Modeling and Mapping Soil Moisture of Plateau Pasture Using RADARSAT-2 Imagery
<p>Location of the study area in the Qinghai Lake watershed, northeastern Tibetan Plateau, China. Red and yellow rectangles indicate the location of the RADARSAT-2 images (full-polarimetric, fine quad mode) acquired on 29 September 2012 and 13 May 2013, respectively.</p> "> Figure 2
<p>RADARSAT-2 composite false RGB images (HH = red, HV = green, VV = blue) acquired at different dates: (<b>a</b>) 29 September 2012; (<b>b</b>) 13 May 2013. Both images were acquired in fine quad polarization mode, fully polarimetric. Red and yellow circles indicate the locations of the field measurements in September 2012 and May 2013, respectively.</p> "> Figure 3
<p>Flowchart of soil moisture retrieval.</p> "> Figure 4
<p>Comparison between measured and retrieved soil moisture using the two developed models for calibration: (<b>a</b>) Chen model in September 2012 (<span class="html-italic">R</span><sup>2</sup> = 0.57, RMSE = 4.81); (<b>b</b>) Chen model in May 2013 (<span class="html-italic">R</span><sup>2</sup> = 0.91, RMSE = 1.8); (<b>c</b>) Dubois model in September 2012 (<span class="html-italic">R</span><sup>2</sup> = 0.7, RMSE = 4.3); and (<b>d</b>) Dubois model in May 2013 (<span class="html-italic">R</span><sup>2</sup> = 0.88, RMSE = 2.1).</p> "> Figure 5
<p>Comparison between measured and retrieved soil moisture using the two models for validation: (<b>a</b>) Chen model in September 2012 (<span class="html-italic">R</span><sup>2</sup> = 0.64, RMSE = 6.56); (<b>b</b>) Chen model in May 2013 (<span class="html-italic">R</span><sup>2</sup> = 0.75, RMSE = 2.56); (<b>c</b>) Dubois model in September 2012 (<span class="html-italic">R</span><sup>2</sup> = 0.8, RMSE = 5.4); and (<b>d</b>) Dubois model in May 2013 (<span class="html-italic">R</span><sup>2</sup> = 0.78, RMSE = 3.05).</p> "> Figure 6
<p>SMC maps of the extended area (28 × 12 km<sup>2</sup>) produced by the developed Dubois model using two RADARSAT-2 images acquired at different dates: (<b>a</b>) 29 September 2012; (<b>b</b>) 13 May 2013. Water and urban areas are masked in white.</p> ">
Abstract
:1. Introduction
2. Study Area and Dataset
2.1. Study Area
2.2. SAR Data
Acquisition Date | Acquisition Time (UTC) | Scene Centre | Beam Mode | Incidence Angle (°) | Orbit | Polarization |
---|---|---|---|---|---|---|
29 September 2012 | 11:07:56 | 99:07:56r 29,2 | Fine quad-pol | 18.5–20.3 | Asc. | Full-pol |
13 May 2013 | 23:31:22 | 99:31:22013ol2 | Fine quad-pol | 28.1–29.8 | Des. | Full-pol |
2.3. Experimental Measurements
Date | Soil Surface Parameters | Min | Max | Average |
---|---|---|---|---|
September 2012 | RMS height (cm) | 0.4 | 5.1 | 1.2 |
Correlation length (cm) | 9.0 | 65 | 24.1 | |
May 2013 | RMS height (cm) | 0.2 | 1.6 | 0.7 |
Correlation length (cm) | 28.5 | 51.5 | 41.3 |
Date | Min | Max | Average | Standard Deviation |
---|---|---|---|---|
September 2012 | 10.5 | 42.3 | 24.4 | 8.2 |
May 2013 | 13.0 | 38.5 | 21.0 | 5.9 |
2.4. Ancillary Data
Acquisition Date | Acquisition Time(UTC) | Scene Centre | Sensor Type |
---|---|---|---|
12 September 2012 | 3:51:32 | 100°24′E/37°30′N | Landsat ETM+ |
18 May 2013 | 3:58:07 | 100°22′E/37°29′N | Landsat TM |
3. Methods Development
3.1. Parameterization of Vegetation Effect
3.2. Soil Moisture Retrieval Models for Bare Soil
3.2.1. Dubois Model
3.2.2. Chen Model
3.3. Soil Moisture Retrieval Models for Vegetated Soil
3.3.1. Development Based on Dubois Model
3.3.2. Development Based on Chen Model
3.4. Evaluation Indexes
4. Results and Discussion
4.1. Soil Moisture Retrieval
- (1)
- Pre-processing, classification, and masking of SAR data.
