images, which are commonly scarce due to unfavorable weather, and such a limitation might cause errors in spatiotemporal fusion. Thus, the effective use of limited fine-resolution images, while critical, remains challenging. To address this issue, in this paper we propose a new phenological similarity strategy (PSS) to select the optimal combination of image pairs for a prediction date. The PSS considers the temporal proximity and phenological similarity between the base and prediction images and computes a weight for identifying the optimal combination of image pairs. Using the PSS, we further evaluate the influence of input data on the fusion accuracy by varying the number and temporal distribution of input images. The results show that the PSS (mean R = 0.827 and 0.760) outperforms the nearest date (mean R = 0.786 and 0.742) and highest correlation (mean R = 0.821 and 0.727) strategies in both the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the linear mixing growth model (LMGM), respectively, for fusing Landsat 8 OLI and MODIS NDVI datasets. Furthermore, base images adequately covering different growth stages yield better predictability than simply increasing the number of base images.

ACS Style

Yiting Wang; Donghui Xie; Yinggang Zhan; Huan Li; Guangjian Yan; Yuanyuan Chen. Assessing the Accuracy of Landsat-MODIS NDVI Fusion with Limited Input Data: A Strategy for Base Data Selection. Remote Sensing 2021, 13, 266 .

AMA Style

Yiting Wang, Donghui Xie, Yinggang Zhan, Huan Li, Guangjian Yan, Yuanyuan Chen. Assessing the Accuracy of Landsat-MODIS NDVI Fusion with Limited Input Data: A Strategy for Base Data Selection. Remote Sensing. 2021; 13 (2):266.

Chicago/Turabian Style

Yiting Wang; Donghui Xie; Yinggang Zhan; Huan Li; Guangjian Yan; Yuanyuan Chen. 2021. "Assessing the Accuracy of Landsat-MODIS NDVI Fusion with Limited Input Data: A Strategy for Base Data Selection." Remote Sensing 13, no. 2: 266.

Journal article
Published: 27 May 2020 in IEEE Transactions on Geoscience and Remote Sensing
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ACS Style

Guangjian Yan; Qing Chu; Yiyi Tong; Xihan Mu; Jianbo Qi; Yingji Zhou; Yanan Liu; Tianxing Wang; Donghui Xie; Wuming Zhang; Kai Yan; Shengbo Chen; Hongmin Zhou. An Operational Method for Validating the Downward Shortwave Radiation Over Rugged Terrains. IEEE Transactions on Geoscience and Remote Sensing 2020, 1 -18.

AMA Style

Guangjian Yan, Qing Chu, Yiyi Tong, Xihan Mu, Jianbo Qi, Yingji Zhou, Yanan Liu, Tianxing Wang, Donghui Xie, Wuming Zhang, Kai Yan, Shengbo Chen, Hongmin Zhou. An Operational Method for Validating the Downward Shortwave Radiation Over Rugged Terrains. IEEE Transactions on Geoscience and Remote Sensing. 2020; ():1-18.

Chicago/Turabian Style

Guangjian Yan; Qing Chu; Yiyi Tong; Xihan Mu; Jianbo Qi; Yingji Zhou; Yanan Liu; Tianxing Wang; Donghui Xie; Wuming Zhang; Kai Yan; Shengbo Chen; Hongmin Zhou. 2020. "An Operational Method for Validating the Downward Shortwave Radiation Over Rugged Terrains." IEEE Transactions on Geoscience and Remote Sensing , no. : 1-18.

Journal article
Published: 19 March 2020 in IEEE Transactions on Geoscience and Remote Sensing
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Accurate estimation of the fine-resolution fraction of absorbed photosynthetically active radiation (FPAR) across broad spatial extents and long time periods requires efficient and applicable methods. The existing methods can hardly provide a balance between accuracy, simplicity, and transferability through space and time. Within the remote-sensing trend-surface conceptual framework, this article proposes a scaling-based method to efficiently retrieve FPAR from fine-resolution satellite data using coarse-resolution FPAR products as a reference. The method was particularly developed and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) FPAR product and Landsat imagery. First, necessary prior knowledge related to FPAR retrieval and scaling theories was used to explicitly linearize the complex relationship between MODIS FPAR and Landsat surface reflectance. Second, the explicit linear model for FPAR estimation was trained through one-pair image learning for each date to estimate FPAR from Landsat imagery in real time. Both homogeneous and heterogeneous cases were considered. The method was validated at ten selected worldwide sites from the Validation of Land European Remote Sensing Instruments (VALERI) program and derived an overall root mean squared error (RMSE) of 0.133. A long time series of FPAR data set at the 30-m resolution was generated at the regional scale (approximately 2000 km²) for 13 years (2000-2012). The results were accurate (RMSE = 0.072) and MODIS-consistent, which were significantly better than those of the normalized difference vegetation index (NDVI) downscaling-based and regression tree methods. The scaling-based method provides accurate, MODIS-consistent and spatially consistent FPAR estimates in real time, is highly transferrable through space and time, and allows for future extension of FPAR estimates to the era of the Landsat series satellites.

