scholarly journals Thermal Inertia-Based Method for Estimating Soil Moisture

Soil Moisture ◽  
2019 ◽  
Author(s):  
Dai Matsushima
Keyword(s):  
Soil Moisture ◽  
Thermal Inertia

scholarly journals Soil moisture mapping based on thermal inertia from NOAA-11satellite AVHRR measurements.

1990 ◽  
Vol 99 (5) ◽  
pp. 483-489
Author(s):  
Yojiro UTSUNOMIYA ◽  
Huachang ZHAO ◽  
Runkuei HUA ◽  
Bingxi YU
Keyword(s):  
Soil Moisture ◽  
Thermal Inertia

Monitoring soil moisture by apparent thermal inertia method

2012 ◽  
Vol 19 (5) ◽  
pp. 1157-1161 ◽  
Author(s):  
Shu-Cong YANG ◽  
Yan-Jun SHEN ◽  
Ying GUO ◽  
Akihiko KONDOH
Keyword(s):  
Soil Moisture ◽  
Thermal Inertia

Soil moisture estimation from high-resolution UASs imagery based on machine learning approaches for land cover classification

2021 ◽  
Author(s):  
Teresa Pizzolla ◽  
Silvano Fortunato Dal Sasso ◽  
Ruodan Zhuang ◽  
Alonso Pizarro ◽  
Salvatore Manfreda
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Soil Moisture ◽  
High Resolution ◽  
Thermal Inertia ◽  
Bare Soil ◽  
Unmanned Aerial Systems ◽  
Learning Approaches ◽  
Land Use Classification ◽  
Aerial Systems

<p>Soil moisture (SM) is an essential variable in the earth system as it influences water, energy and, carbon fluxes between the land surface and the atmosphere. The SM spatio-temporal variability requires detailed analyses, high-definition optics and fast computing approaches for near real-time SM estimation at different spatial scales. Remote Sensing-based Unmanned Aerial Systems (UASs) represents the actual solution providing low-cost approaches to meet the requirements of spatial, spectral and temporal resolutions [1; 3; 4]. In this context, a proper land use classification is crucial in order to discriminate the behaviors of vegetation and bare soil in such high-resolution imagery. Therefore, high-resolution UASs-based imagery requires a specific images classification approach also considering the illumination conditions. In this work, the land use classification was carried out using a methodology based on a combined machine learning approaches: k-means clustering algorithm for removing shadow pixels from UASs images and, binary classifier for vegetation filtering. This approach led to identifying the bare soil on which SM estimation was computed using the Apparent Thermal Inertia (ATI) method [2]. The estimated SM values were compared with field measurements obtaining a good correlation (R<sup>2</sup> = 0.80). The accuracy of the results shows good reliability of the procedure and allows extending the use of UASs also in unclassified areas and ungauged basins, where the monitoring of the SM is very complex.</p><p><strong>References</strong></p><p>[1] Manfreda, S., McCabe, M.F., Miller, P.E., Lucas, R., Pajuelo Madrigal, V., Mallinis, G., Ben Dor, E., Helman, D., Estes, L., Ciraolo, G., et al. On the Use of Unmanned Aerial Systems for Environmental Monitoring, Remote Sensing, 2018, 10, 641.</p><p>[2] Minacapilli, M., Cammalleri, C., Ciraolo, G., D’Asaro, F., Iovino, M., and Maltese, A. Thermal Inertia Modeling for Soil Surface Water Content Estimation: A Laboratory Experiment. Soil. Sci. Soc. Amer. J. 2012, vol.76, n.1, pp. 92–100</p><p>[3] Paruta, A., P. Nasta, G. Ciraolo, F. Capodici, S. Manfreda, N. Romano, E. Bendor, Y. Zeng, A. Maltese, S. F. Dal Sasso and R. Zhuang, A geostatistical approach to map near-surface soil moisture through hyper-spatial resolution thermal inertia, IEEE Transactions on Geoscience and Remote Sensing, 2020.</p><p>[4] Petropoulos, G.P., A. Maltese, T. N. Carlson, G. Provenzano, A. Pavlides, G. Ciraolo, D. Hristopulos, F. Capodici, C. Chalkias, G. Dardanelli, S. Manfreda, Exploring the use of UAVs with the simplified “triangle” technique for Soil Water Content and Evaporative Fraction retrievals in a Mediterranean setting, International Journal of Remote Sensing, 2020.</p>

Soil moisture retrieval using thermal inertia, determined with visible and thermal spaceborne data, validated for European forests

2006 ◽  
Vol 101 (3) ◽  
pp. 299-314 ◽  
Author(s):  
Willem W. Verstraeten ◽  
Frank Veroustraete ◽  
Corné J. van der Sande ◽  
Ief Grootaers ◽  
Jan Feyen
Keyword(s):  
Soil Moisture ◽  
Thermal Inertia ◽  
European Forests
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