Study of a GIS-supported remote sensing method and a model for monitoring soil moisture at depth

Huailiang Chen; Xiangde Xu; Chunhui Zou

doi:10.1117/12.505312

22 December 2003 Study of a GIS-supported remote sensing method and a model for monitoring soil moisture at depth

Huailiang Chen, Xiangde Xu, Chunhui Zou

Proceedings Volume 5153, Ecosystems' Dynamics, Agricultural Remote Sensing and Modeling, and Site-Specific Agriculture; (2003) https://doi.org/10.1117/12.505312
Event: Optical Science and Technology, SPIE's 48th Annual Meeting, 2003, San Diego, California, United States

Abstract

Remote sensing techniques for monitoring soil moisture, e.g., that of thermal inertia, are confined to the top level of soil, generally with useful measurements only at the 0~20 cm interval due to the fact that the thermal inertia method is built mainly on the difference in daily temperature, part of whose patterns are limited largely to soil surface level without attacking its depth. The paper makes an approach to the problem, proposing a scheme and a model for estimating soil moisture at depth from NOAA/AVHRR sensings, based upon the apparent thermal inertia (ATI) and the aid of Geographic Information System (GIS), and with the effect of soil quality allowed for. Evidence suggests a rather high nonlinear relationship between the surface and deep levels of soil and its model is in the form S=Ax(d-d₀)+S₀x[1+Bx(d-d₀)²]+S_c, with which to estimate the water at depth by means of remotely sensed top-level moisture. As demonstrated in the practical applications to moisture sensing on a long-term and a multi-temporal phase basis in Henan Province, the developed model raises the mean accuracy by 5.5%~8.1% compared to the direct monitoring from satellite sensings of soil moisture at depth. On the other hand, owing to the limitation to the data of deep level moisture the water conditions at depth retrieved from the presented method and the developed model do not exceed 100 cm. And on land just irrigated or after rain the precision would be affected to noticeable degree because of the nonlinear relation available no longer.

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Huailiang Chen, Xiangde Xu, and Chunhui Zou "Study of a GIS-supported remote sensing method and a model for monitoring soil moisture at depth", Proc. SPIE 5153, Ecosystems' Dynamics, Agricultural Remote Sensing and Modeling, and Site-Specific Agriculture, (22 December 2003); https://doi.org/10.1117/12.505312

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KEYWORDS

Soil science

Data modeling

Remote sensing

Geographic information systems

Satellites

Humidity

Thermal modeling

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