Spatiotemporal fusion for spectral remote sensing: A statistical analysis and review

Chen, Guangsheng; Lu, Hongbin; Zou, Weitao; Li, Linhui; Emam, Mahmoud; Chen, Xuebin; Jing, Weipeng; Wang, Jian; Li, Chao

doi:10.1016/j.jksuci.2023.02.021

Cited by 5 publications

(1 citation statement)

References 123 publications

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“…If data from disparate remote sensing sensors should be integrated, at this stage, techniques such as spatiotemporal fusion [66,67] may be used, which can combine the positive traits of multiple sensors. Moreover, the use of knowledge representation techniques such as geospatial ontologies [68,69] and semantic datacubes [70], which can represent both symbolic and numeric knowledge and share knowledge on the interpretation of these products, can provide a further option to semantically enhance the stored products.…”

Section: Data Integration and Fusionmentioning

confidence: 99%

Cognitive Soil Digital Twin for Monitoring the Soil Ecosystem: A Conceptual Framework

Tsakiridis,

Samarinas,

Kalopesa

et al. 2023

Soil Systems

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The digital twin concept has found widespread application across diverse industries. Herein, we present a comprehensive conceptual framework for the cognitive soil digital twin, which embodies the intricate physical reality of the soil ecosystem, aiding in its holistic monitoring and comprehension. The digital twin can seamlessly integrate a multitude of sensor data sources, including field Internet of Things sensors, remote sensing data, field measurements, digital cartography, surveys, and other Earth observation datasets. By virtue of its duality, this digital counterpart facilitates data organisation and rigorous analytical exploration, unravelling the intricacies of physical, chemical, and biological soil constituents while discerning their intricate interrelationships and their impact on ecosystem services. Its potential extends beyond mere data representation, acting as a versatile tool for scenario analysis and enabling the visualisation of diverse environmental impacts, including the effects of climate change and transformations in land use or management practices. Beyond academic circles, the digital twin’s utility extends to a broad spectrum of stakeholders across the entire quadruple helix, encompassing farmers and agronomists, soil researchers, the agro-industry, and policy-makers. By fostering collaboration among these stakeholders, the digital twin catalyses informed decision-making, underpinned by data-driven insights. Moreover, it acts as a testbed for the development of innovative sensors and monitoring frameworks, in addition to providing a platform that can educate users and the broader public using immersive and innovative visualisation tools, such as augmented reality. This innovative framework underscores the imperative of a holistic approach to soil ecosystem monitoring and management, propelling the soil science discipline into an era of unprecedented data integration and predictive modelling, by harnessing the effects of climate change towards the development of efficient decision-making.

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