Have a personal or library account? Click to login
Estimation of Bare Soil Moisture from Remote Sensing Indices in the 0.4–2.5 mm Spectral Range Cover

Estimation of Bare Soil Moisture from Remote Sensing Indices in the 0.4–2.5 mm Spectral Range

Open Access
|Jul 2021

References

  1. [1] Yue, J., Tian, J., Tian, Q., Xu, K. and Xu, N., 2019, “Development of soil moisture indices from differences in water absorption between shortwave-infrared bands,” ISPRS J. Photogramm. Remote Sens., 154, 216-230. DOI: 10.1016/j.isprsjprs.2019.06.012.10.1016/j.isprsjprs.2019.06.012
  2. [2] Fabre, S., Briottet, X. and Lesaignoux, A., 2015, “Estimation of soil moisture content from the spectral reflectance of bare soils in the 0.4–2.5 μm domain,” Sensors (Switzerland), 15(2), 3262-3281. DOI: 10.3390/s150203262.10.3390/s150203262436735825648710
  3. [3] Gholami Bidkhani, N.O. and Mobasheri, M. R., 2018, “Influence of soil texture on the estimation of bare soil moisture content using MODIS images,” Eur. J. Remote Sens., 51(1), 911-920. DOI: 10.1080/22797254.2018.1514986.10.1080/22797254.2018.1514986
  4. [4] Susha Lekshmi, S. U., Singh, D. N. and Shojaei Baghini, M., 2014, “A critical review of soil moisture measurement,” Measurement: Journal of the International Measurement Confederation, (54), 92-105. DOI: 10.1016/j.measurement.2014.04.007.10.1016/j.measurement.2014.04.007
  5. [5] Wagner, W., Lemoine, G. and Rott, H., 1999, “A method for estimating soil moisture from ERS Scatterometer and soil data,” Remote Sens. Environ., 70(2), 191-207. DOI: 10.1016/S0034-4257(99)00036-X.10.1016/S0034-4257(99)00036-X
  6. [6] Zhang, D. and Zhou, G., 2016, “Estimation of soil moisture from optical and thermal remote sensing: A review,” Sensors (Switzerland), 16(8). MDPI AG. DOI: 10.3390/s16081308.10.3390/s16081308501747327548168
  7. [7] Zhang, C., Mishra, D. R. and Pennings, S. C., 2019 “Mapping salt marsh soil properties using imaging spectroscopy,” ISPRS J. Photogramm. Remote Sens., 148, 221-234. DOI: 10.1016/j.isprsjprs.2019.01.006.10.1016/j.isprsjprs.2019.01.006
  8. [8] Oltra-Carrió, R., Baup, F., Fabre, S., Fieuzal, R. and Briottet, X., 2015, “Improvement of Soil Moisture Retrieval from Hyperspectral VNIR-SWIR Data Using Clay Content Information: From Laboratory to Field Experiments,” Remote Sens., 7(3), 3184-3205. DOI: 10.3390/rs70303184.10.3390/rs70303184
  9. [9] Gillies, R. R., Carlson, T. N., Cui, J., Kustas, W. P. and Humes, K. S., 1997, “A verification of the ‘triangle’ method for obtaining surface soil water content and energy fluxes from remote measurements of the normalized difference vegetation index (ndvi) and surface e,” Int. J. Remote Sens., 18(15), 3145-3166. DOI: 10.1080/014311697217026.10.1080/014311697217026
  10. [10] Shi, J., Wang, J., Hsu, A. Y., O’Neill, P. E. and Engman, E. T., 1997, “Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data,” IEEE Trans. Geosci. Remote Sens., 35(5), 1254-1266. DOI: 10.1109/36.628792.10.1109/36.628792
  11. [11] Ghulam, A., Qin, Q., Teyip, T. and Li, Z. I., 2007, “Modified perpendicular drought index (MPDI): a real-time drought monitoring method,” ISPRS J. Photogramm. Remote Sens., 62(2), 150-164. DOI: 10.1016/j.isprsjprs.2007.03.002.10.1016/j.isprsjprs.2007.03.002
  12. [12] Casamitjana, M., Torres-Madroñero, M. C., Bernal-Riobo, J. and Varga, D., 2020, “Soil Moisture Analysis by Means of Multispectral Images According to Land Use and Spatial Resolution on Andosols in the Colombian Andes,” Appl. Sci., 10(16), 5540-5555. DOI: 10.3390/app10165540.10.3390/app10165540
