Remote Sensing of Soil Moisture in Vineyards Using Airborne and Ground-Based Thermal Inertia Data
Abstract
:1. Introduction
2. Background
2.1. Thermal Inertia: Theoretical Background
2.2. Thermal Inertia Modification by a Vegetation Cover
3. Methods
3.1. Study Area
3.2. Fieldwork and Data Collection
3.3. Aerial Mapping of Thermal Inertia
3.4. Calculating Thermal Inertia
3.5. Statistical Analysis
4. Results
4.1. Aerial Thermal Imagery
4.2. Soil Physical Properties
4.3. Relationships between Field Measurements and Remote Sensing Imagery
4.4. Regression Relationships between Thermal Inertia and Soil Properties
5. Discussion
5.1. Thermal Inertia and Soil Properties
5.2. Advantages of Remotely-Sensed Thermal Inertia
5.3. Limitations of Field Estimated Thermal Inertia
5.4. Improving Moisture Retrieval Using Thermal Inertia
6. Conclusions
Acknowledgments
Conflict of Interest
References and Notes
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Measurement | Replicates | Proxy for |
---|---|---|
Bulk electrical conductivity (by EM38) | Horizontal mode perpendicular to vine rows Horizontal mode parallel to vine rows Vertical mode perpendicular to vine rows Vertical mode parallel to vine rows | Clay mineralogy and soil solution salinity given dry conditions |
Mechanical resistance (by soil penetrometer) | Right slice of the vine row Middle of vine row Left slice of the vine row | Soil bulk density, soil compaction and stoniness |
Soil dielectric constant (by Theta Probe) | Same as mechanical resistance sampling | Soil volumetric moisture content |
TIR emissions (by thermal infrared gun) | Same as mechanical resistance sampling | Surface temperature |
Flight Time | Estimated Surface Emissivity | Sky Temperature [°C] | Relative Humidity [%] | Air Temperature [°C] | Flight Height [m AGL] |
---|---|---|---|---|---|
6:50 p.m.–7:03 p.m. 17 September 2007 | 0.94 | −1.4 | 40.5 | 20.6 | ≈ 490 |
7:11 a.m.–7:33 a.m. September 18, 2007 | 0.94 | −3.4 | 78.8 | 13.1 | ≈ 490 |
Variable | Minimum | Lower Quartile | Median | Mean | Upper Quartile | Maximum |
---|---|---|---|---|---|---|
Apparent Electrical Conductivity Horizontal-Parallel Mode [mS/m] | 18.6 | 30.7 | 37.2 | 40.7 | 47.8 | 68.6 |
Volumetric soil moisture (Theta Probe) [%] | 9.7 | 12.7 | 15 | 14.5 | 16.3 | 19.4 |
Mechanical Resistance (average) [kPa] | 1,480 | 2,056 | 2,368 | 2,399 | 2,655 | 3,306 |
LST-sunset (field) [°C] | 14.2 | 14.5 | 14.6 | 14.6 | 14.8 | 15 |
LST-sunrise (field) [°C] | 9.6 | 10.1 | 10.2 | 10.2 | 10.4 | 10.7 |
Lst-sunset (remote) [°C] | 15.2 | 15.5 | 15.5 | 15.5 | 15.6 | 15.9 |
Lst-sunrise (remote) [°C] | 10.9 | 11.1 | 11.2 | 11.2 | 11.3 | 11.5 |
TIc (field) [J·m−2·K−1·s−1/2] | 2,894 | 2,894 | 3,357 | 3,410 | 3,609 | 3,872 |
TIc (remote) [J·m−2·K−1·s−1/2] | 3,068 | 3,287 | 3,348 | 3,361 | 3,430 | 3,581 |
Variables | Correlation Coefficient (p-value) |
---|---|
LST (remote), LST (field) at local sunrise | 0.075 (p = 0.721) |
LST (remote) vs. LST (field) at local sunset | 0.392 (p = 0.053) |
TIc (remote) vs. TIc (field) | 0.374 (p = 0.065) |
EM38 Horizontal Parallel | Theta Probe | Mechanical Resistance Average | Mechanical Resistance (0/1) | |
---|---|---|---|---|
TIc (field) | −0.057 | 0.392 | 0.108 | 0.699 |
TIc (remote) | −0.351 | 0.500 | 0.167 | 0.706 |
LST-sunset (field) | 0.199 | −0.274 | −0.326 | 0.688 |
LST-sunrise (field) | 0.085 | 0.316 | −0.066 | 0.614 |
Lst-sunset (remote) | 0.128 | −0.331 | −0.195 | 0.772 |
Lst-sunrise (remote) | −0.267 | 0.249 | −0.001 | 0.522 |
Response Variable | Intercept | Theta Probe | EM38 | Mechanical Resistance (0/1) | R2 (adjusted R2) | Residual Standard Deviation |
---|---|---|---|---|---|---|
TIc (field) | 2,863.72 | 34.57 | - | 138.17 | 0.224 | 227.2 |
[17.59] | [97.73] | (0.153) | ||||
(0.062) | (0.171) | |||||
TIc (remote) | 3,118.101 | 21.365 | −2.313 | 81.965 | 0.416 | 105.6 |
[8.265]* | [1.589] | [45.721] | (0.333) | |||
(0.01)) | (0.160) | (0.087) | ||||
TIc (field) In the mixed-effects model | 2,950.321 | 31.0470 | - | 139.143 | 0.167* | 287.9*** |
[11.385] | [86.944] | 0.624** | ||||
(0.009) | (0.116) |
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Soliman, A.; Heck, R.J.; Brenning, A.; Brown, R.; Miller, S. Remote Sensing of Soil Moisture in Vineyards Using Airborne and Ground-Based Thermal Inertia Data. Remote Sens. 2013, 5, 3729-3748. https://doi.org/10.3390/rs5083729
Soliman A, Heck RJ, Brenning A, Brown R, Miller S. Remote Sensing of Soil Moisture in Vineyards Using Airborne and Ground-Based Thermal Inertia Data. Remote Sensing. 2013; 5(8):3729-3748. https://doi.org/10.3390/rs5083729
Chicago/Turabian StyleSoliman, Aiman, Richard J. Heck, Alexander Brenning, Ralph Brown, and Stephen Miller. 2013. "Remote Sensing of Soil Moisture in Vineyards Using Airborne and Ground-Based Thermal Inertia Data" Remote Sensing 5, no. 8: 3729-3748. https://doi.org/10.3390/rs5083729