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Dr. Bo Ping
Tianjin University

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Research Keywords & Expertise

0 China
0 Sea Surface Temperature (sst)
0 Ocean Remote Sensing, OCEAN BIG DATA
0 chlorophyll a
0 tianjin university

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Journal article
Published: 30 January 2021 in Remote Sensing
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Tianjin is the largest open city along the coastline in Northern China, which has several important wetland ecosystems. However, no systematic study has assessed the water body changes over the past few decades for Tianjin, not to mention their response to human activities and climate change. Here, based on the water change tracking (WCT) algorithm, we proposed an improved water change tracking (IWCT) algorithm, which could remove built-up shade noise (account for 0.4%~6.0% of the final water area) and correct omitted water pixels (account for 1.1%~5.1% of the final water area) by taking the time-series data into consideration. The seasonal water product of the Global Surface Water Data (GSWD) was used to provide a comparison with the IWCT results. Significant changes in water bodies of the selected area in Tianjin were revealed from the time-series water maps. The permanent water area of Tianjin decreased 282.5 km2 from 1984 to 2019. Each time after the dried-up period, due to government policies, the land reclamation happened in Tuanbo Birds Nature Reserve (TBNR), and, finally, 12.6 km2 of the lake has been reclaimed. Meanwhile, 488.6 km2 of land has been reclaimed from the sea along the coastal zone in the past 16 years at a speed of 28.74 km2 yr−1 in the Binhai New Area (BHNA). The method developed in this study could be extended to other sensors which have similar band settings with Landsat; the products acquired in this study could provide fundamental reference for the wetland management in Tianjin.

ACS Style

Xingxing Han; Wei Chen; Bo Ping; Yong Hu. Implementation of an Improved Water Change Tracking (IWCT) Algorithm: Monitoring the Water Changes in Tianjin over 1984–2019 Using Landsat Time-Series Data. Remote Sensing 2021, 13, 493 .

AMA Style

Xingxing Han, Wei Chen, Bo Ping, Yong Hu. Implementation of an Improved Water Change Tracking (IWCT) Algorithm: Monitoring the Water Changes in Tianjin over 1984–2019 Using Landsat Time-Series Data. Remote Sensing. 2021; 13 (3):493.

Chicago/Turabian Style

Xingxing Han; Wei Chen; Bo Ping; Yong Hu. 2021. "Implementation of an Improved Water Change Tracking (IWCT) Algorithm: Monitoring the Water Changes in Tianjin over 1984–2019 Using Landsat Time-Series Data." Remote Sensing 13, no. 3: 493.

Journal article
Published: 03 December 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Deep learning-based super-resolution (SR) methods have been widely used in natural images; however, their applications in satellite-derived sea surface temperature (SST) have not yet been fully discussed. Hence, it is necessary to analyze the validity of deep learning-based SR methods in SST reconstruction. In this study, an SR model, including multiscale feature extraction and multireceptive field mapping, was first proposed. Then, the proposed model and four other existing SR models were applied to SST reconstruction and analyzed. First, compared with the bicubic interpolation method, the SR models can improve the reconstruction accuracy. Compared with four other SR models, the proposed model can achieve the lowest mean squared error (MAE) in the East China Sea (ECS), in the northwest Pacific (NWP) and in the west Atlantic (WA), the second-lowest MAE in the southeast Pacific (SEP); the lowest root mean squared error (RMSE) in ECS and WA, the second-lowest RMSE in NWP and SEP. Additionally, ODRE model can acquire the highest or the second-highest peak single-to-noise ratio (PSNR) and structural similarity index (SSIM) in ECS, NWP and SEP. Moreover, the number of missing pixels and SST variety are two essential factors in the SR performance. The proposed multiscale feature extraction process can enhance the SR performance, especially for small regions and stable SST regions. Finally, while a deeper network can be helpful in achieving SR performance, the approach of simply adding more dilation convolutions may not enhance the reconstruction accuracy.

ACS Style

Bo Ping; Fenzhen Su; Xingxing Han; Yunshan Meng. Applications of Deep Learning-Based Super-Resolution for Sea Surface Temperature Reconstruction. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 14, 887 -896.

AMA Style

Bo Ping, Fenzhen Su, Xingxing Han, Yunshan Meng. Applications of Deep Learning-Based Super-Resolution for Sea Surface Temperature Reconstruction. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 14 (99):887-896.

Chicago/Turabian Style

Bo Ping; Fenzhen Su; Xingxing Han; Yunshan Meng. 2020. "Applications of Deep Learning-Based Super-Resolution for Sea Surface Temperature Reconstruction." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 887-896.

