Abstract
The present study aims to exploit the thermal inertia model to assess the shallow groundwater level distribution, as an attempt to address agricultural water shortage and ecological degradation in arid and semi-arid regions. Apparent thermal inertia (ATI) model is one of the most commonly used and successfully implemented models, whereas in some cases, the scales of the results achieved on different dates are inconsistent, which poses a serious difficulty when monitoring soil moisture over time. To address this restriction, we introduce a soil moisture indicator, called RTI, which derives an approximation of soil thermal inertia, from flexible multi-temporal MODIS observations covering a 2-year period. Taking the Ejina Basin in arid area as an example, to more accurately assess the shallow groundwater level distribution, ATI and RTI models were adopted based on the long time series MODIS data to assess the multi-temporal soil moisture content in the study area. The data of groundwater level measured by observation well and thermal inertia were employed to generate scatter diagrams. Subsequently, a nonlinear equation was developed. Based on the thermal inertia, the temporal and spatial distribution map of the shallow groundwater level in the whole study area was generated with the inverse calculation of the nonlinear equation. It was found that the shallow groundwater level distribution in Ejina Basin tended to be shallower from south to north, as well as from summer to winter. As revealed from the results, both ATI and RTI models can effectively assess the shallow groundwater level in arid areas. The RTI model could better reflect the seasonal changes of soil moisture content and groundwater level.
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References
Alkhaier F, Flerchinger GN, Su Z (2012) Shallow groundwater effect on land surface temperature and surface energy balance under bare soil conditions: modeling and description. Hydrol Earth Syst Sci 16(15):1817–1831. https://doi.org/10.5194/hess-16-1817-2012
Babaeian E, Homaee M, Montzka C et al (2016) Soil moisture prediction of bare soil profifiles using diffffuse spectral re-flflectance information and vadose zone flflow modeling. Remote Sens Environ 187:218–229. https://doi.org/10.1016/j.rse.2016.10.029
Becker MW (2006) Potential for satellite remote sensing of ground water. Groundwater 44(2):306–318. https://doi.org/10.1111/j.1745-6584.2005.00123.x
Carlson TN, Perry EM, Schmugge TJ (1990) Remote sensing of soil moisture availability and fractional vegetation cover for agricultural field. Agric Forest Meteorol 52(1–2):45–69. https://doi.org/10.1016/0168-1923(90)90100-K
Chen H, Huo Z, Zhang L, White I (2020) New perspective about application of extended Budyko formula in arid irrigation district with shallow groundwater. J Hydrol 582:124496. https://doi.org/10.1016/j.jhydrol.2019.124496
Cosh MH, Jackson TJ, Moran S et al (2008) Temporal persistence and stability of surface soil moisture in a semi-arid watershed. Remote Sensing Environ 112(2):304–313. https://doi.org/10.1016/j.rse.2007.07.001
Dash CJ, Sarangi A, Adhikary PP et al (2016) Soil water budgeting approach to quantify potential groundwater recharge from croplands and groundwater use in a semi-arid region. Environ Earth Sci 75(10):1–14. https://doi.org/10.1007/s12665-016-5620-7
Entekhabi D, Njoku EG, O’Neill PE et al (2010) The Soil Moisture Active Passive (SMAP) mission. Proc IEEE 98(5):704–716. https://doi.org/10.1109/JPROC.2010.2043918
François C (2002) The potential of directional radiometric temperatures for monitoring soil and leaf temperature and soil moisture status. Remote Sens Environ. 80:122–133. https://doi.org/10.1016/S0034-4257(01)00293-0
