Human impact on suspended particulate matter in the Yellow River Estuary, China: Evidence from remote sensing data fusion using an improved spatiotemporal fusion method

Li, Peng; Ke, Yinghai; Wang, Dawei; Ji, Hongyu; Chen, Shenliang; Chen, Mengmeng; Lyu, Mingyuan; Zhou, Demin

doi:10.1016/j.scitotenv.2020.141612

Cited by 41 publications

(20 citation statements)

References 51 publications

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“…In this paper, fusion studies of MODIS-Landsat 8 and MODIS-Sentinel 2 were carried out to evaluate the consistency of the fused images to achieve results consistent with Li et al [22]. In addition to obtaining results that align with previous studies, this project undertook the following: (1) selecting two spatiotemporal fusion models, ESTARFM and FSDAF, and comparing their applicability in the Ebinur Lake area; (2) two strategies, "fusion first" and "inversion first", were employed to explore the feasibility of the fused images to invert SPM; (3) at the image level, the consistency between the original Landsat 8, Sentinel 2, and their fused images was examined; (4) the consistency between the original image and the fused image for the SPM inversion was explored.…”

Section: Discussionmentioning

confidence: 92%

“…However, only a few studies have explored the applicability of the spatiotemporal fusion algorithm in high-frequency SPM monitoring [21]. As a result, the retrieval of SPM concentration data using the spatiotemporal fusion of multi-source remote-sensing data is extremely rare [22].…”

Section: Introductionmentioning

confidence: 99%

“…To resolve such problems, spatiotemporal fusion technology can effectively obtain high spatial resolution and continuous time-series data [26]. In addition to the fusion technology, scholars also intended to improve the spatiotemporal resolution of remote-sensing images in other perspectives such as improving the quality of the images [27,28], integrating multi-source satellites data [22], and developing CubeSat constellations [29]. The improvement of remote-sensing image quality can only play a role when the original image quality is poor.…”

Section: Introductionmentioning

confidence: 99%

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Feasibility of the Spatiotemporal Fusion Model in Monitoring Ebinur Lake’s Suspended Particulate Matter under the Missing-Data Scenario

et al. 2021

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High-frequency monitoring of suspended particulate matter (SPM) concentration can improve water resource management. Missing high-resolution satellite images could hamper remote-sensing SPM monitoring. This study resolved the problem by applying spatiotemporal fusion technology to obtain high spatial resolution and dense time-series data to fill image-data gaps. Three data sources (MODIS, Landsat 8, and Sentinel 2) and two spatiotemporal fusion methods (the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data fusion (FSDAF)) were used to reconstruct missing satellite images. We compared their fusion accuracy and verified the consistency of fusion images between data sources. For the fusion images, we used random forest (RF) and XGBoost as inversion methods and set “fusion first” and “inversion first” strategies to test the method’s feasibility in Ebinur Lake, Xinjiang, arid northwestern China. Our results showed that (1) the blue, green, red, and NIR bands of ESTARFM fusion image were better than FSDAF, with a good consistency (R2 ≥ 0.54) between the fused Landsat 8, Sentinel 2 images, and their original images; (2) the original image and fusion image offered RF inversion effect better than XGBoost. The inversion accuracy based on Landsat 8 and Sentinel 2 were R2 0.67 and 0.73, respectively. The correlation of SPM distribution maps of the two data sources attained a good consistency of R2 0.51; (3) in retrieving SPM from fused images, the “fusion first” strategy had better accuracy. The optimal combination was ESTARFM (Landsat 8)_RF and ESTARFM (Sentinel 2)_RF, consistent with original SPM maps (R2 = 0.38, 0.41, respectively). Overall, the spatiotemporal fusion model provided effective SPM monitoring under the image-absence scenario, with good consistency in the inversion of SPM. The findings provided the research basis for long-term and high-frequency remote-sensing SPM monitoring and high-precision smart water resource management.