- (2)
- Parameterization of vegetation contribution in backscattering, based on NDWI, the water cloud model, the empirical relationship between VWC and NDWI, and development of new soil moisture.
- (3)
- Fitting of parameters in new soil moisture algorithms for vegetated areas on the basis of experiment data.
- (4)
- Generation of soil moisture map.
Date | Model | Performance | RMSE | R2 | RPD |
---|---|---|---|---|---|
September 2012 | Chen | calibration | 4.81 | 0.57 | 1.57 |
validation | 6.6 | 0.64 | 1.25 | ||
Dubois | calibration | 4.32 | 0.70 | 1.74 | |
validation | 5.40 | 0.80 | 1.60 | ||
May 2013 | Chen | calibration | 1.76 | 0.93 | 3.5 |
validation | 2.56 | 0.75 | 1.84 | ||
Dubois | calibration | 2.16 | 0.88 | 2.87 | |
validation | 3.05 | 0.78 | 1.74 |
4.2. Validation of Methods
4.3. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Dobson, M.C.; Ulaby, F.T.; Hallikainen, M.T.; El-Rayes, M.A. Microwave dielectric behavior of wet soil-part II: Dielectric mixing models. IEEE Trans. Geosci. Remote Sens. 1985. [Google Scholar] [CrossRef]
- Ulaby, F.; Moore, R.; Fung, A. Microwave Remote Sensing: Active and Passive, Vol. III: Scattering and Emission Theory, Advanced Systems and Applications; Artech House, Inc.: Dedham, MA, USA, 1986. [Google Scholar]
- Giacomelli, A.; Bacchiega, U.; Troch, P.A.; Mancini, M. Evaluation of surface soil moisture distribution by means of SAR remote sensing techniques and conceptual hydrological modelling. J. Hydrol. 1995, 166, 445–459. [Google Scholar] [CrossRef]
- Fung, A.K.; Li, Z.; Chen, K.S. Backscattering from a randomly rough dielectric surface. IEEE Trans. Geosci. Remote Sens. 1992, 30, 356–369. [Google Scholar] [CrossRef]
- Baghdadi, N.; Zribi, M.; Loumagne, C.; Ansart, P.; Anguela, T.P. Analysis of TerraSAR-X data and their sensitivity to soil surface parameters over bare agricultural fields. Remote Sens. Environ. 2008, 112, 4370–4379. [Google Scholar] [CrossRef]
- Oh, Y.; Sarabandi, K.; Ulaby, F.T. An empirical model and an inversion technique for radar scattering from bare soil surfaces. IEEE Trans. Geosci. Remote Sens. 1992, 30, 370–381. [Google Scholar] [CrossRef]
- Chen, K.S.; Yen, S.K.; Huang, W.P. A simple-model for retrieving bare soil-moisture from radar-scattering coefficients. Remote Sens. Environ. 1995, 54, 121–126. [Google Scholar] [CrossRef]
- Dubois, P.C.; Van Zyl, J.; Engman, T. Measuring soil moisture with imaging radars. IEEE Trans. Geosci. Remote Sens. 1995, 33, 915–926. [Google Scholar] [CrossRef]
- Ulaby, F.T.; Dubois, P.C.; van Zyl, J. Radar mapping of surface soil moisture. J. Hydrol. 1996, 184, 57–84. [Google Scholar] [CrossRef]
- Baghdadi, N.; Aubert, M.; Cerdan, O.; Franchistéguy, L.; Viel, C.; Eric, M.; Zribi, M.; Desprats, J. Operational mapping of soil moisture using synthetic aperture radar data: Application to the touch basin (France). Sensors 2007, 7, 2458–2483. [Google Scholar] [CrossRef]
