Enhanced Estimation of Root Zone Soil Moisture at 1 km Resolution Using SMAR Model and MODIS-Based Downscaled AMSR2 Soil Moisture Data
Abstract
:1. Introduction
- VIS/NIR, TIR, and microwave are currently the three main data sources for global SSM monitoring. The VIS/NIR and TIR data have higher spatial resolution and can easily be affected by clouds, while microwave data have lower spatial resolution and can provide all-weather data coverage. To overcome the gaps between these sources of data, the synergic multi-band SSM retrievals should be considered.
- Considerable efforts have been made in the retrieval of two Land Surface Temperature (LST) and SSM using passive microwave observations and auxiliary data. However, SSM and LST are codependent making the retrieval process difficult. Moreover, obtaining accurate auxiliary information such as meteorological parameters and soil texture is difficult due to clear-sky dependency and low spatial resolution, respectively. To solve this problem, it is essential to simultaneously retrieve LST and SSM from only passive microwave data to reduce the number of unknown parameters and make the retrieval independent of auxiliary information.
- The target accuracy of RMSE of 0.04 m3/m3 between the satellite and measured SSM data has been the criterion for validating the SSM products, however, scientists suggested that distinguished target accuracies should be determined for different combination of surface moisture status, soil texture, and vegetation coverage.
- In order to validate remotely sensed SSM data, in situ SSM measurements with a fixed depth of 5 cm are frequently used. However, VIS/NIR and microwave data can reflect SSM with only a few millimeters and centimeters, respectively, mostly depending on SSM content within the soil column and frequencies used for detecting SSM. Therefore, it seems that currently there is no good way to solve the contradiction of sensing depth, however, post-processing steps (e.g., assimilation technology and the entropy theory) can mitigate this problem.
- The estimation of RZSM from satellite SSM has been increasingly proposed by a number of investigations. The possible approaches for obtaining such data are (a) assimilating microwave SSM data into land surface models; (b) exponential filter and the analytical relationship between RZSM and SSM; and (c) using the P-band SAR with much deeper penetration depth due to its longer wavelength. However, satellite SSM accuracy and soil texture distribution are among the great challenges to obtain RZSM at present.
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. AMSR2 Satellite-Based SSM Data
2.2.2. Ground-Based SM Data
2.2.3. Remotely Sensed MODIS Parameters
2.3. AMSR2 SSM Downscaling
2.4. SMAR Model
3. Results
3.1. AMSR2 Downscaling Using MODIS Albedo, LST and NDVI
3.2. RZSM Estimation Based on the SMAR Model
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Hanson, J.D.; Rojas, K.; Shaffer, M.J. Calibrating the root zone water quality model. Agron. J. 1999, 91, 171–177. [Google Scholar] [CrossRef]