ACS Style

Yiting Wang; Guangjian Yan; Ronghai Hu; Donghui Xie; Wei Chen. A Scaling-Based Method for the Rapid Retrieval of FPAR From Fine-Resolution Satellite Data in the Remote-Sensing Trend-Surface Framework. IEEE Transactions on Geoscience and Remote Sensing 2020, 1 -14.

AMA Style

Yiting Wang, Guangjian Yan, Ronghai Hu, Donghui Xie, Wei Chen. A Scaling-Based Method for the Rapid Retrieval of FPAR From Fine-Resolution Satellite Data in the Remote-Sensing Trend-Surface Framework. IEEE Transactions on Geoscience and Remote Sensing. 2020; (99):1-14.

Chicago/Turabian Style

Yiting Wang; Guangjian Yan; Ronghai Hu; Donghui Xie; Wei Chen. 2020. "A Scaling-Based Method for the Rapid Retrieval of FPAR From Fine-Resolution Satellite Data in the Remote-Sensing Trend-Surface Framework." IEEE Transactions on Geoscience and Remote Sensing , no. 99: 1-14.

Journal article
Published: 16 January 2020 in Remote Sensing
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Vegetation cover estimation for overstory and understory layers provides valuable information for modeling forest carbon and water cycles and refining forest ecosystem function assessment. Although previous studies demonstrated the capability of light detection and ranging (LiDAR) in the three-dimensional (3D) characterization of forest overstory and understory communities, the high cost inhibits its application in frequent and successive survey tasks. Low-cost commercial red–green–blue (RGB) cameras mounted on unmanned aerial vehicles (UAVs), as LiDAR alternatives, provide operational systems for simultaneously quantifying overstory crown cover (OCC) and understory vegetation cover (UVC). We developed an effective method named back-projection of 3D point cloud onto superpixel-segmented image (BAPS) to extract overstory and forest floor pixels using 3D structure-from-motion (SfM) point clouds and two-dimensional (2D) superpixel segmentation. The OCC was estimated from the extracted overstory crown pixels. A reported method, called half-Gaussian fitting (HAGFVC), was used to segement green vegetation and non-vegetation pixels from the extracted forest floor pixels and derive UVC. The UAV-based RGB imagery and field validation data were collected from eight forest plots in Saihanba National Forest Park (SNFP) plantation in northern China. The consistency of the OCC estimates between BAPS and canopy height model (CHM)-based methods (coefficient of determination: 0.7171) demonstrated the capability of the BAPS method in the estimation of OCC. The segmentation of understory vegetation was verified by the supervised classification (SC) method. The validation results showed that the OCC and UVC estimates were in good agreement with reference values, where the root-mean-square error (RMSE) of OCC (unitless) and UVC (unitless) reached 0.0704 and 0.1144, respectively. The low-cost UAV-based observation system and the newly developed method are expected to improve the understanding of ecosystem functioning and facilitate ecological process modeling.

ACS Style

Linyuan Li; Jun Chen; Xihan Mu; Weihua Li; Guangjian Yan; Donghui Xie; Wuming Zhang. Quantifying Understory and Overstory Vegetation Cover Using UAV-Based RGB Imagery in Forest Plantation. Remote Sensing 2020, 12, 298 .

AMA Style

Linyuan Li, Jun Chen, Xihan Mu, Weihua Li, Guangjian Yan, Donghui Xie, Wuming Zhang. Quantifying Understory and Overstory Vegetation Cover Using UAV-Based RGB Imagery in Forest Plantation. Remote Sensing. 2020; 12 (2):298.

Chicago/Turabian Style

Linyuan Li; Jun Chen; Xihan Mu; Weihua Li; Guangjian Yan; Donghui Xie; Wuming Zhang. 2020. "Quantifying Understory and Overstory Vegetation Cover Using UAV-Based RGB Imagery in Forest Plantation." Remote Sensing 12, no. 2: 298.