- Du, J.Y.; Shi, J.C.; Sun, R.J. The development of HJ SAR soil moisture retrieval algorithm. Int. J. Remote Sens. 2010, 31, 3691–3705. [Google Scholar] [CrossRef]
- Srivastava, H.S.; Patel, P.; Navalgund, R.R.; Sharma, Y. Retrieval of surface roughness using multi-polarized ENVISAT-1 ASAR data. Geocarto Int. 2007, 23, 67–77. [Google Scholar] [CrossRef]
- Topp, G.C.; Davis, J.L.; Annan, A.P. Electromagnetic determination of soil water content: Measurements in coaxial transmission lines. Water Resour. Res. 1980, 16, 574–582. [Google Scholar] [CrossRef]
- Hallikainen, M.T.; Ulaby, F.T.; Dobson, M.C.; El-Rayes, M.A.; Lil-Kun, W. Microwave dielectric behavior of wet soil-part 1: Empirical models and experimental observations. IEEE Trans. Geosci. Remote Sens. 1985. [Google Scholar] [CrossRef]
- Fung, A.K. Microwave Scattering and Emission Models and their Applications; Artech House: Boston, MA, USA/London, UK, 1994. [Google Scholar]
- Zribi, M.; Dechambre, M. A new empirical model to retrieve soil moisture and roughness from C-band radar data. Remote Sens. Environ. 2003, 84, 42–52. [Google Scholar] [CrossRef]
- Baghdadi, N.; Abou Chaaya, J.; Zribi, M. Semiempirical calibration of the integral equation model for SAR data in C-band and cross polarization using radar images and field measurements. IEEE Geosci. Remote Sens. Lett. 2011, 8, 14–18. [Google Scholar] [CrossRef]
- Attema, E.P.W.; Ulaby, F.T. Vegetation modeled as a water cloud. Radio Sci. 1978, 13, 357–364. [Google Scholar] [CrossRef]
- Ulaby, F.T.; Sarabandi, K.; McDonald, K.; Whitt, M.; Dobson, M.C. Michigan microwave canopy scattering model. Int. J. Remote Sens. 1990, 11, 1223–1253. [Google Scholar] [CrossRef]
- Yueh, S.H.; Kong, J.A.; Jao, J.K.; Shin, R.T.; le Toan, T. Branching model for vegetation. IEEE Trans. Geosci. Remote Sens. 1992, 30, 390–402. [Google Scholar] [CrossRef]
- Picard, G.; le Toan, T.; Mattia, F. Understanding C-band radar backscatter from wheat canopy using a multiple-scattering coherent model. IEEE Trans. Geosci. Remote Sens. 2003, 41, 1583–1591. [Google Scholar] [CrossRef]
- Bindlish, R.; Barros, A.P. Parameterization of vegetation backscatter in radar-based, soil moisture estimation. Remote Sens. Environ. 2001, 76, 130–137. [Google Scholar] [CrossRef]
- Baghdadi, N.; Holah, N.; Zribi, M. Soil moisture estimation using multi-incidence and multi-polarization ASAR data. Int. J. Remote Sens. 2006, 27, 1907–1920. [Google Scholar] [CrossRef]
- Srivastava, H.S.; Patel, P.; Sharma, Y.; Navalgund, R.R. Large-area soil moisture estimation using multi-incidence-angle RADARSAT-1 SAR data. IEEE Trans. Geosci. Remote Sens. 2009, 47, 2528–2535. [Google Scholar] [CrossRef]
- Jagdhuber, T.; Hajnsek, I.; Bronstert, A.; Papathanassiou, K.P. Soil moisture estimation under low vegetation cover using a multi-angular polarimetric decomposition. IEEE Trans. Geosci. Remote Sens. 2013, 51, 2201–2215. [Google Scholar] [CrossRef]