- Li, Z.-L.; Leng, P.; Zhou, C.-H.; Chen, K.-S.; Zhou, F.-C.; Shang, G.-F. Soil moisture retrieval from remote sensing measurements: Current knowledge and directions for the future. Earth Sci. Rev. 2021, 218, 103673. [Google Scholar] [CrossRef]
- Das, N.N.; Mohanty, B.P. Root zone soil moisture assessment using remote sensing and vadose zone modeling. Vadose Zone J. 2006, 5, 296–307. [Google Scholar] [CrossRef] [Green Version]
- Georgakakos, K.P. Soil Moisture Theories and Observations-Preface; Elsevier Science Bv: Amsterdam, The Netherlands, 1996. [Google Scholar]
- Nuñez-Olivieri, J.; Muñoz-Barreto, J.; Tirado-Corbalá, R.; Lakhankar, T.; Fisher, A. Comparison and downscale of AMSR2 soil moisture products with in situ measurements from the SCAN–NRCS network over Puerto Rico. Hydrology 2017, 4, 46. [Google Scholar] [CrossRef] [Green Version]
- Crow, W.T.; Berg, A.A.; Cosh, M.H.; Loew, A.; Mohanty, B.P.; Panciera, R.; de Rosnay, P.; Ryu, D.; Walker, J.P. Upscaling sparse ground-based soil moisture observations for the validation of coarse-resolution satellite soil moisture products. Rev. Geophys. 2012, 50, 1–20. [Google Scholar] [CrossRef] [Green Version]
- Njoku, E.G.; Jackson, T.J.; Lakshmi, V.; Chan, T.K.; Nghiem, S.V. Soil moisture retrieval from AMSR-E. IEEE Trans. Geosci. Remote Sens. 2003, 41, 215–229. [Google Scholar] [CrossRef]
- Zreda, M.; Shuttleworth, W.; Zeng, X.; Zweck, C.; Desilets, D.; Franz, T.; Rosolem, R. COSMOS: The cosmic-ray soil moisture observing system. Hydrol. Earth Syst. Sci. 2012, 16, 4079–4099. [Google Scholar] [CrossRef] [Green Version]
- Montzka, C.; Bogena, H.R.; Zreda, M.; Monerris, A.; Morrison, R.; Muddu, S.; Vereecken, H. Validation of spaceborne and modelled surface soil moisture products with cosmic-ray neutron probes. Remote Sens. 2017, 9, 103. [Google Scholar] [CrossRef] [Green Version]
- Das, N.N.; Entekhabi, D.; Dunbar, R.S.; Chaubell, M.J.; Colliander, A.; Yueh, S.; Jagdhuber, T.; Chen, F.; Crow, W.; O’Neill, P.E. The SMAP and Copernicus Sentinel 1A/B microwave active-passive high resolution surface soil moisture product. Remote Sens. Environ. 2019, 233, 111380. [Google Scholar] [CrossRef]
- Cho, E.; Su, C.-H.; Ryu, D.; Kim, H.; Choi, M. Does AMSR2 produce better soil moisture retrievals than AMSR-E over Australia? Remote Sens. Environ. 2017, 188, 95–105. [Google Scholar] [CrossRef]
- Gruhier, C.; Rosnay, P.d.; Hasenauer, S.; Holmes, T.; Jeu, R.d.; Kerr, Y.; Mougin, E.; Njoku, E.; Timouk, F.; Wagner, W. Soil moisture active and passive microwave products: Intercomparison and evaluation over a Sahelian site. Hydrol. Earth Syst. Sci. 2010, 14, 141–156. [Google Scholar] [CrossRef] [Green Version]
- Su, C.-H.; Zhang, J.; Gruber, A.; Parinussa, R.; Ryu, D.; Crow, W.T.; Wagner, W. Error decomposition of nine passive and active microwave satellite soil moisture data sets over Australia. Remote Sens. Environ. 2016, 182, 128–140. [Google Scholar] [CrossRef]
- Malbéteau, Y.; Merlin, O.; Gascoin, S.; Gastellu, J.P.; Mattar, C.; Olivera-Guerra, L.; Khabba, S.; Jarlan, L. Normalizing land surface temperature data for elevation and illumination effects in mountainous areas: A case study using ASTER data over a steep-sided valley in Morocco. Remote Sens. Environ. 2017, 189, 25–39. [Google Scholar] [CrossRef] [Green Version]
