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Digital Mapping of Soil Landscape Parameters pp 117–142Cite as

Spatial Soil Moisture Prediction Model Over an Agricultural Land

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Part of the book series: Studies in Big Data ((SBD,volume 72))

Abstract

Satellite remote sensing observations have the potential for efficient and reliable mapping of spatial soil moisture distributions. However, soil moisture retrievals from active/passive microwave remote sensing data are typically complex due to inherent difficulty in characterizing interactions among land surface parameters that contribute to the retrieval process. Therefore, adequate physical mathematical descriptions of microwave backscatter interaction with parameters such as land cover, vegetation density, and soil characteristics are not readily available. In such condition, non-parametric models could be used as a possible alternative for better understanding the impact of variables in the retrieval process and relating it in the absence of exact formulation. The following chapter will provide a conceptual framework for SMPM: A non-parametric approach over an agriculture land.

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Authors and Affiliations

  1. Geomatics Section, Civil Engineering Department, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Pradeep Kumar Garg & Rahul Dev Garg

  2. Surveying and Geomatics Section, Civil Engineering Department, Maharishi Markandeshwar University, Ambala, Haryana, India

    Gaurav Shukla

  3. Indian Institute of Remote Sensing (IIRS), Dehradun, Uttarakhand, India

    Hari Shanker Srivastava

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Garg, P.K., Garg, R.D., Shukla, G., Srivastava, H.S. (2020). Spatial Soil Moisture Prediction Model Over an Agricultural Land. In: Digital Mapping of Soil Landscape Parameters. Studies in Big Data, vol 72. Springer, Singapore. https://doi.org/10.1007/978-981-15-3238-2_6

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