- Ulaby, F.; Allen, C.; Eger Iii, G.; Kanemasu, E. Relating the microwave backscattering coefficient to leaf area index. Remote Sens. Environ. 1984, 14, 113–133. [Google Scholar] [CrossRef]
- Jackson, T.; Schmugge, T. Vegetation effects on the microwave emission of soils. Remote Sens. Environ. 1991, 36, 203–212. [Google Scholar] [CrossRef]
- De Roo, R.D.; Du, Y.; Ulaby, F.T.; Dobson, M.C. A semi-empirical backscattering model at L-band and C-band for a soybean canopy with soil moisture inversion. IEEE Trans. Geosci. Remote Sens. 2001, 39, 864–872. [Google Scholar]
- Yang, H.; Shi, J.; Li, Z.; Guo, H. Temporal and spatial soil moisture change pattern detection in an agricultural area using multi-temporal Radarsat ScanSAR data. Int. J. Remote Sens. 2006, 27, 4199–4212. [Google Scholar] [CrossRef]
- Gherboudj, I.; Magagi, R.; Berg, A.A.; Toth, B. Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data. Remote Sens. Environ. 2011, 115, 33–43. [Google Scholar] [CrossRef]
- Jiancheng, S.; Wang, J.; Hsu, A.Y.; O’Neill, P.E.; Engman, E.T. Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data. IEEE Trans. Geosci. Remote Sens. 1997, 35, 1254–1266. [Google Scholar] [CrossRef]
- Joseph, A.T.; van der Velde, R.; O’Neill, P.E.; Lang, R.; Gish, T. Effects of corn on C- and L-band radar backscatter: A correction method for soil moisture retrieval. Remote Sens. Environ. 2010, 114, 2417–2430. [Google Scholar] [CrossRef]
- Balenzano, A.; Mattia, F.; Satalino, G.; Davidson, M.W.J. Dense temporal series of C- and L-band SAR data for soil moisture retrieval over agricultural crops. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2011, 4, 439–450. [Google Scholar] [CrossRef]
- Hasan, S.; Montzka, C.; Rüdiger, C.; Ali, M.; Bogena, H.R.; Vereecken, H. Soil moisture retrieval from airborne L-band passive microwave using high resolution multispectral data. ISPRS J. Photogramm. Remote Sens. 2014, 91, 59–71. [Google Scholar] [CrossRef]
- Ballester Berman, J.D.; Vicente Guijalba, F.; López Sánchez, J.M. Polarimetric SAR model for soil moisture estimation over vineyards at C-band. Prog. Electromagn. Res. 2013, 142, 639–665. [Google Scholar] [CrossRef]
- Luckman, A.J. The effects of topography on mechanisms of radar backscatter from coniferous forest and upland pasture. IEEE Trans. Geosci. Remote Sens. 1998, 36, 1830–1834. [Google Scholar] [CrossRef]
- Paloscia, S.; Pampaloni, P.; Pettinato, S.; Santi, E. Generation of soil moisture maps from ENVISAT/ASAR images in mountainous areas: A case study. Int. J. Remote Sens. 2010, 31, 2265–2276. [Google Scholar] [CrossRef]
- Pasolli, L.; Notarnicola, C.; Bruzzone, L.; Bertoldi, G.; Chiesa, S.D.; Niedrist, G.; Tappeiner, U.; Zebisch, M. Polarimetric RADARSAT-2 imagery for soil moisture retrieval in alpine areas. Can. J. Remote Sens. 2011, 37, 535–547. [Google Scholar] [CrossRef]
- Bertoldi, G.; Della Chiesa, S.; Notarnicola, C.; Pasolli, L.; Niedrist, G.; Tappeiner, U. Estimation of soil moisture patterns in mountain grasslands by means of SAR RADARSAT2 images and hydrological modeling. J. Hydrol. 2014, 516, 245–257. [Google Scholar] [CrossRef]
