Remote Sensing Data Fusion to Evaluate Patterns of Regional Evapotranspiration: A Case Study for Dynamics of Film-Mulched Drip Irrigated Cotton in China's Manas River Basin Over 20 Years

Qiao, Xuejin; Yang, Guangyuan; Shi, Jianchu; Zuo, Qiting; Liu, Lining; Mu, Niu; Wu, Xun; Ben‐Gal, Alon

doi:10.2139/ssrn.4040665

Cited by 2 publications

(2 citation statements)

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“…In addition, the Fit_FC model fits the high-temporal and low-resolution data of the known and predicted periods at pixel scale, and directly applies the fitting coefficients to the high-resolution and low-temporal data. When the difference between high-resolution data and low-resolution data is large, the results of the Fit_FC model show obvious "block effect" [28,50].…”

Section: Fit_fc In Different Regionsmentioning

confidence: 99%

“…With the development of satellite technology and the improvements in sensor technology, the demand for high spatial resolution is increasing. However, research on spatiotemporal fusion using high-resolution images is scarce; in particular, the accuracy of the high-resolution fusion images is unknown [28][29][30][31][32]. At the same time, there is no research on the fusion accuracy of different models in different land use types in the current spatiotemporal fusion research.…”

Section: Introductionmentioning

confidence: 99%

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Applicability Analysis of GF-2PMS and PLANETSCOPE Data for Ground Object Recognition in Karst Region

Shen

Zhou

Yang

et al. 2022

Land

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Remote sensing image with high spatial and temporal resolution is very important for rational planning and scientific management of land resources. However, due to the influence of satellite resolution, revisit period, and cloud pollution, it is difficult to obtain high spatial and temporal resolution images. In order to effectively solve the “space–time contradiction” problem in remote sensing application, based on GF-2PMS (GF-2) and PlanetSope (PS) data, this paper compares and analyzes the applicability of FSDAF (flexible spatiotemporal data fusion), STDFA (the spatial temporal data fusion approach), and Fit_FC (regression model fitting, spatial filtering, and residual compensation) in different terrain conditions in karst area. The results show the following. (1) For the boundary area of water and land, the FSDAF model has the best fusion effect in land boundary recognition, and provides rich ground object information. The Fit_FC model is less effective, and the image is blurry. (2) For areas such as mountains, with large changes in vegetation coverage, the spatial resolution of the images fused by the three models is significantly improved. Among them, the STDFA model has the clearest and richest spatial structure information. The fused image of the Fit_FC model has the highest similarity with the verification image, which can better restore the coverage changes of crops and other vegetation, but the actual spatial resolution of the fused image is relatively poor, the image quality is fuzzy, and the land boundary area cannot be clearly identified. (3) For areas with dense buildings, such as cities, the fusion image of the FSDAF and STDFA models is clearer and the Fit_FC model can better reflect the changes in land use. In summary, compared with the Fit_FC model, the FSDAF model and the STDFA model have higher image prediction accuracy, especially in the recognition of building contours and other surface features, but they are not suitable for the dynamic monitoring of vegetation such as crops. At the same time, the image resolution of the Fit_FC model after fusion is slightly lower than that of the other two models. In particular, in the water–land boundary area, the fusion accuracy is poor, but the model of Fit_FC has unique advantages in vegetation dynamic monitoring. In this paper, three spatiotemporal fusion models are used to fuse GF-2 and PS images, which improves the recognition accuracy of surface objects and provides a new idea for fine classification of land use in karst areas.

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Section: Fit_fc In Different Regionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applicability Analysis of GF-2PMS and PLANETSCOPE Data for Ground Object Recognition in Karst Region

Shen

Zhou

Yang

et al. 2022

Land

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Soil Salinity Mapping of Croplands in Arid Areas Based on the Soil–Land Inference Model

Gao,

Wang,

et al. 2023

Agronomy

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Soil salinization can decrease soil productivity and is a significant factor in causing land degradation. Precision mapping of salinization in agricultural fields would improve farmland management. This study focuses on the cropland in the Manas River Basin, located in the arid region of northwest China. It explores the potential of a soil mapping method, the Soil–Land Inference Model (SoLIM), which only requires a small number of soil samples to infer soil salinization of farmlands in arid areas. The model was utilized to create spatial distribution maps of soil salinity for the years 2009 and 2017, and changes in the distribution were analyzed. The research results indicate: (1) Through the analysis of sample point data, it was observed that soil salinity in the study area tends to accumulate in the surface layer (0–30 cm) in spring and in the subsoil layer (60–90 cm) during the crop growing season, with significant spatial variability. Therefore, it is necessary to conduct detailed salinity mapping. (2) Using field measurements as validation data, the simulation results of the SoLIM were compared with spatial interpolation methods and regression models. The SoLIM showed higher inference accuracy, with R2 values for the simulation results of the three soil layers all exceeding 0.5. (3) The SoLIM spatial inference showed salt accumulation in the northern part and desalination in the southern part. The findings of this study suggest that the SoLIM has the potential to effectively map soil salinization of croplands in arid areas, offering an efficient solution for monitoring soil salinity in arid oasis croplands.

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Remote Sensing Data Fusion to Evaluate Patterns of Regional Evapotranspiration: A Case Study for Dynamics of Film-Mulched Drip Irrigated Cotton in China's Manas River Basin Over 20 Years

Cited by 2 publications

References 0 publications

Applicability Analysis of GF-2PMS and PLANETSCOPE Data for Ground Object Recognition in Karst Region

Applicability Analysis of GF-2PMS and PLANETSCOPE Data for Ground Object Recognition in Karst Region

Soil Salinity Mapping of Croplands in Arid Areas Based on the Soil–Land Inference Model

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