Journal article
Published: 12 November 2019 in Remote Sensing
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Three-dimensional (3D) radiative transfer models are the most accurate remote sensing models. However, presently the application of 3D models to heterogeneous Earth scenes is a computationally intensive task. A common approach to reduce computation time is abstracting the landscape elements into simpler geometries (e.g., ellipsoid), which, however, may introduce biases. Here, a hybrid scene structuring approach is proposed to accelerate the radiative transfer simulations while keeping the scene as realistic as possible. In a first step, a 3D description of the Earth landscape with equal-sized voxels is optimized to keep only non-empty voxels (i.e., voxels that contain triangles) and managed using a bounding volume hierarchy (BVH). For any voxel that contains triangles, within-voxel BVHs are created to accelerate the ray–triangle intersection tests. The hybrid scheme is implemented in the Discrete Anisotropic Radiative Transfer (DART) model by integrating the Embree ray-tracing kernels developed at Intel. In this paper, the performance of the hybrid algorithm is compared with the original uniform grid approach implemented in DART for a 3D city scene and a forest scene. Results show that the removal of empty voxels can accelerate urban simulation by 1.4×~3.7×, and that the within-voxel BVH can accelerate forest simulations by up to 258.5×.

ACS Style

Jianbo Qi; Tiangang Yin; Donghui Xie; Jean-Philippe Gastellu-Etchegorry. Hybrid Scene Structuring for Accelerating 3D Radiative Transfer Simulations. Remote Sensing 2019, 11, 2637 .

AMA Style

Jianbo Qi, Tiangang Yin, Donghui Xie, Jean-Philippe Gastellu-Etchegorry. Hybrid Scene Structuring for Accelerating 3D Radiative Transfer Simulations. Remote Sensing. 2019; 11 (22):2637.

Chicago/Turabian Style

Jianbo Qi; Tiangang Yin; Donghui Xie; Jean-Philippe Gastellu-Etchegorry. 2019. "Hybrid Scene Structuring for Accelerating 3D Radiative Transfer Simulations." Remote Sensing 11, no. 22: 2637.

Journal article
Published: 23 October 2019 in Remote Sensing
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The fraction of absorbed photosynthetically active radiation (FAPAR) is generally divided into the fraction of radiation absorbed by the photosynthetic components (FAPARgreen) and the fraction of radiation absorbed by the non-photosynthetic components (FAPARwoody) of the vegetation. However, most global FAPAR datasets do not take account of the woody components when considering the canopy radiation transfer. The objective of this study was to develop a generic algorithm for partitioning FAPARcanopy into FAPARgreen and FAPARwoody based on a triple-source leaf-wood-soil layer (TriLay) approach. The LargE-Scale remote sensing data and image simulation framework (LESS) model was used to validate the TriLay approach. The results showed that the TriLay FAPARgreen had higher retrieval accuracy, as well as a significantly lower bias (R2 = 0.937, Root Mean Square Error (RMSE) = 0.064, and bias = −6.02% for black-sky conditions; R2 = 0.997, RMSE = 0.025 and bias = −4.04% for white-sky conditions) compared to the traditional linear method (R2 = 0.979, RMSE = 0.114, and bias = −18.04% for black-sky conditions; R2 = 0.996, RMSE = 0.106 and bias = −16.93% for white-sky conditions). For FAPAR that did not take account of woody components (FAPARnoWAI), the corresponding results were R2 = 0.920, RMSE = 0.071, and bias = −7.14% for black-sky conditions, and R2 = 0.999, RMSE = 0.043, and bias = −6.41% for white-sky conditions. Finally, the dynamic FAPARgreen, FAPARwoody, FAPARcanopy and FAPARnoWAI products for a North America region were generated at a resolution of 500 m for every eight days in 2017. A comparison of the results for FAPARgreen against those for FAPARnoWAI and FAPARcanopy showed that the discrepancy between FAPARgreen and other FAPAR products for forest vegetation types could not be ignored. For deciduous needleleaf forest, in particular, the black-sky FAPARgreen was found to contribute only about 23.86% and 35.75% of FAPARcanopy at the beginning and end of the year (from January to March and October to December, JFM and OND), and 75.02% at the peak growth stage (from July to September, JAS); the black-sky FAPARnoWAI was found to be overestimated by 38.30% and 28.46% during the early (JFM) and late (OND) part of the year, respectively. Therefore, the TriLay approach performed well in separating FAPARgreen from FAPARcanopy, which is of great importance for a better understanding of the energy exchange within the canopy.

ACS Style

Siyuan Chen; Liangyun Liu; Xiao Zhang; Xiaojin Qian; Yue Xu; Donghui Xie. Retrieval of the Fraction of Radiation Absorbed by Photosynthetic Components (FAPARgreen) for Forest using a Triple-Source Leaf-Wood-Soil Layer Approach. Remote Sensing 2019, 11, 2471 .

AMA Style

Siyuan Chen, Liangyun Liu, Xiao Zhang, Xiaojin Qian, Yue Xu, Donghui Xie. Retrieval of the Fraction of Radiation Absorbed by Photosynthetic Components (FAPARgreen) for Forest using a Triple-Source Leaf-Wood-Soil Layer Approach. Remote Sensing. 2019; 11 (21):2471.