  13. [13] Wang, J., Ling, Z., Wang, Y. and Zeng, H., 2016, “Improving spatial representation of soil moisture by integration of microwave observations and the temperature-vegetation-drought index derived from MODIS products,” ISPRS J. Photogramm. Remote Sens., 113, 144-154. DOI: 10.1016/j.isprsjprs.2016.01.009.10.1016/j.isprsjprs.2016.01.009
  14. [14] Bablet, A. et al., 2018, “MARMIT: A multilayer radiative transfer model of soil reflectance to estimate surface soil moisture content in the solar domain (400–2500 nm),” Remote Sens. Environ., 217, 1-17. DOI: 10.1016/j.rse.2018.07.031.10.1016/j.rse.2018.07.031
  15. [15] Mohamed, E. S., Saleh, A. M., Belal, A. B. and Gad, A. A., 2018, “Application of near-infrared reflectance for quantitative assessment of soil properties,” Egyptian Journal of Remote Sensing and Space Science, 21(1), 1-14. DOI: 10.1016/j.ejrs.2017.02.001.10.1016/j.ejrs.2017.02.001
  16. [16] Bowers, S. A. and Smith, S. J., 1972, “Spectrophotometric Determination of Soil Water Content,” Soil Sci. Soc. Am. J., 36(6), pp. 978-980. DOI: 10.2136/sssaj1972.03615995003600060045x.10.2136/sssaj1972.03615995003600060045x
  17. [17] Whalley, W. R., Leeds-Harrison, P. B. and Bowman, G. E., 1991, “Estimation of soil moisture status using near infrared reflectance,” Hydrol. Process., 5(3), 321-327. DOI: 10.1002/hyp.3360050312.10.1002/hyp.3360050312
  18. [18] Haubrock, S. N., Chabrillat, S., Lemmnitz, C. and Kaufmann, H., 2008, “Surface soil moisture quantification models from reflectance data under field conditions,” Int. J. Remote Sens., 29(1), 3-29. DOI: 10.1080/01431160701294695.10.1080/01431160701294695
  19. [19] Zhang, N., Hong, Y., Qin, Q. and Liu, L., 2013, “VSDI: A visible and shortwave infrared drought index for monitoring soil and vegetation moisture based on optical remote sensing,” Int. J. Remote Sens., 34(13), 4585-4609. DOI: 10.1080/01431161.2013.779046.10.1080/01431161.2013.779046
  20. [20] Knowles, O. and Dawson A., 2018, “Current soil sampling methods – a review”, in: Farm environmental planning – Science, policy and practice. (L.D. Currie and C.L. Christensen, Eds), Occasional Report No. 31, Fertilizer and Lime Research Centre, Massey University, Palmerston North, New Zealand.
  21. [21] Scrimgeour, C., 2008, “Soil Sampling and Methods of Analysis”, in: Experimental Agriculture, M.R. Carter and E.G. Gregorich, Eds, Boca Raton, Fl, USA: CRC Press, 44(3). DOI: 10.1017/S0014479708006546.10.1017/S0014479708006546
  22. [22] Chen, J. M., 1996, “Evaluation of vegetation indices and a modified simple ratio for boreal applications,” Can. J. Remote Sens., 22(3), 229-242. DOI: 10.1080/07038992.1996.10855178.10.1080/07038992.1996.10855178
  23. [23] Gomez, C. and Lagacherie, P., 2016, “Mapping of Primary Soil Properties Using Optical Visible and Near Infrared (Vis-NIR) Remote Sensing”, in “Land Surface Remote Sensing in Agriculture and Forest”, Nicolas Baghdadi, Mehrez Zribi, Eds, Elsevier, 1-35.10.1016/B978-1-78548-103-1.50001-7
  24. [24] Ansari, S., Padmanabhi, A. D. and Deshmukh, D. R. R., 2017, “Spectral Estimation of Soil Moisture Content using Semi Empirical Soil Model in the 0.4–2.5 µm Domain,” Int. J. Comput. Appl. Technol. Res., 6(07), 338-343. DOI: 10.7753/ijcatr0607.1013.10.7753/IJCATR0607.1013
  25. [25] Li, C. et al., 2016, “Hyperspectral parameters and prediction model of soil moisture in coastal saline,” Chinese J. Appl. Ecol., 27(2), 525-531. DOI: 10.13287/j.1001-9332.201602.019.