- Wang, Y.; Leng, P.; Ma, J.; Peng, J. A Method for Downscaling Satellite Soil Moisture Based on Land Surface Temperature and Net Surface Shortwave Radiation. Ieee Geosci. Remote Sens. Lett. 2021. [Google Scholar] [CrossRef]
- Jackson, T.J.; Cosh, M.H.; Bindlish, R.; Starks, P.J.; Bosch, D.D.; Seyfried, M.; Goodrich, D.C.; Moran, M.S.; Du, J. Validation of advanced microwave scanning radiometer soil moisture products. IEEE Trans. Geosci. Remote Sens. 2010, 48, 4256–4272. [Google Scholar] [CrossRef]
- Yan, H.; DeChant, C.M.; Moradkhani, H. Improving soil moisture profile prediction with the particle filter-Markov chain Monte Carlo method. IEEE Trans. Geosci. Remote Sens. 2015, 53, 6134–6147. [Google Scholar] [CrossRef]
- Liu, P.-W.; Bindlish, R.; Fang, B.; Lakshmi, V.; O’Neill, P.E.; Yang, Z.; Cosh, M.H.; Bongiovanni, T.; Bosch, D.D.; Collins, C.H. Assessing Disaggregated SMAP Soil Moisture Products in the United States. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 2577–2592. [Google Scholar] [CrossRef]
- Chauhan, N.; Miller, S.; Ardanuy, P. Spaceborne soil moisture estimation at high resolution: A microwave-optical/IR synergistic approach. Int. J. Remote Sens. 2003, 24, 4599–4622. [Google Scholar] [CrossRef]
- Choi, M.; Hur, Y. A microwave-optical/infrared disaggregation for improving spatial representation of soil moisture using AMSR-E and MODIS products. Remote Sens. Environ. 2012, 124, 259–269. [Google Scholar] [CrossRef]
- Piles, M.; Sánchez, N.; Vall-llossera, M.; Camps, A.; Martínez-Fernández, J.; Martínez, J.; González-Gambau, V. A downscaling approach for SMOS land observations: Evaluation of high-resolution soil moisture maps over the Iberian Peninsula. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014, 7, 3845–3857. [Google Scholar] [CrossRef] [Green Version]
- Sánchez-Ruiz, S.; Piles, M.; Sánchez, N.; Martínez-Fernández, J.; Vall-llossera, M.; Camps, A. Combining SMOS with visible and near/shortwave/thermal infrared satellite data for high resolution soil moisture estimates. J. Hydrol. 2014, 516, 273–283. [Google Scholar] [CrossRef]
- Zhao, W.; Li, A. A downscaling method for improving the spatial resolution of AMSR-E derived soil moisture product based on MSG-SEVIRI data. Remote Sens. 2013, 5, 6790–6811. [Google Scholar] [CrossRef] [Green Version]
- Piles, M.; Sánchez, N. Spatial downscaling of passive microwave data with visible-to-infrared information for high-resolution soil moisture mapping. In Satellite Soil Moisture Retrieval; Elsevier: Amsterdam, The Netherlands, 2016; pp. 109–132. [Google Scholar]
- Gao, H.; Wood, E.F.; Jackson, T.; Drusch, M.; Bindlish, R. Using TRMM/TMI to retrieve surface soil moisture over the southern United States from 1998 to 2002. J. Hydrometeorol. 2006, 7, 23–38. [Google Scholar] [CrossRef]
- Escorihuela, M.-J.; Chanzy, A.; Wigneron, J.-P.; Kerr, Y. Effective soil moisture sampling depth of L-band radiometry: A case study. Remote Sens. Environ. 2010, 114, 995–1001. [Google Scholar] [CrossRef] [Green Version]