Girotto M, Reichle RH, Rodell M et al (2019) Multi-sensor assimilation of SMOS brightness temperature and GRACE terrestrial water storage observations for soil moisture and shallow groundwater estimation. Remote Sens Environ 227:12–27. https://doi.org/10.1016/j.rse.2019.04.001
Hassan AM, Belal AA, Hassan MA, Farag FM, Mohamed ES (2019) Potential of thermal remote sensing techniques in monitoring waterlogged area based on surface soil moisture retrieval. J African Earth Sci 155:64–74. https://doi.org/10.1016/j.jafrearsci.2019.04.005
Huang F, Zhang Y, Zhang D, Chen X (2019) Environmental groundwater depth for groundwater-dependent terrestrial ecosystems in arid/semiarid regions: a review. Int J Environ Res Public Health 16(5):763. https://doi.org/10.3390/ijerph16050763
Hulley GC, Hook SJ, Baldridge AM (2010) Investigating the effects of soil moisture on thermal infrared land surface temperature and emissivity using satellite retrievals and laboratory measurements. Remote Sens Environ 114(7):1480–1493. https://doi.org/10.1016/j.rse.2010.02.002
Ireson AM, Wheater HS, Butler AP, Mathias S (2006) Hydrological processes in the Chalk unsaturated zone – insights from an intensive field monitoring programme. J Hydrol 330(1–2):43. https://doi.org/10.1016/j.jhydrol.2006.04.021
Jackson TJ (2002) Remote sensing of soil moisture: implications for groundwater recharge. Hydrogeol J 10(1):40–51. https://doi.org/10.1007/s10040-001-0168-2
Jeong J, Park E, Chen H et al (2020) Estimation of groundwater level based on the robust training of recurrent neural networks using corrupted data. J Hydrol 582:124512. https://doi.org/10.1016/j.jhydrol.2019.124512
Jin X (2010) Quantitative relationship between the desert vegetation and groundwater depth in Ejina Oasis, the Heihe River Basin. Earth Sci Front 17(6):181–186 CNKI:SUN:DXQY.0.2010–06-024
Kang J, Jin R, Li X et al (2017) High spatio-temporal resolution mapping of soil moisture by integrating wireless sensor network observations and MODIS apparent thermal inertia in the Babao River Basin, China. Remote Sens Environ 191:232–245. https://doi.org/10.1016/j.rse.2017.01.027
Kerr YH, Waldteufel P, Wigneron JP et al (2002) Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) mission. IEEE Trans Geosci Remote Sens 39(8):1729–1735. https://doi.org/10.1109/36.942551
Lei SG, Bian ZF, Daniels JL et al (2014) Improved spatial resolution in soil moisture retrieval at arid mining area using apparent thermal inertia. Trans Nonferrous Metals Soc China 24(6):1866–1873. https://doi.org/10.1016/S1003-6326(14)63265-9
Li X, Xia J, Zhao X, Chen Y (2019) Effects of planting Tamarix chinensis on shallow soil water and salt content under different groundwater depths in the Yellow River Delta. Geoderma 335:104–111. https://doi.org/10.1016/j.geoderma.2018.08.017
Liang S (2001) Narrowband to broadband conversions of land surface albedo I: Algorithms[J]. Remote Sens Environ 76(2):213–238. https://doi.org/10.1016/s0034-4257(00)00205-4
Luo H, Wang H, Shi C (2013) Retrieving groundwater in Yellow River Delta area using remote sensing. Remote Sens for Land Resour 25(3):145–152. https://doi.org/10.3969/j.issn.1009-2722.2005.06.008
Ma A (1997) Remote Sensing Information Model. Peking University Press, Beijing
Ma C, Wang W, Han X et al (2013) Soil moisture retrieval in the Heihe river basin based on the real thermal inertia method. IEEE J Selected Topics Appl Earth Observations Remote Sens 6(3):1460–1467. https://doi.org/10.1109/JSTARS.2013.2252149
Maihemuti B, Simayi Z, Alifujiang Y et al (2021) Development and evaluation of the soil water balance model in an inland arid delta oasis: Implications for sustainable groundwater resource management. Glob Ecol Conserv 25:e01408. https://doi.org/10.1016/j.gecco.2020.e01408
Mathias SA, Sorensen JPR, Butler AP (2017) Soil moisture data as a constraint for groundwater recharge estimation. J Hydrol 552:258–266. https://doi.org/10.1016/j.jhydrol.2017.06.040
