140
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

Field-scale evaluation of remote sensing soil moisture retrievals using a multi-satellite approach

, , &
Pages 181-202 | Received 24 Aug 2022, Accepted 21 Mar 2023, Published online: 06 Apr 2023

References

  • Ahmed, A., Zhang, Y., and Nichols, S., 2011. Review and evaluation of remote sensing methods for soil-moisture estimation. SPIE Reviews, 2 (1), 028001.
  • Alexakis, D.D., et al., 2017. Soil moisture content estimation based on sentinel-1 and auxiliary earth observation products. a hydrological approach. Sensors (Basel), 17, 6. doi:10.3390/s17061455.
  • Allen, R.G., et al., 1998. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations. 56 (97), e156.
  • Amazirh, A., et al., 2018. Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: a study case over bare soil. Remote Sensing of Environment, 211, 321–337. doi:10.1016/j.rse.2018.04.013.
  • Attarzadeh, R., et al., 2018. Synergetic use of sentinel-1 and sentinel-2 data for soil moisture mapping at plot scale. Remote Sensing, 10 (8), 1285. doi:10.3390/rs10081285.
  • Attema, E. and Ulaby, F.T., 1978. Vegetation modeled as a water cloud. Radio Science, 13 (2), 357–364. doi:10.1029/RS013i002p00357.
  • Ayari, E., et al., 2022. Investigation of multi-frequency SAR data to retrieve the soil moisture within a drip irrigation context using modified water cloud model. Sensors, 22 (2), 580. doi:10.3390/s22020580.
  • Baghdadi, N., et al., 2008. Operational performance of current synthetic aperture radar sensors in mapping soil surface characteristics in agricultural environments: application to hydrological and erosion modelling. Hydrological Processes: An International Journal, 22 (1), 9–20. doi:10.1002/hyp.6609.
  • Baghdadi, N., et al., 2017. Calibration of the water cloud model at C-band for winter crop fields and grasslands. Remote Sensing, 9 (9), 969. doi:10.3390/rs9090969.
  • Baghdadi, N., et al., 2018. Potential of Sentinel-1 images for estimating the soil roughness over bare agricultural soils. Water, 10 (2), 131. doi:10.3390/w10020131.
  • Baghdadi, N., Holah, N., and Zribi, M., 2006. Soil moisture estimation using multi‐incidence and multi‐polarization ASAR data. International Journal of Remote Sensing, 27 (10), 1907–1920. doi:10.1080/01431160500239032.
  • Bao, Y., et al., 2018. Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model. International Journal of Applied Earth Observation and Geoinformation, 72, 76–85. doi:10.1016/j.jag.2018.05.026.
  • Bauer-Marschallinger, B., et al., 2019. Toward global soil moisture monitoring with Sentinel-1: harnessing assets and overcoming obstacles. IEEE Transactions on Geoscience and Remote Sensing, 57 (1), 520–539. doi:10.1109/TGRS.2018.2858004.
  • Behari, J., 2005. Microwave dielectric behaviour of wet soils. Remote Sensing and Digital Image Processing. New Delhi, India: Anamaya Publishers, Vol.8, pp.178.
  • Bruckler, L., Witono, H., and Stengel, P., 1988. Near surface soil moisture estimation from microwave measurements. Remote Sensing of Environment, 26 (2), 101–121. doi:10.1016/0034-4257(88)90091-0.
  • Chen, S., et al., 2015. Temperature vegetation dryness index estimation of soil moisture under different tree species. Sustainability, 7 (9), 11401–11417. doi:10.3390/su70911401.
  • Cheng, M., et al., 2022. Using multimodal remote sensing data to estimate regional-scale soil moisture content: a case study of Beijing, China. Agricultural Water Management, 260, 107298. doi:10.1016/j.agwat.2021.107298.
  • Chiraz, M.C., Olfa, M., and Hamadi, H., 2022. Remote sensing and soil moisture data for water productivity determination. Agricultural Water Management, 263, 107482. doi:10.1016/j.agwat.2022.107482.
  • Choi, K. and Chong, K., 2022. Modified inverse distance weighting interpolation for particulate matter estimation and mapping. Atmosphere, 13 (5), 846. doi:10.3390/atmos13050846.
  • Colliander, A., et al., 2021. Validation of soil moisture data products from the NASA SMAP mission. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 364–392. doi:10.1109/JSTARS.2021.3124743.
  • Das, N.N., Entekhabi, D., and Njoku, E.G. 2011. An algorithm for merging SMAP radiometer and radar Data for high-resolution soil-moisture retrieval. IEEE Transactions on Geoscience and Remote Sensing, 49 (5), 1504–1512. doi:10.1109/TGRS.2010.2089526.
  • Dente, L., 2016. Microwave remote sensing for soil moisture monitoring: synergy of active and passive observations and validation of retrieved products, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, The Netherlands.
  • El Hajj, M., et al., 2017. Synergic use of Sentinel-1 and Sentinel-2 images for operational soil moisture mapping at high spatial resolution over agricultural areas. Remote Sensing, 9 (12), 1292. doi:10.3390/rs9121292.