Chicago/Turabian Style

Siyuan Chen; Liangyun Liu; Xiao Zhang; Xiaojin Qian; Yue Xu; Donghui Xie. 2019. "Retrieval of the Fraction of Radiation Absorbed by Photosynthetic Components (FAPARgreen) for Forest using a Triple-Source Leaf-Wood-Soil Layer Approach." Remote Sensing 11, no. 21: 2471.

Journal article
Published: 20 February 2019 in IEEE Geoscience and Remote Sensing Letters
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ACS Style

Jianbo Qi; Donghui Xie; Linyuan Li; Wuming Zhang; Xihan Mu; Guangjian Yan. Estimating Leaf Angle Distribution From Smartphone Photographs. IEEE Geoscience and Remote Sensing Letters 2019, 16, 1190 -1194.

AMA Style

Jianbo Qi, Donghui Xie, Linyuan Li, Wuming Zhang, Xihan Mu, Guangjian Yan. Estimating Leaf Angle Distribution From Smartphone Photographs. IEEE Geoscience and Remote Sensing Letters. 2019; 16 (8):1190-1194.

Chicago/Turabian Style

Jianbo Qi; Donghui Xie; Linyuan Li; Wuming Zhang; Xihan Mu; Guangjian Yan. 2019. "Estimating Leaf Angle Distribution From Smartphone Photographs." IEEE Geoscience and Remote Sensing Letters 16, no. 8: 1190-1194.

Journal article
Published: 17 December 2018 in Remote Sensing of Environment
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Three-dimensional (3D) radiative transfer modeling of the transport and interaction of radiation through earth surfaces is challenging due to the complexity of the landscapes as well as the intensive computational cost of 3D radiative transfer simulations. To reduce computation time, current models work with schematic landscapes or with small-scale realistic scenes. The computer graphics community provides the most accurate and efficient models (known as renderers) but they were not designed specifically for performing scientific radiative transfer simulations. In this study, we propose LESS, a new 3D radiative transfer modeling framework. LESS employs a weighted forward photon tracing method to simulate multispectral bidirectional reflectance factor (BRF) or flux-related data (e.g., downwelling radiation) and a backward path tracing method to generate sensor images (e.g., fisheye images) or large-scale (e.g. 1 km2) spectral images. The backward path tracing also has been extended to simulate thermal infrared radiation by using an on-the-fly computation of the sunlit and shaded scene components. This framework is achieved through the development of a user-friendly graphic user interface (GUI) and a set of tools to help construct the landscape and set parameters. The accuracy of LESS is evaluated with other models as well as field measurements in terms of directional BRFs and pixel-wise simulated image comparisons, which shows very good agreement. LESS has the potential in simulating datasets of realistically reconstructed landscapes. Such simulated datasets can be used as benchmarks for various applications in remote sensing, forestry investigation and photogrammetry.

ACS Style

Jianbo Qi; Donghui Xie; Tiangang Yin; Guangjian Yan; Jean Philippe Gastellu-Etchegorry; Linyuan Li; Wuming Zhang; Xihan Mu; Leslie K. Norford. LESS: LargE-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes. Remote Sensing of Environment 2018, 221, 695 -706.

AMA Style

Jianbo Qi, Donghui Xie, Tiangang Yin, Guangjian Yan, Jean Philippe Gastellu-Etchegorry, Linyuan Li, Wuming Zhang, Xihan Mu, Leslie K. Norford. LESS: LargE-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes. Remote Sensing of Environment. 2018; 221 ():695-706.

Chicago/Turabian Style

Jianbo Qi; Donghui Xie; Tiangang Yin; Guangjian Yan; Jean Philippe Gastellu-Etchegorry; Linyuan Li; Wuming Zhang; Xihan Mu; Leslie K. Norford. 2018. "LESS: LargE-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes." Remote Sensing of Environment 221, no. : 695-706.

Review
Published: 06 December 2018 in Agricultural and Forest Meteorology
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Leaf area index (LAI) is a key parameter of vegetation structure in the fields of agriculture, forestry, and ecology. Optical indirect methods based on the Beer-Lambert law are widely adopted in numerous fields given their high efficiency and feasibility for LAI estimation. These methods have undergone considerable progress in the past decades, thereby making them operational in ground-based LAI measurement and even in airborne estimation. However, several challenges remain, given the requirement of increasing accuracy and new applications. Clumping effect correction attained significant progress for continuous canopies with non-randomly disturbed leaves while non-continuous canopies are rarely studied. Convenient and operational measurement of leaf angle distribution and woody components is lacked. Accurate and comprehensive validations are still very difficult due to the limitations of direct measurement. The introduction of active laser scanning technology is a driving force for addressing several challenges, but its three-dimensional information has not been fully explored and utilized. In order to update the general knowledge and identify the possible error source, this study comprehensively reviews the temporal development, theoretical framework, and issues of indirect LAI measurement, followed by current methods, instruments, and platforms. Latest methods and instruments are introduced and compared to traditional ones. Current challenges, recent advances, and future perspectives are discussed to provide recommendations for further research.