Journal article
Published: 22 April 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Cloud and cloud shadow detection can be considered as a classification process, in which the objective pixels are partitioned into cloud/cloud shadow or non-cloud/non-cloud shadow classes. However, some cloud pixels, especially the thin cloud pixels, can be considered as a mixture of reflectances of clouds and land objects. Fuzzy clustering may better characterize the status of one given pixel belonging to clouds or non-clouds. The fuzzy c-means method (FCM) was utilized in this study for cloud and cloud shadow detection. In addition, the “flood-fill” morphological transformation may misclassify some clear-sky areas surrounded by clouds as cloud shadows as a whole, so a modified cloud shadow index calculation was proposed. Moreover, a cloud and cloud shadow spatial matching strategy based on the projection direction and spatial coexistence was used to exclude some pseudo cloud shadows. Fewer predefined parameters and spectral bands are needed is one characteristic of the proposed method. In this study, 41 scenes with percentages of cloud cover from 4.99% to 97.63%, were utilized to validate of the FCM. The detected results demonstrate that the thick and thin clouds along with their associated cloud shadows can be precisely extracted by using the FCM. Compared with Fmask method, the FCM has relatively lower producer agreement rates, but it misclassifies as clouds fewer clear-sky pixels; compared with SVM method, the FCM can achieve better cloud detection accuracy. The results demonstrate that the FCM can attain a better balance between cloud pixel detection and non-cloud pixel exclusion.

ACS Style

Ping Bo; Su Fenzhen; Meng Yunshan. A Cloud and Cloud Shadow Detection Method Based on Fuzzy c-Means Algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 1714 -1727.

AMA Style

Ping Bo, Su Fenzhen, Meng Yunshan. A Cloud and Cloud Shadow Detection Method Based on Fuzzy c-Means Algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):1714-1727.

Chicago/Turabian Style

Ping Bo; Su Fenzhen; Meng Yunshan. 2020. "A Cloud and Cloud Shadow Detection Method Based on Fuzzy c-Means Algorithm." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 1714-1727.

Journal article
Published: 05 June 2018 in Remote Sensing
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Landsat and MODIS data have been widely utilized in many remote sensing applications, however, the trade-off between the spatial resolution and temporal frequency has limited their capacities in monitoring detailed spatio-temporal dynamics. Spatio-temporal fusion methods based on a linear model that considers the differences between fine- and coarse-spatial-resolution images as linear can effectively solve this trade-off problem, yet the existing linear fusion methods either regard the coefficients of the linear model as constants or have adopted regression methods to calculate the coefficients, both of which may introduce some errors in the fusion process. In this paper, we proposed an enhanced linear spatio-temporal fusion method (ELSTFM) to improve the data fusion accuracy. In the ELSTFM, it is not necessary to calculate the slope of the linear model, and the intercept, which can be deemed as the residual caused by systematic biases, is calculated based on spectral unmixing theory. Additionally, spectrally similar pixels in a given fine-spatial-resolution pixel’s neighborhood and their corresponding weights were used in the proposed method to mitigate block effects. Landsat-7/ETM+ and 8-day composite MODIS reflectance data covering two study sites with heterogeneous and homogenous landscapes were selected to validate the proposed method. Compared to three other typical spatio-temporal fusion methods visually and quantitatively, the predicted images obtained from ELSTFM could acquire better results for the two selected study sites. Furthermore, the resampling methods used to resample MODIS to the same spatial resolution of Landsat could slightly, but did not significantly influence the fusion accuracy, and the distributions of slopes of different bands for the two study sites could all be deemed as normal distributions with a mean value close to 1. The performance of ELSTFM depends on the accuracy of residual calculation at fine-resolution and large landscape changes may influence the fusion accuracy.

ACS Style

Bo Ping; Yunshan Meng; Fenzhen Su. An Enhanced Linear Spatio-Temporal Fusion Method for Blending Landsat and MODIS Data to Synthesize Landsat-Like Imagery. Remote Sensing 2018, 10, 881 .

AMA Style

Bo Ping, Yunshan Meng, Fenzhen Su. An Enhanced Linear Spatio-Temporal Fusion Method for Blending Landsat and MODIS Data to Synthesize Landsat-Like Imagery. Remote Sensing. 2018; 10 (6):881.

Chicago/Turabian Style

Bo Ping; Yunshan Meng; Fenzhen Su. 2018. "An Enhanced Linear Spatio-Temporal Fusion Method for Blending Landsat and MODIS Data to Synthesize Landsat-Like Imagery." Remote Sensing 10, no. 6: 881.