- Mahmood, R.; Hubbard, K.G. Relationship between soil moisture of near surface and multiple depths of the root zone under heterogeneous land uses and varying hydroclimatic conditions. Hydrol. Process. Int. J. 2007, 21, 3449–3462. [Google Scholar] [CrossRef]
- Mahmood, R.; Littell, A.; Hubbard, K.G.; You, J. Observed data-based assessment of relationships among soil moisture at various depths, precipitation, and temperature. Appl. Geogr. 2012, 34, 255–264. [Google Scholar] [CrossRef]
- Baldwin, D.; Manfreda, S.; Keller, K.; Smithwick, E. Predicting root zone soil moisture with soil properties and satellite near-surface moisture data across the conterminous United States. J. Hydrol. 2017, 546, 393–404. [Google Scholar] [CrossRef]
- Ragab, R. Towards a continuous operational system to estimate the root-zone soil moisture from intermittent remotely sensed surface moisture. J. Hydrol. 1995, 173, 1–25. [Google Scholar] [CrossRef]
- Puma, M.J.; Celia, M.A.; Rodriguez-Iturbe, I.; Guswa, A.J. Functional relationship to describe temporal statistics of soil moisture averaged over different depths. Adv. Water Resour. 2005, 28, 553–566. [Google Scholar] [CrossRef]
- Manfreda, S.; McCabe, M.F.; Fiorentino, M.; Rodríguez-Iturbe, I.; Wood, E.F. Scaling characteristics of spatial patterns of soil moisture from distributed modelling. Adv. Water Resour. 2007, 30, 2145–2150. [Google Scholar] [CrossRef]
- Sabater, J.M.; Jarlan, L.; Calvet, J.-C.; Bouyssel, F.; De Rosnay, P. From near-surface to root-zone soil moisture using different assimilation techniques. J. Hydrometeorol. 2007, 8, 194–206. [Google Scholar] [CrossRef]
- Wagner, W.; Lemoine, G.; Rott, H. A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sens. Environ. 1999, 70, 191–207. [Google Scholar] [CrossRef]
- Laio, F.; Porporato, A.; Fernandez-Illescas, C.; Rodriguez-Iturbe, I. Plants in water-controlled ecosystems: Active role in hydrologic processes and response to water stress: IV. Discussion of real cases. Adv. Water Resour. 2001, 24, 745–762. [Google Scholar] [CrossRef]
- Manfreda, S.; Fiorentino, M. A stochastic approach for the description of the water balance dynamics in a river basin. Hydrol. Earth Syst. Sci. 2008, 12, 1189–1200. [Google Scholar] [CrossRef] [Green Version]
- Manfreda, S.; Brocca, L.; Moramarco, T.; Melone, F.; Sheffield, J. A physically based approach for the estimation of root-zone soil moisture from surface measurements. Hydrol. Earth Syst. Sci. 2014, 18, 1199–1212. [Google Scholar] [CrossRef] [Green Version]
- Jackson, T.J.; Schmugge, T.J. Passive microwave remote sensing system for soil moisture: Some supporting research. IEEE Trans. Geosci. Remote Sens. 1989, 27, 225–235. [Google Scholar] [CrossRef]
- Faridani, F.; Farid, A.; Ansari, H.; Manfreda, S. Estimation of the root-zone soil moisture using passive microwave remote sensing and SMAR Model. J. Irrig. Drain. Eng. 2017, 143, 04016070. [Google Scholar] [CrossRef]
- Ansari, H.; Hassanpour, M. Design and construction of REC-P55 for reading of soil moisture, temperature and salinity. Iran. J. Irrig. Drain. 2015, 9, 32–43. [Google Scholar]
- Kawaguchi, M.; Yoshida, T. Regular Observation by Global Change Observation Mission 1st-Water GCOM-W1 (Shizuku). Nec Tech. J 2013, 8, 32–35. [Google Scholar]