ACS Style

Guangjian Yan; Ronghai Hu; Jinghui Luo; Marie Weiss; Hailan Jiang; Xihan Mu; Donghui Xie; Wuming Zhang. Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives. Agricultural and Forest Meteorology 2018, 265, 390 -411.

AMA Style

Guangjian Yan, Ronghai Hu, Jinghui Luo, Marie Weiss, Hailan Jiang, Xihan Mu, Donghui Xie, Wuming Zhang. Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives. Agricultural and Forest Meteorology. 2018; 265 ():390-411.

Chicago/Turabian Style

Guangjian Yan; Ronghai Hu; Jinghui Luo; Marie Weiss; Hailan Jiang; Xihan Mu; Donghui Xie; Wuming Zhang. 2018. "Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives." Agricultural and Forest Meteorology 265, no. : 390-411.

Journal article
Published: 14 October 2018 in Remote Sensing
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Interpreting remotely-sensed data requires realistic, but simple, models of radiative transfer that occurs within a vegetation canopy. In this paper, an improved version of the stochastic radiative transfer model (SRTM) is proposed by assuming that all photons that have not been specularly reflected enter the leaf interior. The contribution of leaf specular reflection is considered by modifying leaf scattering phase function using Fresnel reflectance. The canopy bidirectional reflectance factor (BRF) estimated from this model is evaluated through comparisons with field-measured maize BRF. The result shows that accounting for leaf specular reflection can provide better performance than that when leaf specular reflection is neglected over a wide range of view zenith angles. The improved version of the SRTM is further adopted to investigate the influence of leaf specular reflection on the canopy radiative regime, with emphases on vertical profiles of mean radiation flux density, canopy absorptance, BRF, and normalized difference vegetation index (NDVI). It is demonstrated that accounting for leaf specular reflection can increase leaf albedo, which consequently increases canopy mean upward/downward mean radiation flux density and canopy nadir BRF and decreases canopy absorptance and canopy nadir NDVI when leaf angles are spherically distributed. The influence is greater for downward/upward radiation flux densities and canopy nadir BRF than that for canopy absorptance and NDVI. The results provide knowledge of leaf specular reflection and canopy radiative regime, and are helpful for forward reflectance simulations and backward inversions. Moreover, polarization measurements are suggested for studies of leaf specular reflection, as leaf specular reflection is closely related to the canopy polarization.

ACS Style

Bin Yang; Yuri Knyazikhin; Donghui Xie; Haimeng Zhao; Junqiang Zhang; Yi Wu. Influence of Leaf Specular Reflection on Canopy Radiative Regime Using an Improved Version of the Stochastic Radiative Transfer Model. Remote Sensing 2018, 10, 1632 .

AMA Style

Bin Yang, Yuri Knyazikhin, Donghui Xie, Haimeng Zhao, Junqiang Zhang, Yi Wu. Influence of Leaf Specular Reflection on Canopy Radiative Regime Using an Improved Version of the Stochastic Radiative Transfer Model. Remote Sensing. 2018; 10 (10):1632.

Chicago/Turabian Style

Bin Yang; Yuri Knyazikhin; Donghui Xie; Haimeng Zhao; Junqiang Zhang; Yi Wu. 2018. "Influence of Leaf Specular Reflection on Canopy Radiative Regime Using an Improved Version of the Stochastic Radiative Transfer Model." Remote Sensing 10, no. 10: 1632.

Journal article
Published: 24 August 2018 in Remote Sensing of Environment
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The retrieval of vegetation parameters benefits significantly from the data fusion of optical and microwave signals. The integration of accurate forward models in both regions can play an important role in supporting these fusion approaches. Because of the different imaging mechanisms used in optical and microwave wavelength domains, the forward models in the two domains have been generally developed separately based on the different specifications of the scene. The inconsistencies between optical and microwave models, such as confusing input/output parameter definitions, different scattering theories and discrepant model complexity, make the data fusion difficult to conduct and lead to different results in terms of accuracy and computer time. Therefore, it is of great interest to develop a unified three-dimensional (3D) model using one scattering theory, identical input and similar complexity. To our knowledge, there are very few 3D models that can accomplish this task. By extending the Radiosity Applicable to Porous IndiviDual Objects (RAPID) model for the optical region, a general radiosity model (RAPID2) was proposed in this paper for the microwave region. This is the first time radiosity theory has been applied in microwave remote sensing, which invents a new way to solve the radar multiple scattering more efficiently. RAPID2 has four new functions: projecting translucent objects, tracking specular scattering, separating polarization components and imaging radar signals. The relationship between the radar cross section (RCS) and the bi-directional reflectance factor (BRF) is bridged. The modified Stokes vector and Mueller matrix are integrated into radiosity formulas to unify the scattering process between the optical and microwave regions. RAPID2 can simulate double-bouncing and multiple scattering effects over vegetated 3D scenes containing topography. The simulated radar images can well reflect the distinct radar geometric features, including layover, foreshortening and shadows. Validation over two forest sites shows good agreement with AIRSAR backscattering data (errors < 3.4 dB). The demonstrated results show the importance of the incident azimuth angle (variation up to 2 dB), slope (variation up to 5 dB), and multiple scattering effects (contribution up to 2 dB), which should be considered in forest parameter inversion.