Mengistu AG, Van RLD, Mavimbela SSW (2018) Shallow groundwater effects on evaporation and soil temperature in two windblown sands (Eutric Cambisol and Chromic Luvisol) in South Africa. Geoderma Regional 15:e00190. https://doi.org/10.1016/j.geodrs.2018.e00190
Notarnicola C, Caporaso L, Di Giuseppe F et al (2012) Inferring soil moisture variability in the Mediterrean Sea area using infrared and passive microwave observations. Can J Remote Sens 38(1):46–59. https://doi.org/10.5589/m12-011
Obuobie E, Diekkrueger B, Agyekum W, Agodzo S (2012) Groundwater level monitoring and recharge estimation in the White Volta River basin of Ghana. J Afr Earth Sci 71–72:80–86. https://doi.org/10.1016/j.jafrearsci.2012.06.005
Petropoulos G, Carlson TN, Wooster MJ, Islam S (2009) A review of T-s/VI remote sensing based methods for the retrieval of land surface energy fluxes and soil surface moisture. Prog Phys Geogr. 33:224–250. https://doi.org/10.1177/0309133309338997
Qin J, Yang K, Lu N, Chen Y, Zhao L, Han M (2013) Spatial upscaling of in-situ soil moisture measurements based on MODIS-derived apparent thermal inertia. Remote Sens Environ 138:1–9. https://doi.org/10.1016/j.rse.2013.07.003
Rahimzadeh-Bajgiran P, Berg AA, Champagne C, Omasa K (2013) Estimation of soil moisture using optical/thermal infrared remote sensing in the Canadian Prairies. ISPRS J Photogram Remote Sens. 83:94–103. https://doi.org/10.1016/j.isprsjprs.2013.06.004
Richard A, Galle S, Descloitres M et al (2013) Interplay of riparian forest and groundwater in the hillslope hydrology of Sudanian West Africa (northern Benin). Hydrol Earth Syst Sci Discussions 17:5079–5096. https://doi.org/10.5194/hess-17-5079-2013
Roth CH, Malicki MA, Plagge R (2006) Empirical evaluation of the relationship between soil dielectric constant and volumetric water content as the basis for calibrating soil moisture measurements by TDR. J Soil Sci 43:1–13. https://doi.org/10.1111/j.1365-2389.1992.tb00115.x
Sadeghi M, Babaeian E, Tuller M, Jones SB (2017) The optical trapezoid model: a novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations. Remote Sens Environ. 198:52–68. https://doi.org/10.1016/j.rse.2017.05.041
Schumann GJP, Bates PD, Lunt DJ et al (2008) Consistency of freely and globally available remote sensing soil moisture products. J Am Chem Soc 73(8). https://doi.org/10.1021/ja01152a004
Shi F, Zhao C, Zhao X et al (2020) Spatial variability of the groundwater exploitation potential in an arid alluvial-diluvial plain using GIS-based Dempster-Shafer theory. Quaternary Int. https://doi.org/10.1016/j.quaint.2020.10.055
Taktikou E, Bourazanis G, Papaioannou G et al (2016) Prediction of soil moisture from remote sensing data. Procedia Eng 162:309–316. https://doi.org/10.1016/j.proeng.2016.11.066
Tan H, Liu Z, Rao W et al (2017) Stable isotopes of soil water: implications for soil water and shallow groundwater recharge in hill and gully regions of the Loess Plateau, China. Agric Ecosyst Environ 243:1–9. https://doi.org/10.1016/j.agee.2017.04.001
Tapley BD, Bettadpur S, Watkins W, Reigber C (2004) The gravity recovery and climate experiment: mission overview and early results. Geophys Res Lett 31(9). https://doi.org/10.1029/2004GL019920
Tian J, Philpot WD (2015) Relationship between surface soil water content, evaporation rate, and water absorption band depths in SWIR reflflectance spectra. Remote Sens. Environ. 169:280–289. https://doi.org/10.1016/j.rse.2015.08.007
Van Doninck J, Peters J, Baets BD et al (2011) The potential of multitemporal Aqua and Terra MODIS apparent thermal inertia as a soil moisture indicator. Int J Appl Earth Observation Geoinformation 13(6):934–941. https://doi.org/10.1016/j.jag.2011.07.003
Wang S, Liu H, Yu Y et al (2020) Evaluation of groundwater sustainability in the arid Hexi Corridor of Northwestern China, using GRACE, GLDAS and measured groundwater data products. Sci Total Environ 705:135829. https://doi.org/10.1016/j.scitotenv.2019.135829