- Hihara, T.; Kubota, M.; Okuro, A. Evaluation of sea surface temperature and wind speed observed by GCOM-W1/AMSR2 using in situ data and global products. Remote Sens. Environ. 2015, 164, 170–178. [Google Scholar] [CrossRef]
- Owe, M.; de Jeu, R.; Holmes, T. Multisensor historical climatology of satellite-derived global land surface moisture. J. Geophys. Res. Earth Surf. 2008, 113, 1–17. [Google Scholar] [CrossRef]
- Peng, J.; Loew, A.; Merlin, O.; Verhoest, N.E. A review of spatial downscaling of satellite remotely sensed soil moisture. Rev. Geophys. 2017, 55, 341–366. [Google Scholar] [CrossRef]
- Busch, F.A.; Niemann, J.D.; Coleman, M. Evaluation of an empirical orthogonal function–based method to downscale soil moisture patterns based on topographical attributes. Hydrol. Process. 2012, 26, 2696–2709. [Google Scholar] [CrossRef]
- Coleman, M.L.; Niemann, J.D. Controls on topographic dependence and temporal instability in catchment-scale soil moisture patterns. Water Resour. Res. 2013, 49, 1625–1642. [Google Scholar] [CrossRef]
- Ray, R.L.; Jacobs, J.M.; Cosh, M.H. Landslide susceptibility mapping using downscaled AMSR-E soil moisture: A case study from Cleveland Corral, California, US. Remote Sens. Environ. 2010, 114, 2624–2636. [Google Scholar] [CrossRef]
- Gheybi, F.; Paridad, P.; Faridani, F.; Farid, A.; Pizarro, A.; Fiorentino, M.; Manfreda, S. Soil Moisture Monitoring in Iran by Implementing Satellite Data into the Root-Zone SMAR Model. Hydrology 2019, 6, 44. [Google Scholar] [CrossRef] [Green Version]
- Baldwin, D.; Manfreda, S.; Lin, H.; Smithwick, E.A. Estimating root zone soil moisture across the Eastern United States with passive microwave satellite data and a simple hydrologic model. Remote Sens. 2019, 11, 2013. [Google Scholar] [CrossRef] [Green Version]
- Zhuang, R.; Zeng, Y.; Manfreda, S.; Su, Z. Quantifying long-term land surface and root zone soil moisture over Tibetan Plateau. Remote Sens. 2020, 12, 509. [Google Scholar] [CrossRef] [Green Version]
- Laio, F. A vertically extended stochastic model of soil moisture in the root zone. Water Resour. Res. 2006, 42, 1–10. [Google Scholar] [CrossRef] [Green Version]
- Piles, M.; Camps, A.; Vall-Llossera, M.; Corbella, I.; Panciera, R.; Rudiger, C.; Kerr, Y.H.; Walker, J. Downscaling SMOS-derived soil moisture using MODIS visible/infrared data. IEEE Trans. Geosci. Remote Sens. 2011, 49, 3156–3166. [Google Scholar] [CrossRef]
Website | Spatial Resolution (km-) | Temporal Resolution (Day) | Product Level/Version | Space Agency | Platform | Type | Sensor |
---|---|---|---|---|---|---|---|
https://smap.jpl.nasa.gov/ (accessed on 11 April 2018). | 3 (Active), 36 (Passive) | 1 | L3 | NASA | SMAP | Passive | SMAP |
http://www.esa.int/Our_Activities/ accessed on 11 April 2018) | 40 | 1 | L2/V6 | ESA | SMOS | Passive | MIRAS |
https://nsidc.org/data/amsre (accessed on 11 April 2018) | 25 | 1 | L3/V2 | NASA, JAXA | AQUA | Passive | AMSR-E |
https://hydro1.gesdisc.eosdis.nasa.gov/ (accessed on 11 April 2018) | 25 | 1 | L3/V1 | NASA, JAXA | GCOM-W1 | Passive | AMSR2 |
