All Issue

2024 Vol.57, Issue 9 Preview Page

Research Article

30 September 2024. pp. 615-626
Abstract
References
1

Adab, H., Morbidelli, R., Saltalippi, C., Moradian, M., and Ghalhari, G.A.F. (2020). "Machine learning to estimate surface soil moisture from remote sensing data." Water, Vol. 12, No, 11, 3223.

10.3390/w12113223
2

Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019). "Optuna: A next-generation hyperparameter optimization framework." In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, U.S., pp. 2623-2631.

10.1145/3292500.3330701
3

Chung, J., Son, M., Lee, Y., and Kim, S. (2021). "Estimation of soil moisture using Sentinel-1 SAR images and multiple linear regression model considering antecedent precipitations." Korean Journal of Remote Sensing, Vol. 37, No.3, pp. 515-530.

4

Chung, J.H., Lee, Y.G., and Kim, S.J. (2020). "Research trends on estimation of soil moisture and hydrological components using synthetic aperture radar." Journal of the Korean Association of Geographic Information Studies, Vol. 23, No.3, pp. 26-67.

5

DeVries, B., Huang, C., Armston, J., Huang, W., Jones, J.W., and Lang, M.W. (2020). "Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine." Remote Sensing of Environment, Vol. 240, 111664.

10.1016/j.rse.2020.111664
6

Dorigo, W.A., Xaver, A., Vreugdenhil, M., Gruber, A., Hegyiova, A., Sanchis-Dufau, A.D., Zamojski, D., Cordes, C. Wagner, W., and Drusch, M. (2013). "Global automated quality control of in situ soil moisture data from the International Soil Moisture Network." Vadose Zone Journal, Vol. 12, No. 3, pp. 1-21.

10.2136/vzj2012.0097
7

GDAL/OGR Contributors (2024). GDAL/OGR geospatial data abstraction software library, accessed 22 August 2024, <https://gdal.org>.

8

Hao, B., Ma, M., Li, S., Li, Q., Hao, D., Huang, J., Ge Z., Yang H., and Han, X. (2019). "Land use change and climate variation in the three gorges reservoir catchment from 2000 to 2015 based on the Google Earth Engine." Sensors, Vol. 19 No. 9, 2118.

10.3390/s19092118
9

Hao, Z., and AghaKouchak, A. (2013). "Multivariate standardized drought index: A parametric multi-index model." Advances in Water Resources, Vol. 57, pp. 12-18.

10.1016/j.advwatres.2013.03.009
10

He, X., Zhao, K., and Chu, X. (2021). "AutoML: A survey of the state-of-the-art." Knowledge-based Systems, Vol. 212, 106622.

10.1016/j.knosys.2020.106622
11

Hengl, T., Mendes de Jesus, J., Heuvelink, G.B., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M.N., Geng, X., Marschallinger, B.B., Guevara, M. A., Vargas, R., MacMillan, R.A., Batjes, N.H., Leenaars, J.G.B. Ribeiro, E., Wheeler, I., Mantel, S., and Kempen, B. (2017). "SoilGrids250m: Global gridded soil information based on machine learning." PLoS One, Vol. 12. No. 2, e0169748.

10.1371/journal.pone.0169748
12

Huang, Q., Long, D., Du, M., Zeng, C., Qiao, G., Li, X., Hou A., and Hong, Y. (2018). "Discharge estimation in high-mountain regions with improved methods using multisource remote sensing: A case study of the Upper Brahmaputra River." Remote Sensing of Environment, Vol. 219, pp. 115-134.

10.1016/j.rse.2018.10.008
13

Huffman, G.J., Bolvin, D.T., Braithwaite, D., Hsu, K., Joyce, R., Xie, P., and Yoo, S.H. (2015). NASA Global Precipitation Measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG), Algorithm Theoretical Basis Document (ATBD), NASA/GSFC, Greenbelt, MD, U.S.

14

Jarvis, A., Reuter, H.I., Nelson, A., and Guevara, E. (2008). Hole-filled seamless SRTM data V4, International Centre for Tropical Agriculture (CIAT), accessed 22 July 2024, <https://srtm.csi.cgiar.org>.

15

Kim, G.S., and Kim, J.P. (2011). "Correlation analysis between soil moisture retrieved from satellite images and ground network measurements." Journal of the Korean Association of Geographic Information Studies, Vol. 14, No.2, pp. 69-81.

10.11108/kagis.2011.14.2.069
16

Knipling, E.B. (1970). "Physical, and physiological basis for the reflectance of visible and near-infrared radiation from vegetation." Remote Sensing of Environment Vol. 1 No. 3, pp. 155-159.

10.1016/S0034-4257(70)80021-9
17

Lee, C.S., Sohn, E., Park, J.D., and Jang, J.D. (2019). "Estimation of soil moisture using deep learning based on satellite data: A case study of South Korea." GIScience & Remote Sensing, Vol. 56, No. 1, pp. 43-67.

