Browse > Article
http://dx.doi.org/10.7780/kjrs.2022.38.6.1.14

Research Status of Satellite-based Evapotranspiration and Soil Moisture Estimations in South Korea  

Choi, Ga-young (Water Resources and Environmental Research Center, K-water Research Institute)
Cho, Younghyun (Water Resources and Environmental Research Center, K-water Research Institute)
Publication Information
Korean Journal of Remote Sensing / v.38, no.6_1, 2022 , pp. 1141-1180 More about this Journal
Abstract
The application of satellite imageries has increased in the field of hydrology and water resources in recent years. However, challenges have been encountered on obtaining accurate evapotranspiration and soil moisture. Therefore, present researches have emphasized the necessity to obtain estimations of satellite-based evapotranspiration and soil moisture with related development researches. In this study, we presented the research status in Korea by investigating the current trends and methodologies for evapotranspiration and soil moisture. As a result of examining the detailed methodologies, we have ascertained that, in general, evapotranspiration is estimated using Energy balance models, such as Surface Energy Balance Algorithm for Land (SEBAL) and Mapping Evapotranspiration with Internalized Calibration (METRIC). In addition, Penman-Monteith and Priestley-Taylor equations are also used to estimate evapotranspiration. In the case of soil moisture, in general, active (AMSR-E, AMSR2, MIRAS, and SMAP) and passive (ASCAT and SAR)sensors are used for estimation. In terms of statistics, deep learning, as well as linear regression equations and artificial neural networks, are used for estimating these parameters. There were a number of research cases in which various indices were calculated using satellite-based data and applied to the characterization of drought. In some cases, hydrological cycle factors of evapotranspiration and soil moisture were calculated based on the Land Surface Model (LSM). Through this process, by comparing, reviewing, and presenting major detailed methodologies, we intend to use these references in related research, and lay the foundation for the advancement of researches on the calculation of satellite-based hydrological cycle data in the future.
Keywords
Evapotranspiration; Soil moisture; Satellite imagery; Remote sensing;
Citations & Related Records
Times Cited By KSCI : 53  (Citation Analysis)
연도 인용수 순위
1 Baik, J.J. and M.H. Choi, 2014. Actual evapotranspiration evaluation using Communication, Ocean and Meteorological satellite, Proc. of Korean Society for Civil Engineers Conference, Daegu, Korea, Oct. 22-24, pp. 215-216.
2 Morton, F.I., 1978. Estimating evapotranspiration from potential evaporation: practicality of an iconoclastic approach, Journal of Hydrology, 38(1-2): 1-32. https://doi.org/10.1016/0022-1694(78)90129-4   DOI
3 Mu, Q., M. Zhao, and S.W. Running, 2013. MODIS global terrestrial evapotranspiration (ET) product (NASA MOD16A2/A3)-Algorithm Theoretical Basis Document, Collection 5, Algorithm Theoretical Basis Document, National Aeronautics and Space Administration, Washington, D.C., USA.
4 Njoku, E.G. and L. Li, 1999. Retrieval of land surface parameters using passive microwave measurements at 6-18 GHz, IEEE Transactions on Geoscience and Remote Sensing, 37(1): 79-93. https://doi.org/10.1109/36.739125   DOI
5 Ulaby, F.T., R.K. Moore, and A.K. Fung, 1982. Microwave remote sensing active and passive Volume 2-Radar remote sensing and surface scattering and emission theory, Addison-Wesley, Boston, MA, USA, pp. 848-902.
6 Wagner, W., 1998. Soil moisture retrieval from ERS scatterometer data, Vienna University of Technology, Vienna, Austria.