ACS Style

Huaguo Huang; Zhiyu Zhang; Wenjian Ni; Linna Chai; Wenhan Qin; Guang Liu; Donghui Xie; Lingmei Jiang; Qinhuo Liu. Extending RAPID model to simulate forest microwave backscattering. Remote Sensing of Environment 2018, 217, 272 -291.

AMA Style

Huaguo Huang, Zhiyu Zhang, Wenjian Ni, Linna Chai, Wenhan Qin, Guang Liu, Donghui Xie, Lingmei Jiang, Qinhuo Liu. Extending RAPID model to simulate forest microwave backscattering. Remote Sensing of Environment. 2018; 217 ():272-291.

Chicago/Turabian Style

Huaguo Huang; Zhiyu Zhang; Wenjian Ni; Linna Chai; Wenhan Qin; Guang Liu; Donghui Xie; Lingmei Jiang; Qinhuo Liu. 2018. "Extending RAPID model to simulate forest microwave backscattering." Remote Sensing of Environment 217, no. : 272-291.

Journal article
Published: 19 July 2018 in Remote Sensing
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Spatial and temporal data fusion approaches have been developed to fuse reflectance imagery from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS), which have complementary spatial and temporal sampling characteristics. The approach relies on using Landsat and MODIS image pairs that are acquired on the same day to estimate Landsat-scale reflectance on other MODIS dates. Previous studies have revealed that the accuracy of data fusion results partially depends on the input image pair used. The selection of the optimal image pair to achieve better prediction of surface reflectance has not been fully evaluated. This paper assesses the impacts of Landsat-MODIS image pair selection on the accuracy of the predicted land surface reflectance using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) over different landscapes. MODIS images from the Aqua and Terra platforms were paired with images from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) to make different pair image combinations. The accuracy of the predicted surface reflectance at 30 m resolution was evaluated using the observed Landsat data in terms of mean absolute difference, root mean square error and correlation coefficient. Results show that the MODIS pair images with smaller view zenith angles produce better predictions. As expected, the image pair closer to the prediction date during a short prediction period produce better prediction results. For prediction dates distant from the pair date, the predictability depends on the temporal and spatial variability of land cover type and phenology. The prediction accuracy for forests is higher than for crops in our study areas. The Normalized Difference Vegetation Index (NDVI) for crops is overestimated during the non-growing season when using an input image pair from the growing season, while NDVI is slightly underestimated during the growing season when using an image pair from the non-growing season. Two automatic pair selection strategies are evaluated. Results show that the strategy of selecting the MODIS pair date image that most highly correlates with the MODIS image on the prediction date produces more accurate predictions than the nearest date strategy. This study demonstrates that data fusion results can be improved if appropriate image pairs are used.

ACS Style

Donghui Xie; Feng Gao; Liang Sun; Martha Anderson. Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs. Remote Sensing 2018, 10, 1142 .

AMA Style

Donghui Xie, Feng Gao, Liang Sun, Martha Anderson. Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs. Remote Sensing. 2018; 10 (7):1142.

Chicago/Turabian Style

Donghui Xie; Feng Gao; Liang Sun; Martha Anderson. 2018. "Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs." Remote Sensing 10, no. 7: 1142.

Journal article
Published: 22 June 2018 in IEEE Transactions on Geoscience and Remote Sensing
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Estimation of daily downward shortwave radiation (DSR) is of great importance in global energy budget and climatic modeling. The combination of satellite-based instantaneous measurements and temporal extrapolation models is the most feasible way to capture daily radiation variations at large scales. However, previous studies did not pay enough attention to topographic effects and simple temporal extrapolation methods were applied directly to rugged terrains which cover a large amount of the land surface. This paper, divided into two parts, aims at analyzing the topographic uncertainties of existing models and proposing a better method based on a mountain radiative transfer (MRT) model to calculate daily DSR. As the first part, this paper analyze the spatiotemporal variations of DSR influenced by topographic effects and checks the applicability of three temporal extrapolation methods on cloud-free days. Considering that clouds also have a strong influence on solar radiation, cloud-free days are chosen for targeted analysis of topographic effects on DSR. Three indices, the coefficient of variation, entropy-based dispersion coefficient (CH), and sill of semivariogram, are put forward to give a quantitative description of spatial heterogeneity. Our results show that the topography can dramatically strengthen the spatial heterogeneity of DSR. The index, CH, has an advantage for quantifying spatial heterogeneity as it offers a tradeoff between accuracy and efficiency. Spatial heterogeneity distorts the daily variation of DSR. Application of extrapolation methods in rugged terrains leads to overestimation of daily average DSR up to 60 W/m2 and a maximum 200 W/m2 error of instantaneous DSR on cloud-free days. This paper makes a quantitative analysis of topographic effects under different spatiotemporal conditions, which lays the foundation for developing a new extrapolation method.