  • Fan, L.-Y., et al., 2009. Investigating the relationship between NDVI and LAI in semi-arid grassland in Inner Mongolia using in-situ measurements. Theoretical and Applied Climatology, 95 (1), 151–156. doi:10.1007/s00704-007-0369-2.
  • Fang, B., et al., 2022. A global 1‐km downscaled SMAP soil moisture product based on thermal inertia theory. Vadose Zone Journal, 21 (2), e20182. doi:10.1002/vzj2.20182.
  • Gao, Q., et al., 2017. Synergetic use of Sentinel-1 and Sentinel-2 data for soil moisture mapping at 100 m resolution. Sensors (Basel), 17 (9), 1966. doi:10.3390/s17091966.
  • Gao, F., Kustas, W., and Anderson, M., 2012. A data mining approach for sharpening thermal satellite imagery over land. Remote Sensing, 4 (11), 3287–3319. doi:10.3390/rs4113287.
  • Guzinski, R. and Nieto, H., 2019. Evaluating the feasibility of using Sentinel-2 and Sentinel-3 satellites for high-resolution evapotranspiration estimations. Remote Sensing of Environment, 221, 157–172. doi:10.1016/j.rse.2018.11.019.
  • Hamze, M., et al., 2021. Integration of L-Band derived soil roughness into a bare soil moisture retrieval approach from C-Band SAR data. Remote Sensing, 13 (11), 2102. doi:10.3390/rs13112102.
  • Han, D., et al., 2020. Linking an agro-meteorological model and a water cloud model for estimating soil water content over wheat fields. Computers and Electronics in Agriculture, 179, 105833. doi:10.1016/j.compag.2020.105833.
  • Jackson, T.J., et al., 2010. Validation of advanced microwave scanning radiometer soil moisture products. IEEE Transactions on Geoscience and Remote Sensing, 48 (12), 4256–4272. doi:10.1109/TGRS.2010.2051035.
  • Jackson, T.J., et al., 2011. Validation of soil moisture and ocean salinity (SMOS) soil moisture over watershed networks in the US. IEEE Transactions on Geoscience and Remote Sensing, 50 (5), 1530–1543. doi:10.1109/TGRS.2011.2168533.
  • Jackson, T. and Schmugge, T., 1991. Vegetation effects on the microwave emission of soils. Remote Sensing of Environment, 36 (3), 203–212. doi:10.1016/0034-4257(91)90057-D.
  • Kaheil, Y.H., et al., 2008. Downscaling and assimilation of surface soil moisture using ground truth measurements. IEEE Transactions on Geoscience and Remote Sensing, 46 (5), 1375–1384. doi:10.1109/TGRS.2008.916086.
  • Kaniska, M., Bhattacharya, B.K., and Patel, N.K., 2009. Estimating volumetric surface moisture content for cropped soils using a soil wetness index based on surface temperature and NDVI. Agricultural and Forest Meterology, 149, 1327–1342. doi:10.1016/j.agrformet.2009.03.004.
  • Khandan, R., et al., 2022. Assimilation of satellite-derived soil moisture and brightness temperature in land surface models: a review. Remote Sensing, 14 (3), 770. doi:10.3390/rs14030770.
  • Knight, E.J. and Kvaran, G., 2014. Landsat-8 operational land imager design, characterization and performance. Remote Sensing, 6 (11), 10286–10305. doi:10.3390/rs61110286.
  • Kojima, Y., et al., 2016. Estimating soil moisture distributions across small farm fields with ALOS/PALSAR. International Scholarly Research Notices, (2016), 4203783. doi:10.1155/2016/4203783.
  • Li, X., et al., 2022. A new SMAP soil moisture and vegetation optical depth product (SMAP-IB): algorithm, assessment and inter-comparison. Remote Sensing of Environment, 271, 112921. doi:10.1016/j.rse.2022.112921.
  • Lu, G.Y. and Wong, D.W., 2008. An adaptive inverse-distance weighting spatial interpolation technique. Computers & Geosciences, 34 (9), 1044–1055. doi:10.1016/j.cageo.2007.07.010.
  • Malik, M.S. and Shukla, J., 2014. Estimation of soil moisture by remote sensing and field methods: a review. International Journal of Remote Sensing & Geoscience (IJRSG), 3 (4), 21–27.
  • Manns, H.R., et al., 2014. Impact of soil surface characteristics on soil water content variability in agricultural fields. Hydrological Processes, 28 (14), 4340–4351. doi:10.1002/hyp.10216.
  • Mohanty, B.P., et al., 2017. Soil moisture remote sensing: state-of-the-science. Vadose Zone Journal, 16 (1), 1–9. doi:10.2136/vzj2016.10.0105.
  • Montzka, C., et al., 2020. Soil moisture product validation good practices protocol Version 1.0. In: C. Montzka, M. Cosh, J. Nickeson, F. Camacho (Eds.): Good practices for satellite derived land product validation. Land Product Validation Subgroup (WGCV/CEOS).