7 Wang, J.R. and T.J. Schmugge, 1980. An empirical model for the complex dielectric permittivity of soils as a function of water content, IEEE Transactions on Geoscience and Remote Sensing, GE-18(4): 288-295. https://doi.org/10.1109/TGRS.1980.350304   DOI
8 Wright, J.L., 1982. New evapotranspiration crop coefficients, Journal of the Irrigation and Drainage Division, 108(1): 57-74. https://doi.org/10.1061/JRCEA4.0001372   DOI
9 Zhang, K., J.S. Kimball, and S.W. Running, 2016. A review of remote sensing based actual evapotranspiration estimation, Wiley Interdisciplinary Reviews: Water, 3(6): 834-853. https://doi.org/10.1002/wat2.1168   DOI
10 Owe, M., R. de Jeu, and J. Walker, 2001. A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index, IEEE Transactions on Geoscience and Remote Sensing, 39(8): 1643-1654. https://doi.org/10.1109/36.942542   DOI
11 Parinussa, R.M., T.R. Holmes, N. Wanders, W.A. Dorigo, and R. A. de Jeu, 2015. A preliminary study toward consistent soil moisture from AMSR2, Journal of Hydrometeorology, 16(2): 932-947. https://doi.org/10.1175/JHM-D-13-0200.1   DOI
12 Koster, R.D., M.J. Suarez, A. Ducharne, M. Stieglitz, and P. Kumar, 2000. A catchment-based approach to modeling land surface processes in a general circulation model: 1. Model structure, Journal of Geophysical Research: Atmospheres, 105(D20): 24809-24822. https://doi.org/10.1029/2000JD900327   DOI
13 Kroes, J., J. Van Dam, J. Huygen, and R. Vervoort. 1999. User's Guide of SWAP version 2.0. Simulation of water flow, solute transport and plant growth in the Soil-Water-Atmosphere-Plant environment, Technical Document 48, DLO Winand Staring Centre, Wageningen, Netherlands.
14 Kustas, W.P., 1995. Recent advances associated with large scale field experiments in hydrology, Reviews of Geophysics, 33(S2): 959-965. https://doi.org/10.1029/95RG00395   DOI
15 Kustas, W. and J. Norman, 1996. Use of remote sensing for evapotranspiration monitoring over land surfaces, Hydrological Sciences Journal, 41(4): 495-516. https://doi.org/10.1080/02626669609491522   DOI
16 Anderson, M.C., C. Hain, J. Otkin, X. Zhan, K. Mo, M. Svoboda, B. Wardlow, and A. Pimstein, 2013. An intercomparison of drought indicators based on thermal remote sensing and NLDAS-2 simulations with US Drought Monitor classifications, Journal of Hydrometeorology, 14(4): 1035-1056. https://doi.org/10.1175/JHM-D-12-0140.1   DOI
17 Wigneron, J.-P., J.-C. Calvet, T. Pellarin, A. A. Van de Griend, M. Berger, and P. Ferrazzoli, 2003. Retrieving near-surface soil moisture from microwave radiometric observations: current status and future plans, Remote Sensing of Environment, 85(4): 489-506. https://doi.org/10.1016/S0034-4257(03)00051-8   DOI
18 Mu, Q., F.A. Heinsch, M. Zhao, and S.W. Running, 2007. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data, Remote Sensing of Environment, 111(4): 519-536. https://doi.org/10.1016/j.rse.2007.04.015   DOI
19 D'urso, G. and M. Minacapilli, 2006. A semi-empirical approach for surface soil water content estimation from radar data without a-priori information on surface roughness, Journal of Hydrology, 321(1-4): 297-310. https://doi.org/10.1016/j.jhydrol.2005.08.013   DOI
20 Cho, S.K., J.H. Jeong, S.C. Lee, and M.H. Choi, 2020. A estimation of soil moisture based on sentinel-1 SAR data: focusing on cropland and grassland area, Journal of Korea Water Resources Association, 53(11): 973-983. https://doi.org/10.3741/JKWRA.2020.53.11.973   DOI