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Section: Discussionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feasibility of the Spatiotemporal Fusion Model in Monitoring Ebinur Lake’s Suspended Particulate Matter under the Missing-Data Scenario

et al. 2021

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“…In a previous study, most of the riverine organic carbon originated from delta vegetation debris (Phragmites, Suaeda, and Tamarisk) in particulate form (Wang et al., 2018 ). Suspended particulate matter acts as the main carrier of organic matter, providing energy to the estuarine food web from upstream carrying, which plays an important role in the conditioning of productivity and ecosystem functions in estuaries (Li et al., 2020 ). As a representative of cordgrass in the intertidal zone, spartina provided a considerable food contribution proportion for estuarine fish communities in this study.…”

Section: Discussionmentioning

confidence: 99%

Natural distinction of carbon and nitrogen isotopic niches in common fish species across marine biotopes in the Yellow River estuary

Qü

Pang

et al. 2021

Ecology and Evolution

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Estuarine biotopes display distinct trophic structures of biocoenosis driven by the supply and transformation of multiple energy sources (Underwood, 2010), while flowing waters sustain riverine and marine biodiversity, and make important contributions to global biogeochemical cycles (Palmer & Ruhi, 2019). It has been widely recognized that most adjacent marine ecosystems are strongly connected due to the water transference of organic matter and

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“…Note that although revisions of FSDAF, i.e., IFSDAF, SFSDAF, and FSDAF 2.0 have been proposed, the framework of these hybrid STF methods is basically consistent with the original FSDAF. Moreover, FSDAF has been widely used [63][64][65][66], suggesting its effectiveness and representativeness. We thus used FSDAF as the representative method of the hybrid STF methods.…”

Section: Comparison and Evaluation Strategymentioning

confidence: 99%

A Hybrid Deep Learning-Based Spatiotemporal Fusion Method for Combining Satellite Images with Different Resolutions

et al. 2021

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Spatiotemporal fusion (STF) is considered a feasible and cost-effective way to deal with the trade-off between the spatial and temporal resolution of satellite sensors, and to generate satellite images with high spatial and high temporal resolutions. This is achieved by fusing two types of satellite images, i.e., images with fine temporal but rough spatial resolution, and images with fine spatial but rough temporal resolution. Numerous STF methods have been proposed, however, it is still a challenge to predict both abrupt landcover change, and phenological change, accurately. Meanwhile, robustness to radiation differences between multi-source satellite images is crucial for the effective application of STF methods. Aiming to solve the abovementioned problems, in this paper we propose a hybrid deep learning-based STF method (HDLSFM). The method formulates a hybrid framework for robust fusion with phenological and landcover change information with minimal input requirements, and in which a nonlinear deep learning-based relative radiometric normalization, a deep learning-based superresolution, and a linear-based fusion are combined to address radiation differences between different types of satellite images, landcover, and phenological change prediction. Four comparative experiments using three popular STF methods, i.e., spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), and Fit-FC, as benchmarks demonstrated the effectiveness of the HDLSFM in predicting phenological and landcover change. Meanwhile, HDLSFM is robust for radiation differences between different types of satellite images and the time interval between the prediction and base dates, which ensures its effectiveness in the generation of fused time-series data.

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Human impact on suspended particulate matter in the Yellow River Estuary, China: Evidence from remote sensing data fusion using an improved spatiotemporal fusion method

Cited by 41 publications

References 51 publications

Feasibility of the Spatiotemporal Fusion Model in Monitoring Ebinur Lake’s Suspended Particulate Matter under the Missing-Data Scenario

Feasibility of the Spatiotemporal Fusion Model in Monitoring Ebinur Lake’s Suspended Particulate Matter under the Missing-Data Scenario

Natural distinction of carbon and nitrogen isotopic niches in common fish species across marine biotopes in the Yellow River estuary

A Hybrid Deep Learning-Based Spatiotemporal Fusion Method for Combining Satellite Images with Different Resolutions

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