- Gamon, J.A.; Field, C.B.; Goulden, M.L.; Griffin, K.L.; Hartley, A.E.; Joel, G.; Penuelas, J.; Valentini, R. Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecol. Appl. 1995, 5, 28–41. [Google Scholar] [CrossRef]
- Gao, B.-C. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 1996, 58, 257–266. [Google Scholar] [CrossRef]
- Jackson, T.J.; Chen, D.; Cosh, M.; Li, F.; Anderson, M.; Walthall, C.; Doriaswamy, P.; Hunt, E.R. Vegetation water content mapping using landsat data derived normalized difference water index for corn and soybeans. Remote Sens. Environ. 2004, 92, 475–482. [Google Scholar] [CrossRef]
- Chen, D.; Huang, J.; Jackson, T.J. Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands. Remote Sens. Environ. 2005, 98, 225–236. [Google Scholar] [CrossRef]
- Dunn, B.; Batten, G.; Beecher, H.; Ciavarella, S. The potential of near-infrared reflectance spectroscopy for soil analysis—A case study from the Riverine Plain of south-eastern Australia. Anim. Prod. Sci. 2002, 42, 607–614. [Google Scholar] [CrossRef]
- Chang, C.-W.; Laird, D.A.; Mausbach, M.J.; Hurburgh, C.R. Near-infrared reflectance spectroscopy–Principal components regression analyses of soil properties. Soil Sci. Soc. Am. J. 2001, 65, 480–490. [Google Scholar] [CrossRef]
- Baghdadi, N.; Zribi, M. Evaluation of radar backscatter models IEM, OH and Dubois using experimental observations. Int. J. Remote Sens. 2006, 27, 3831–3852. [Google Scholar] [CrossRef]
- Merzouki, A.; McNairn, H.; Pacheco, A. Mapping soil moisture using RADARSAT-2 data and local autocorrelation statistics. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2011, 4, 128–137. [Google Scholar] [CrossRef]
- Prakash, R.; Singh, D.; Pathak, N.P. A fusion approach to retrieve soil moisture with SAR and optical data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2012, 5, 196–206. [Google Scholar] [CrossRef]
© 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Chai, X.; Zhang, T.; Shao, Y.; Gong, H.; Liu, L.; Xie, K. Modeling and Mapping Soil Moisture of Plateau Pasture Using RADARSAT-2 Imagery. Remote Sens. 2015, 7, 1279-1299. https://doi.org/10.3390/rs70201279
Chai X, Zhang T, Shao Y, Gong H, Liu L, Xie K. Modeling and Mapping Soil Moisture of Plateau Pasture Using RADARSAT-2 Imagery. Remote Sensing. 2015; 7(2):1279-1299. https://doi.org/10.3390/rs70201279
Chicago/Turabian StyleChai, Xun, Tingting Zhang, Yun Shao, Huaze Gong, Long Liu, and Kaixin Xie. 2015. "Modeling and Mapping Soil Moisture of Plateau Pasture Using RADARSAT-2 Imagery" Remote Sensing 7, no. 2: 1279-1299. https://doi.org/10.3390/rs70201279
APA StyleChai, X., Zhang, T., Shao, Y., Gong, H., Liu, L., & Xie, K. (2015). Modeling and Mapping Soil Moisture of Plateau Pasture Using RADARSAT-2 Imagery. Remote Sensing, 7(2), 1279-1299. https://doi.org/10.3390/rs70201279