ACS Style

Guangjian Yan; Yiyi Tong; Kai Yan; Xihan Mu; Qing Chu; Yingji Zhou; Yanan Liu; Jianbo Qi; Linyuan Li; Yelu Zeng; Hongmin Zhou; Donghui Xie; Wuming Zhang. Temporal Extrapolation of Daily Downward Shortwave Radiation Over Cloud-Free Rugged Terrains. Part 1: Analysis of Topographic Effects. IEEE Transactions on Geoscience and Remote Sensing 2018, 56, 6375 -6394.

AMA Style

Guangjian Yan, Yiyi Tong, Kai Yan, Xihan Mu, Qing Chu, Yingji Zhou, Yanan Liu, Jianbo Qi, Linyuan Li, Yelu Zeng, Hongmin Zhou, Donghui Xie, Wuming Zhang. Temporal Extrapolation of Daily Downward Shortwave Radiation Over Cloud-Free Rugged Terrains. Part 1: Analysis of Topographic Effects. IEEE Transactions on Geoscience and Remote Sensing. 2018; 56 (11):6375-6394.

Chicago/Turabian Style

Guangjian Yan; Yiyi Tong; Kai Yan; Xihan Mu; Qing Chu; Yingji Zhou; Yanan Liu; Jianbo Qi; Linyuan Li; Yelu Zeng; Hongmin Zhou; Donghui Xie; Wuming Zhang. 2018. "Temporal Extrapolation of Daily Downward Shortwave Radiation Over Cloud-Free Rugged Terrains. Part 1: Analysis of Topographic Effects." IEEE Transactions on Geoscience and Remote Sensing 56, no. 11: 6375-6394.

Journal article
Published: 28 April 2018 in Remote Sensing
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Many studies have been focusing on reconstructing the branch skeleton of a three-dimensional (3D) tree structure that is based on photos or point clouds scanned by a terrestrial laser scanner (TLS), but leaves, as the important component of a tree, are often ignored or simplified because of their complexity. Therefore, we develop a voxel-based method to add leaves to a reconstructed 3D branches structure based on TLS point clouds. The location and size of each leaf depend on the spatial distribution and density of leaves points in the voxel. We reconstruct a small 3D scene with four realistic 3D trees and a virtual tree (including trunk, branches, and leaves), and validate the structure of each tree through the directional gap fractions calculated based on simulated point clouds of this reconstructed scene by the ray-tracing algorithm. The results show good coherence with those from measured point clouds data. The relative errors of the directional gap fractions are no more than 4.1%, though the method is limited by the effects of point occlusion. Therefore, this method is shown to give satisfactory consistency both visually and in the quantitative evaluation of the 3D structure.

ACS Style

Donghui Xie; Xiangyu Wang; Jianbo Qi; Yiming Chen; Xihan Mu; Wuming Zhang; Guangjian Yan. Reconstruction of Single Tree with Leaves Based on Terrestrial LiDAR Point Cloud Data. Remote Sensing 2018, 10, 686 .

AMA Style

Donghui Xie, Xiangyu Wang, Jianbo Qi, Yiming Chen, Xihan Mu, Wuming Zhang, Guangjian Yan. Reconstruction of Single Tree with Leaves Based on Terrestrial LiDAR Point Cloud Data. Remote Sensing. 2018; 10 (5):686.

Chicago/Turabian Style

Donghui Xie; Xiangyu Wang; Jianbo Qi; Yiming Chen; Xihan Mu; Wuming Zhang; Guangjian Yan. 2018. "Reconstruction of Single Tree with Leaves Based on Terrestrial LiDAR Point Cloud Data." Remote Sensing 10, no. 5: 686.