21 Parrens, M., J.-P. Wigneron, P. Richaume, A. Al Bitar, A. Mialon, R. Fernandez-Moran, A. Al-Yaari, P. O'Neill, and Y. Kerr, 2017. Considering combined or separated roughness and vegetation effects in soil moisture retrievals, International Journal of Applied Earth Observation and Geoinformation, 55: 73-86. https://doi.org/10.1016/j.jag.2016.11.001   DOI
22 Reichle, R.H., 2008. Data assimilation methods in the Earth sciences, Advances in Water Resources, 31(11): 1411-1418. https://doi.org/10.1016/j.advwatres.2008.01.001   DOI
23 Mecikalski, J.R., G.R. Diak, M.C. Anderson, and J.M. Norman, 1999. Estimating fluxes on continental scales using remotely sensed data in an atmospheric-land exchange model, Journal of Applied Meteorology, 38(9): 1352-1369. https://doi.org/10.1175/1520-0450(1999)038<1352:EFOCSU>2.0.CO;2   DOI
24 Owe, M., R. de Jeu, and T. Holmes, 2008. Multisensor historical climatology of satellite-derived global land surface moisture, Journal of Geophysical Research: Earth Surface, 113(F1): 1-17. https://doi.org/10.1029/2007JF000769   DOI
25 Park, G.H., W.S. Yu, E.H. Hwang, and K.S. Jung, 2020. Calculation of soil moisture and evaporation on the Korean Peninsula using NASA LIS (Land Information System), Journal of the Korean Association of Geographic Information Studies, 23(4): 83-100. https://doi.org/10.11108/kagis.2020.23.4.083   DOI
26 Piles, M., A. Camps, M. Vall-Llossera, I. Corbella, R. Panciera, C. Rudiger, Y.H. Kerr, and J. Walker, 2011. Downscaling SMOS-derived soil moisture using MODIS visible/infrared data, IEEE Transactions on Geoscience and Remote Sensing, 49(9): 3156-3166. https://doi.org/10.1109/TGRS.2011.2120615   DOI
27 Priestley, C.H.B. and R.J. Taylor, 1972. On the assessment of surface heat flux and evaporation using largescale parameters, Monthly Weather Review, 100(2): 81-92. https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2   DOI
28 Wagner, W., S. Hahn, R. Kidd, T. Melzer, Z. Bartalis, S. Hasenauer, J. Figa, P. De Rosnay, A. Jann, and S. Schneider, 2013. The ASCAT Soil Moisture Product: A Review of its, Meteorologische Zeitschrift, 22(1): 1-29. https://doi.org/10.1127/0941-2948/2013/0399   DOI
29 Bastiaanssen, W.G., 2000. SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey, Journal of Hydrology, 229(1-2): 87-100. https://doi.org/10.1016/S0022-1694(99)00202-4   DOI
30 Wagner, W., G. Lemoine, and H. Rott, 1999. A method for estimating soil moisture from ERS scatterometer and soil data, Remote Sensing of Environment, 70(2): 191-207. https://doi.org/10.1016/S0034-4257(99)00036-X   DOI
31 Waters, R., R. Allen, W. Bastiaanssen, M. Tasumi, and R. Trezza, 2002. SEBAL-Surface Energy Balance Algorithms for Land, Idaho Implementation, Advanced Training and Users Manual, University of Idaho, Moscow, ID, USA.
32 Koike, T., Y. Nakamura, I. Kaihotsu, G. Davaa, N. Matsuura, K. Tamagawa, and H. Fujii, 2004. Development of an advanced microwave scanning radiometer (AMSR-E) algorithm for soil moisture and vegetation water content, Proceedings of Hydraulic Engineering, 48: 217-222. https://doi.org/10.2208/prohe.48.217   DOI
33 Kim, H.L. and M.H. Choi, 2015. An Inter-comparison of Active and Passive satellite Soil Moisture Products in East Asia for Dust-Outbreak Prediction, Journal of the Korean Society of Hazard Mitigation, 15(4): 53-58. http://dx.doi.org/10.9798/KOSHAM.2015.15.4.53   DOI
34 Kim, H.L., W.Y. Sunwoo, S.K. Kim, and M.H. Choi, 2016b. Construction and estimation of soil moisture site with FDR and COSMIC-ray (SM-FC) sensors for calibration/validation of satellite-based and COSMIC-ray soil moisture products in Sungkyunkwan university, South Korea, Journal of Korea Water Resources Association, 49(2): 133-144. https://doi.org/10.3741/JKWRA.2016.49.2.133   DOI