10.1080/15481603.2018.1489943
18

Lee, S., Hong, S., Cho, J., and Lee, Y. (2017). "Experimental retrieval of soil moisture for cropland in South Korea using Sentinel-1 SAR data." Korean Journal of Remote Sensing, Vol. 33, No. 6-1, pp. 947-960.

19

Lee, Y.G., Kim, S.H., Ahn, S.R., Choi, M.H., Lim, K.S., and Kim, S.J. (2015). "Estimation of spatial evapotranspiration using Terra MODIS satellite image and SEBAL model - A case of Yongdam dam watershed." Journal of the Korean Association of Geographic Information Studies, Vol. 18, No. 1, pp. 90-104.

10.11108/kagis.2015.18.1.090
20

Lee, Y.J., Kim, G.Y., Lee, Y.G., Jeong, J.H., and Choi, M.H. (2020). "Introduction and development direction of various soil moisture measurement methods." Water for Future, Vol. 53, No. 10, pp. 126-134.

21

Liu, Y., Yang, Y., Jing, W., and Yue, X. (2017). "Comparison of different machine learning approaches for monthly satellite- based soil moisture downscaling over Northeast China." Remote Sensing, Vol. 10, No. 1, 31.

10.3390/rs10010031
22

McKee, T.B., Doesken, N.J., and Kleist, J. (1993). "The relationship of drought frequency and duration of time scales." 8th Conference on Applied Climatology, Aneheim, CA, U.S., pp. 179-183.

23

Narasimhan, B., Srinivasan, R., Arnold, J.G., and Di Luzio, M. (2005). "Estimation of long-term soil moisture using a distributed parameter hydrologic model and verification using remotely sensed data." Transactions of the ASAE, Vol. 48, No. 3, pp. 1101-1113.

10.13031/2013.18520
24

Nguyen, T.T., Ngo, H.H., Guo, W., Chang, S.W., Nguyen, D.D., Nguyen, C.T., Zhang J. Liang S. Bui X.T., and Hoang, N.B. (2022). "A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm." Science of the Total Environment, Vol. 833, 155066.

10.1016/j.scitotenv.2022.155066
25

PyCaret (2020). PyCaret: An open source, low-code machine learning library in Python, accessed 22, August 2024, <https://pycaret.gitbook.io/docs>.

26

Rulinda, C.M., Dilo, A., Bijker, W., and Stein, A. (2012). "Characterising and quantifying vegetative drought in East Africa using fuzzy modelling and NDVI data." Journal of Arid Environments, Vol. 78, pp. 169-178.

10.1016/j.jaridenv.2011.11.016
27

Rural Development Administration (RDA) (2024). Agricultural weather 365, accessed 22 August 2024, <https://weather.rda.go.kr>.

28

Senanayake, I.P., Yeo, I.Y., Walker, J.P., and Willgoose, G.R. (2021). "Estimating catchment scale soil moisture at a high spatial resolution: Integrating remote sensing and machine learning." Science of The Total Environment, Vol. 776, 145924.

10.1016/j.scitotenv.2021.145924
29

Tucker, C.J., (1979). "Red and photographic infrared linear combinations for monitoring vegetation." Remote Sensing of Environment, Vol. 8, No. 2, pp. 127-150.

10.1016/0034-4257(79)90013-0
30

Veysi, S., and Galehban E. (2024). "Chapter 4 - Conceptual of soil moisture based on remote sensing and reanalysis dataset." Remote sensing of soil and land surface processes monitoring, mapping, and modeling, Edited by Melesse, A.M., Rahmati, O., Khosravi, K., and Petropoulos G.P., Elsevier, Amsterdam, Nederlands, pp. 77-98.

10.1016/B978-0-443-15341-9.00020-4
31

Wan, Z. (2007). Collection-5 MODIS land surface temperature products users' guide. ICESS, University of California, Santa Barbara, CA, U.S.

32

Wan, Z., and Dozier, J. (1996). "A generalized split-window algorithm for retrieving land-surface temperature from space." IEEE Transactions on Geoscience and Remote Sensing, Vol. 34, No. 4, pp. 892-905.

10.1109/36.508406
33

Zhang, T., Zhou, Y., Zhu, Z., Li, X., and Asrar, G.R. (2021). "A global seamless 1 km resolution daily land surface temperature dataset (2003-2020)." Earth System Science Data Discussions, Vol. 14, No. 2, pp. 1-16.

10.5194/essd-14-651-2022
34

Zhao, Q., Yu, L., Li, X., Peng, D., Zhang, Y., and Gong, P. (2021). "Progress and trends in the application of Google Earth and Google Earth Engine." Remote Sensing, Vol. 13, No. 18, 3778.

10.3390/rs13183778
Information
  • Publisher :KOREA WATER RESOURECES ASSOCIATION
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 57
  • No :9
  • Pages :615-626
  • Received Date : 2024-06-24
  • Revised Date : 2024-08-23
  • Accepted Date : 2024-09-09