scholarly journals Theoretical Analysis of a Thermal Inertia Model for Soil Moisture Estimation and its Application to Remote Sensing.

1992 ◽  
Vol 31 (4) ◽  
pp. 15-26 ◽  
Author(s):  
Yojiro UTSUNOMIYA
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Theoretical Analysis ◽  
Thermal Inertia ◽  
Inertia Model ◽  
Moisture Estimation

Estimation régionale de l’humidité du sol par télédétection

1996 ◽  
Vol 76 (3) ◽  
pp. 325-334 ◽  
Author(s):  
J. B. Boisvert ◽  
Y. Crevier ◽  
T. J. Pultz
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Incidence Angle ◽  
Thermal Inertia ◽  
Estimation Accuracy ◽  
Large Area ◽  
Dry Period ◽  
The Impact ◽  
Moisture Estimation ◽  
Canadian Research

Several pilot projects have demonstrated that estimation of soil moisture over a large area can be done using remote sensing. Three main methods have been tested with some success: thermal inertia, passive microwave and synthetic aperture radar (SAR). The advantages and limitations of each approach were summarized. Most Canadian research has focused on SAR data. It has shown that several parameters can affect the accuracy of soil moisture estimation using radar such as incidence angle, roughness, polarization and frequency. The data collected during the SIR-C/X-SAR experiment in Altona, Manitoba, were used to evaluate the impact of incidence angle on soil moisture estimation accuracy. Incidence angle was the most significant factor to explain the signal variations over time. The effect of incidence angle (38° to 58°) on the signal was linear in October. Correlation between soil moisture and the signal was higher with surface (0–2.5cm) measurements in the wet period (April) but there was no significant correlation during the dry period (October). A statistical model using soil moisture and incidence angle in April showed that an increase of 1° in incidence angle could decreased the C-HH signal by 0.25 dB and the L-HH signal by 0.30 dB. Such variation would generate a change of 2% (C-HH) and 5% (L-HH) in soil moisture estimation. Key words: Radar, remote sensing, soil moisture, microwave

Improving soil moisture estimation through a dual-cycle assimilation strategy

2020 ◽  
Author(s):  
Jiaxin Tian ◽  
Jun Qin ◽  
Kun Yang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Parameters ◽  
Significant Implication ◽  
Large Spatial Scale ◽  
Dual Cycle ◽  
Moisture Estimation

<p>Soil moisture plays a key role in land surface processes. Both remote sensing and model simulation have their respective limitations in the estimation of soil moisture on a large spatial scale. Data assimilation is a promising way to merge remote sensing observation and land surface model (LSM), thus having a potential to acquire more accurate soil moisture. Two mainstream assimilation algorithms (variational-based and sequential-based) both need model and observation uncertainties due to their great impact on assimilation results. Besides, as far as land surface models are concerned, model parameters have a significant implication for simulation. However, how to specify these two uncertainties and parameters has been confusing for a long time. A dual-cycle assimilation algorithm, which consists of two cycles, is proposed for addressing the above issue. In the outer cycle, a cost function is constructed and minimized to estimate model parameters and uncertainties in both model and observation. In the inner cycle, a sequentially based filtering method is implemented to estimate soil moisture with the parameters and uncertainties estimated in the outer cycle. For the illustration of the effectiveness of the proposed algorithm, the Advanced Microwave Scanning Radiometer of earth Observing System (AMSR-E) brightness temperatures are assimilated into land surface model with a radiative transfer model as the observation operator in three experimental fields, including Naqu and Ngari on the Tibetan Plateau, and Coordinate Enhanced Observing (CEOP) reference site on Mongolia. The results indicate that the assimilation algorithm can significantly improve soil moisture estimation.</p>

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>

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