Journal article
Published: 26 March 2018 in IEEE Transactions on Geoscience and Remote Sensing
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The airborne laser scanner (ALS) provides great potential for mapping the leaf area index (LAI) at the landscape scale using grid cell statistics, while its application is restricted by the lack of clumping information, which has been an unsolved issue highlighted for a long time. ALS generally provides an effective LAI because its footprint is too large to capture small gaps to apply traditional ground-based clumping correction methods. Here, we present a grid cell method based on path length distribution model to calculate the clumping-corrected LAI using ALS data without the requirement of additional field measurements. We separated the within- and between-crown areas to consider between-crown clumping, and used the path length distribution as estimated by local canopy height distribution to consider 3-D foliage profile and within-crown clumping. The path length distribution model takes advantage of the 3-D information rather than the gap size distribution, thus avoiding the limitation of large ALS footprint. With the 0.4-m-footprint ALS data, the results are generally promising and a multilevel clumping analysis is consistent with landscape flown. The ALS LAIs of different resolutions are consistent, with a difference of less than 5% from 5- to 250-m resolutions. Due to its consistency and simple configuration, the method provides an opportunity to map the clumping-corrected LAI operationally and strengthens the ability of airborne lidar to monitor vegetation change and validate the satellite product. This grid cell method based on path length distribution is worth further testing and application using more recent laser technology.

ACS Style

Ronghai Hu; Guangjian Yan; Francoise Nerry; Yunshu Liu; Yumeng Jiang; Shuren Wang; Yiming Chen; Xihan Mu; Wuming Zhang; Donghui Xie. Using Airborne Laser Scanner and Path Length Distribution Model to Quantify Clumping Effect and Estimate Leaf Area Index. IEEE Transactions on Geoscience and Remote Sensing 2018, 56, 3196 -3209.

AMA Style

Ronghai Hu, Guangjian Yan, Francoise Nerry, Yunshu Liu, Yumeng Jiang, Shuren Wang, Yiming Chen, Xihan Mu, Wuming Zhang, Donghui Xie. Using Airborne Laser Scanner and Path Length Distribution Model to Quantify Clumping Effect and Estimate Leaf Area Index. IEEE Transactions on Geoscience and Remote Sensing. 2018; 56 (6):3196-3209.

Chicago/Turabian Style

Ronghai Hu; Guangjian Yan; Francoise Nerry; Yunshu Liu; Yumeng Jiang; Shuren Wang; Yiming Chen; Xihan Mu; Wuming Zhang; Donghui Xie. 2018. "Using Airborne Laser Scanner and Path Length Distribution Model to Quantify Clumping Effect and Estimate Leaf Area Index." IEEE Transactions on Geoscience and Remote Sensing 56, no. 6: 3196-3209.

Journal article
Published: 09 March 2018 in Remote Sensing
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Surface shortwave (SW) irradiation is the primary driving force of energy exchange in the atmosphere and land interface. The global climate is profoundly influenced by irradiation changes due to the special climatic condition in Antarctica. Remote-sensing retrieval can offer only the instantaneous values in an area, whilst daily cycle and average values are necessary for further studies and applications, including climate change, ecology, and land surface process. When considering the large values of and small diurnal changes of solar zenith angle and cloud coverage, we develop two methods for the temporal extension of remotely sensed downward SW irradiance over Antarctica. The first one is an improved sinusoidal method, and the second one is an interpolation method based on cloud fraction change. The instantaneous irradiance data and cloud products are used in both methods to extend the diurnal cycle, and obtain the daily average value. Data from South Pole and Georg von Neumayer stations are used to validate the estimated value. The coefficient of determination (R2) between the estimated daily averages and the measured values based on the first method is 0.93, and the root mean square error (RMSE) is 32.21 W/m2 (8.52%). As for the traditional sinusoidal method, the R2 and RMSE are 0.68 and 70.32 W/m2 (18.59%), respectively The R2 and RMSE of the second method are 0.96 and 25.27 W/m2 (6.98%), respectively. These values are better than those of the traditional linear interpolation (0.79 and 57.40 W/m2 (15.87%)).

ACS Style

Yingji Zhou; Guangjian Yan; Jing Zhao; Qing Chu; Yanan Liu; Kai Yan; Yiyi Tong; Xihan Mu; Donghui Xie; Wuming Zhang. Estimation of Daily Average Downward Shortwave Radiation over Antarctica. Remote Sensing 2018, 10, 422 .

AMA Style

Yingji Zhou, Guangjian Yan, Jing Zhao, Qing Chu, Yanan Liu, Kai Yan, Yiyi Tong, Xihan Mu, Donghui Xie, Wuming Zhang. Estimation of Daily Average Downward Shortwave Radiation over Antarctica. Remote Sensing. 2018; 10 (3):422.

Chicago/Turabian Style

Yingji Zhou; Guangjian Yan; Jing Zhao; Qing Chu; Yanan Liu; Kai Yan; Yiyi Tong; Xihan Mu; Donghui Xie; Wuming Zhang. 2018. "Estimation of Daily Average Downward Shortwave Radiation over Antarctica." Remote Sensing 10, no. 3: 422.