Wen X, Wu Y, Su J et al (2005) Hydrochemical characteristics and salinity of groundwater in the Ejina Basin, Northwestern China. Environ Geology 48(6):665–675. https://doi.org/10.1007/s00254-005-0001-7
Xi H, Feng Q, Liu W et al (2010) The research of groundwater flow model in Ejina Basin, Northwestern China. Environ Earth Sci 60(5):953–963. https://doi.org/10.1007/s12665-009-0231-1
Xiao F, Li Y, Du Y et al (2014) Monitoring perennial sub-surface waterlogged croplands based on MODIS in Jianghan Plain, middle reaches of the Yangtze River. J Integr Agric 13(8):1791–1801. https://doi.org/10.1016/S2095-3119(13)60563-8
Xue J, Huo Z, Wang F et al (2018) Untangling the effects of shallow groundwater and deficit irrigation on irrigation water productivity in arid region: new conceptual model. Sci Total Environ 619–620:1170–1182. https://doi.org/10.1016/j.scitotenv.2017.11.145
Xue Y, Cracknell AP (1995) Advanced thermal inertia modeling. Int J Remote Sens. Int J Remote Sens 16(3):431–446. https://doi.org/10.1080/01431169508954411
Xue Y, Lawrence SP, LlewellynJones DT, Mutlow C (1998) On the Earth’s surface energy exchange determination from ERS satellite ATSR data. Part I: long-wave radiation. Int J Remote Sens 19(13):2561–2583. https://doi.org/10.1080/014311698214631
Yan Y, Zhu J, Yan Q, Zheng X, Song L (2014) Modeling shallow groundwater levels in Horqin Sandy Land, North China, using satellite-based remote sensing images. J Appl Remote Sens 8(1):083647. https://doi.org/10.1117/1.jrs.8.083647
Yao Y, Zhang Y, Liu Q et al (2019) Evaluation of a satellite-derived model parameterized by three soil moisture constraints to estimate terrestrial latent heat flflux in the Heihe River basin of Northwest China. Sci Total Environ 695:133787. https://doi.org/10.1016/j.scitotenv.2019.133787
Yin L, Zhou Y, Ge S et al (2013) Comparison and modification of methods for estimating evapotranspiration using diurnal groundwater level fluctuations in arid and semiarid regions. J Hydrol 496:9–16. https://doi.org/10.1016/j.jhydrol.2013.05.016
Yin L, Zhou Y, Huang J et al (2015) Interaction between groundwater and trees in an arid site: potential impacts of climate variation and groundwater abstraction on trees. J Hydrol 528:435–448. https://doi.org/10.1016/j.jhydrol.2015.06.063
Zhang R (1996) Experimental remote sensing model and it’s field basis. Science Press, Beijing
Zhang Y, Wu Y, Su J et al (2005) Groundwater replenishment analysis by using natural isotopes in Ejina Basin, Northwestern China. Environ Geology 48(1):6–14. https://doi.org/10.1007/s00254-004-1214-x
Zhu W, Wu B, Yan N et al (2014) A method to estimate diurnal surface soil heat flux from MODIS data for a sparse vegetation and bare soil. J Hydrol 511:139–150. https://doi.org/10.1016/j.jhydrol.2014.01.019
Zribi M, Baghdadi N, Holah N, Fafifin O (2005) New methodology for soil surface moisture estimation and its application to ENVISAT-ASAR multi-incidence data inversion. Remote Sens Environ. 96:485–496. https://doi.org/10.1016/j.rse.2005.04.005
Acknowledgements
This study was supported by the RS geochemistry disciplinary innovation team, Kunming University of Science and Technology, Kunming, China. The authors are grateful to the USGS for providing the required satellite images. The authors are thankful to the “Heihe Plan Science Data Center, National Natural Science Foundation of China” for its data support. We are very grateful for the useful comments provided by anonymous reviewers.
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Luo, D., Wen, X., Xu, J. et al. Assessing shallow groundwater level using RTI model and long-term MODIS data in Ejina Basin, Northwest China. Earth Sci Inform 14, 861–870 (2021). https://doi.org/10.1007/s12145-021-00587-5
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DOI: https://doi.org/10.1007/s12145-021-00587-5