  26. [26] Huuskonen, J. and Oksanen, T., 2018, “Soil sampling with drones and augmented reality in precision agriculture,” Comput. Electron. Agric., 154, 25-35. DOI: 10.1016/j.compag.2018.08.039.10.1016/j.compag.2018.08.039
  27. [27] Mazzoleni, M., Paron, P., Reali, A., Juizo, D., Manane, J. and Brandimarte, L., 2020, “Testing UAV-derived topography for hydraulic modelling in a tropical environment,” Nat. Hazards, 103(1), 139-163. DOI: 10.1007/s11069-020-03963-4.10.1007/s11069-020-03963-4
  28. [28] Kacprzak, M. and Rotchimmel, K., 2016, “Creating photogrammetry products with photos acquired by array of non-metric cameras,” Trans. Inst. Aviat., 243(2), 120-129, (in Polish). DOI: 10.5604/05096669.1205266.10.5604/05096669.1205266
  29. [29] Gitelson, A. A. and Merzlyak, M. N., 1998, “Remote sensing of chlorophyll concentration in higher plant leaves,” Adv. Sp. Res., 22(5), 689-692. DOI: 10.1016/S0273-1177(97)01133-2.10.1016/S0273-1177(97)01133-2
  30. [30] Lu, F., Sun, Y., and Hou, F., 2020, “Using UAV visible images to estimate the soil moisture of steppe,” Water (Switzerland), 12(9), 2334-2351. DOI: 10.3390/W12092334.10.3390/w12092334
  31. [31] Jasiewicz, J., Zwoliński, Zb., Mitasova, H., Hengl, T. (Eds.), 2015, Geomorphometry for Geosciences, Bogucki Wydawnictwo Naukowe, Adam Mickiewicz University in Poznań – Institute of Geoecology and Geoinformation.
  32. [32] Buters, T., Belton, D. and Cross, A., 2019, “Seed and Seedling Detection Using Unmanned Aerial Vehicles and Automated Image Classification in the Monitoring of Ecological Recovery,” Drones, 3(3), 53-69. DOI: 10.3390/drones3030053.10.3390/drones3030053
  33. [33] Wójtowicz, A. Wójtowicz, M. and Piekarczyk, J., 2016, “Application of remote sensing methods in agriculture,” CBCS, 11(1), 31-50.
  34. [34] Al-Gaadi, K. A. et al., 2016, “Prediction of potato crop yield using precision agriculture techniques,” PLoS One, 11(9). DOI: 10.1371/journal.pone.0162219.10.1371/journal.pone.0162219501778727611577
  35. [35] Kotlarz, J., Nasiłowska, S., Rotchimmel, K., Kubiak, K. and Kacprzak, M., 2018, “Species Diversity of Oak Stands and Its Significance for Drought Resistance,” Forests, 9(3), 126-148. DOI: 10.3390/f9030126.10.3390/f9030126
  36. [36] Chmielewski, S., Bochniak, A., Natapov, A. and Wezyk, P., 2020, “Introducing GEOBIA to landscape imageability assessment: A multi-temporal case study of the nature reserve ‘Kozki’, Poland,” Remote Sens., 12(17), 2792-2817. DOI: 10.3390/RS12172792.10.3390/rs12172792
  37. [37] Jeihouni, M., Alavipanah, S. K., Toomanian, A. and Jafarzadeh, A. A., 2020, “Digital mapping of soil moisture retention properties using solely satellite-based data and data mining techniques,” J. Hydrol., 585, Article ID 124786. DOI: 10.1016/j.jhydrol.2020.124786.10.1016/j.jhydrol.2020.124786
Language: English
Page range: 1 - 11
Published on: Jul 1, 2021
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2021 Kubiak Katarzyna, Stypułkowska Justyna, Szymański Jakub, Spiralski Marcin, published by ŁUKASIEWICZ RESEARCH NETWORK – INSTITUTE OF AVIATION
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.