  • Nadeem, A.A., et al., 2022. Multi-scale assessment of SMAP level 3 and level 4 soil moisture products over the soil moisture network within the ShanDian River (SMN-SDR) Basin, China. Remote Sensing, 14 (4), 982. doi:10.3390/rs14040982.
  • Patel, N., et al., 2009. Assessing potential of MODIS derived temperature/vegetation condition index (TVDI) to infer soil moisture status. International Journal of Remote Sensing, 30 (1), 23–39. doi:10.1080/01431160802108497.
  • Peng, J., et al., 2021. A roadmap for high-resolution satellite soil moisture applications–confronting product characteristics with user requirements. Remote Sensing of Environment, 252, 112162. doi:10.1016/j.rse.2020.112162.
  • Piles, M., et al., 2011. Downscaling SMOS-derived soil moisture using MODIS visible/infrared data. IEEE Transactions on Geoscience and Remote Sensing, 49 (9), 3156–3166. doi:10.1109/TGRS.2011.2120615.
  • Piles, M., et al., 2014. A downscaling approach for SMOS land observations: evaluation of high-resolution soil moisture maps over the Iberian Peninsula. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7 (9), 3845–3857. doi:10.1109/JSTARS.2014.2325398.
  • Pou Ibar, F.X., 2015. Implementing improvements in obtaining high resolution soil moisture maps using SMOS. Universitat Politècnica de Catalunya, Barcelona.
  • Roy, D.P., et al., 2014. Landsat-8: science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154–172. doi:10.1016/j.rse.2014.02.001.
  • Shen, X., et al., 2022. Impact of random and periodic surface roughness on P-and L-band radiometry. Remote Sensing of Environment, 269, 112825. doi:10.1016/j.rse.2021.112825.
  • Su, Z., et al., 2009. EAGLE 2006–Multi-purpose, multi-angle and multi-sensor in-situ and airborne campaigns over grassland and forest. Hydrology and Earth System Sciences, 13 (6), 833–845. doi:10.5194/hess-13-833-2009.
  • Taghvaeian, S., Chávez, J., and Hansen, N., 2012. Infrared thermometry to estimate crop water stress index and water use of irrigated maize in Northeastern Colorado. Remote Sensing, 4 (11), 3619–3637. doi:10.3390/rs4113619.
  • Taylor, K.E., 2001. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106 (D7), 7183–7192. doi:10.1029/2000JD900719.
  • Tian, J., et al., 2019. Dynamic response patterns of profile soil moisture wetting events under different land covers in the Mountainous area of the Heihe River Watershed, Northwest China. Agricultural and Forest Meteorology, 271, 225–239. doi:10.1016/j.agrformet.2019.03.006.
  • Tian, H., et al., 2022. A novel spectral index for automatic canola mapping by using Sentinel-2 imagery. Remote Sensing, 14 (5), 1113. doi:10.3390/rs14051113.
  • Wagner, W., et al., 2007. Operational readiness of microwave remote sensing of soil moisture for hydrologic applications. Hydrology Research, 38 (1), 1–20. doi:10.2166/nh.2007.029.
  • Waters, R., et al., 2002. SEBAL (Surface Energy Balance Algorithms for Land)—Idaho Implementation—Advanced Training and User’s Manual. The Idaho Department of Water Resources.
  • Wigneron, J.-P., et al., 2017. Modelling the passive microwave signature from land surfaces: a review of recent results and application to the L-band SMOS & SMAP soil moisture retrieval algorithms. Remote Sensing of Environment, 192, 238–262. doi:10.1016/j.rse.2017.01.024.
  • Xiwu, Z., et al., 2006. A method for retrieving high-resolution surface soil moisture from hydros L-band radiometer and Radar observations. IEEE Transactions on Geoscience and Remote Sensing, 44 (6), 1534–1544. doi:10.1109/TGRS.2005.863319.
  • Yadav, V.P., et al., 2019. Estimation of soil moisture through water cloud model using sentinel-1A SAR data. IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, 28 July - 02 August 2019, Yokohama, Japan, 6961–6964.
  • Zappa, L., et al., 2019. Deriving field scale soil moisture from satellite observations and ground measurements in a hilly agricultural region. Remote Sensing, 11 (22), 2596. doi:10.3390/rs11222596.
  • Zavorotny, V., et al., 2003. Seasonal polarimetric measurements of soil moisture using tower-based GPS bistatic radar, 781–783. IEEE.
  • Zeynoddin, M., et al., 2018. Novel hybrid linear stochastic with non-linear extreme learning machine methods for forecasting monthly rainfall a tropical climate. Journal of Environmental Management, 222, 190–206. doi:10.1016/j.jenvman.2018.05.072.
  • Zhang, D. and Zhou, G., 2016. Estimation of soil moisture from optical and thermal remote sensing: a review. Sensors, 16 (8), 1308. doi:10.3390/s16081308.
  • Zhao, T., et al., 2021. Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm. Remote Sensing of Environment, 257, 112321. doi:10.1016/j.rse.2021.112321.
  • Zheng, G. and Moskal, L.M., 2009. Retrieving leaf area index (LAI) using remote sensing: theories, methods and sensors. Sensors, 9 (4), 2719–2745. doi:10.3390/s90402719.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.