https://trmm.gsfc.nasa.gov/ (accessed on 11 April 2018) | 25 | 1 | L2/V1 | NASA, JAXA | TRMM | Passive | TMI |
https://www.ipf.tuwien.ac.at/ (accessed on 11 April 2018) | 25.5 | 1 | L2 | EUMETSAT, ESA | METOP | Active | ASCAT |
https://podaac.jpl.nasa.gov/SSMI (accessed on 11 April 2018) | 25 | 1 | - | NASA | DMSP | Passive | SSM/I |
Soil Type | N [-] | SW [-] | SC [-] |
---|---|---|---|
sand | 0.44 | 0.06 | 0.14 |
loamy sand | 0.44 | 0.11 | 0.24 |
sandy loam | 0.45 | 0.19 | 0.42 |
silty loam | 0.50 | 0.27 | 0.57 |
loam | 0.46 | 0.25 | 0.50 |
sandy clay loam | 0.40 | 0.34 | 0.62 |
silty clay loam | 0.47 | 0.45 | 0.73 |
clay loam | 0.46 | 0.40 | 0.67 |
sandy clay | 0.43 | 0.51 | 0.75 |
clay | 0.48 | 0.56 | 0.80 |
Coefficient | Value | Squared Error | T-Statistic | P (%) |
---|---|---|---|---|
a000 | 0.892618 | 0.0041 | 49.12 | 0.0015 |
a100 | −0.93067 | 2.45 × 10−5 | 2.98 | 0.1431 |
a010 | −0.00246 | 1.62 × 10−5 | 1.97 | 0.1275 |
a001 | 0.255988 | 5.31 × 10−7 | −1.66 | 0.1112 |
a110 | 0.002259 | 1.02 × 10−7 | −3.24 | 0.1255 |
a101 | −2.13287 | 1.78 × 10−9 | −0.12 | 0.2928 |
a011 | 0.0027 | 2.12 × 10−9 | 0.11 | 0.1159 |
AMSR2 25 km SSM | Downscaled AMSR2 1 km SSM | ||||||
---|---|---|---|---|---|---|---|
Station | Distance (km) | MAE (m3/m3) | RMSE (m3/m3) | R (-) | MAE (m3/m3) | RMSE (m3/m3) | R (-) |
1 | 5.84 | 0.028 | 0.030 | 0.473 | 0.012 | 0.015 | 0.742 |
2 | 8.46 | 0.092 | 0.094 | 0.400 | 0.047 | 0.047 | 0.876 |
3 | 9.73 | 0.043 | 0.044 | 0.350 | 0.011 | 0.013 | 0.715 |
4 | 2.10 | 0.029 | 0.030 | 0.783 | 0.013 | 0.017 | 0.707 |
5 | 1.97 | 0.027 | 0.029 | 0.707 | 0.025 | 0.029 | 0.782 |
6 | 2.58 | 0.037 | 0.040 | 0.491 | 0.028 | 0.031 | 0.538 |
7 | 14.27 | 0.050 | 0.051 | 0.232 | 0.026 | 0.032 | 0.793 |
8 | 2.35 | 0.035 | 0.035 | 0.603 | 0.009 | 0.009 | 0.735 |
9 | 3.46 | 0.004 | 0.004 | 0.666 | 0.002 | 0.004 | 0.802 |
10 | 1.06 | 0.042 | 0.043 | 0.700 | 0.003 | 0.003 | 0.705 |
Average | 5.18 | 0.039 | 0.040 | 0.540 | 0.018 | 0.020 | 0.739 |
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Farokhi, M.; Faridani, F.; Lasaponara, R.; Ansari, H.; Faridhosseini, A. Enhanced Estimation of Root Zone Soil Moisture at 1 km Resolution Using SMAR Model and MODIS-Based Downscaled AMSR2 Soil Moisture Data. Sensors 2021, 21, 5211. https://doi.org/10.3390/s21155211
Farokhi M, Faridani F, Lasaponara R, Ansari H, Faridhosseini A. Enhanced Estimation of Root Zone Soil Moisture at 1 km Resolution Using SMAR Model and MODIS-Based Downscaled AMSR2 Soil Moisture Data. Sensors. 2021; 21(15):5211. https://doi.org/10.3390/s21155211
Chicago/Turabian StyleFarokhi, Maedeh, Farid Faridani, Rosa Lasaponara, Hossein Ansari, and Alireza Faridhosseini. 2021. "Enhanced Estimation of Root Zone Soil Moisture at 1 km Resolution Using SMAR Model and MODIS-Based Downscaled AMSR2 Soil Moisture Data" Sensors 21, no. 15: 5211. https://doi.org/10.3390/s21155211
APA StyleFarokhi, M., Faridani, F., Lasaponara, R., Ansari, H., & Faridhosseini, A. (2021). Enhanced Estimation of Root Zone Soil Moisture at 1 km Resolution Using SMAR Model and MODIS-Based Downscaled AMSR2 Soil Moisture Data. Sensors, 21(15), 5211. https://doi.org/10.3390/s21155211