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A thermal inertia model for soil water content retrieval using thermal and multispectral images

  • Autori: Maltese, A.; Minacapilli, M.; Cammalleri, C.; Ciraolo, G.; D'Asaro, F.
  • Anno di pubblicazione: 2010
  • Tipologia: Proceedings (TIPOLOGIA NON ATTIVA)
  • Parole Chiave: remote sensing, termal inertial
  • OA Link:


Soil moisture is difficult to quantify because of its high spatial variability. Consequently, great efforts have been undertaken by the research community to develop practical remote sensing approaches to estimate the spatial distribution of surface soil moisture over large areas and with high spatial detail. Many methodologies have been developed using remote sensing data acquiring information in different parts of the electromagnetic spectrum. Conventional field measurement techniques (including gravimetric and time-domain reflectometry) are point-based, involve on-site operators, are time expensive and, in any case, do not provide exhaustive information on the spatial distribution of soil moisture because it strongly depends on pedology, soil roughness and vegetation cover. The technological development of imaging sensors acquiring in the visible (VIS), near infrared (NIR) and thermal infrared (TIR), renewed the research interest in setting up remote sensed based techniques aimed to retrieve soil water content variability in the soil-plant-atmosphere system (SPA). In this context different approaches have been widely applied at regional scale throughout synthetic indexes based on VIS, NIR and TIR spectral bands. A laboratory experiment has been carried out to verify a physically based model based on the remote estimation of the soil thermal inertia, P, to indirectly retrieve the soil surface water content, θ. The paper shows laboratory retrievals using simultaneously a FLIR A320G thermal camera, a six bands customized TETRACAM MCA II (Multiple Camera Array) multispectral camera working in the VIS/NIR part of the spectrum. Using these two type of sensors a set of VIS/NIR and TIR images were acquired as the main input dataset to retrieve the spatial variability of the thermal inertia values. Moreover, given that the accuracy of the proposed approach strongly depends on the accurate estimation of the soil thermal conductivity, a Decagon Device KD2 PRO thermal analyzer was used to verify the remotely estimate of thermal conductivity. Remotely estimated water contents were validated using the gravimetric method. The considered thermal inertia approach allowed prediction of the spatial distribution of the soil water with a satisfactory level of accuracy.