35 Kim, Y.H., K.J. Kim, S.J. Lee, J.W. Kim, and Y.W. Lee, 2017b. Deep Learning-based Retrieval of Daily 500-m Soil Moisture for South Korea, Journal of the Korean Cartographic Association, 17(3): 109-121. https://doi.org/10.16879/jkca2017.17.3.109   DOI
36 Lee, Y.G., S.H. Kim, S.R. Ahn, M.H. Choi, K.S. Lim, and S.J. Kim, 2015a. 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, 18(1): 90-104. https://doi.org/10.11108/kagis.2015.18.1.090   DOI
37 Kim, H.L., S.K. Kim, J.H. Jeong, I.C. Shin, J.H. Shin, and M.H. Choi, 2016a. Revising Passive Satellite-based Soil Moisture Retrievals over East Asia Using SMOS (MIRAS) and GCOM-W1 (AMSR2) Satellite and GLDAS Dataset, Journal of Wetlands Research, 18 (2): 132-147. https://doi.org/10.17663/JWR.2016.18.2.132   DOI
38 Kim, S.W., T.H. Lee, B.S. Chun, Y.H. Jung, W.S. Jang, C.Y. Sur, and Y.C. Shin, 2020. Estimation of High-Resolution Soil Moisture Using Sentinel1A/B SAR and Soil Moisture Data Assimilation Scheme, Journal of The Korean Society of Agricultural Engineers, 62(6): 11-20. https://doi.org/10.5389/KSAE.2020.62.6.011   DOI
39 Kustas, W.P. and J.M. Norman, 1999b. Reply to comments about the basic equations of dual-source vegetation-atmosphere transfer models, Agricultural and Forest Meteorology, 94(3-4): 275-278. https://doi.org/10.1016/S0168-1923(99)00012-X   DOI
40 Gruber, A., C.-H. Su, S. Zwieback, W. Crow, W. Dorigo, and W. Wagner, 2016. Recent advances in (soil moisture) triple collocation analysis, International Journal of Applied Earth Observation and Geoinformation, 45: 200-211. https://doi.org/10.1016/j.jag.2015.09.002   DOI
41 Ha, R., H.J. Shin, M.S. Lee, and K.S. Joon, 2010. Estimation of Spatial Evapotranspiration Using satellite images and SEBAL Model, KSCE Journal of Civil Engineering, 30(3B): 233-242.
42 Hallikainen, M.T., F.T. Ulaby, M.C. Dobson, M.A. El-Rayes, and L.-K. Wu, 1985. Microwave dielectric behavior of wet soil-part 1: Empirical models and experimental observations, IEEE Transactions on Geoscience and Remote Sensing, 23(1): 25-34. https://doi.org/10.1109/TGRS.1985.289497   DOI
43 Lettenmaier, D.P., D. Alsdorf, J. Dozier, G.J. Huffman, M. Pan, and E.F. Wood, 2015. Inroads of remote sensing into hydrologic science during the WRR era, Water Resources Research, 51(9): 7309-7342. https://doi.org/10.1002/2015WR017616   DOI
44 Anderson, C., J.A.D. Hildreth, and L. Howland, 2015. Is the desire for status a fundamental human motive? A review of the empirical literature, Psychological Bulletin, 141(3): 574. https://doi.org/10.1037/a0038781   DOI
45 Anonymous, 2015. High-Resolution Global Soil Moisture Map, https://www.jpl.nasa.gov/images/pia19337-high-resolution-global-soil-moisture-map, Accessed on Nov. 10, 2022.
46 Baghdadi, N., M. Zribi, C. Loumagne, P. Ansart, and T. P. Anguela, 2008. Analysis of TerraSAR-X data and their sensitivity to soil surface parameters over bare agricultural fields, Remote Sensing of Environment, 112(12): 4370-4379. https://doi.org/10.1016/j.rse.2008.08.004   DOI
47 Fisher, J.B., K.P. Tu, and D.D. Baldocchi, 2008. Global estimates of the land-atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites, Remote Sensing of Environment, 112(3): 901-919. https://doi.org/10.1016/j.rse.2007.06.025   DOI
48 Kumar, S.V., R.H. Reichle, C.D. Peters-Lidard, R.D. Koster, X. Zhan, W. T. Crow, J. B. Eylander, and P.R. Houser, 2008. A land surface data assimilation framework using the land information system: Description and applications, Advances in Water Resources, 31(11): 1419-1432. https://doi.org/10.1016/j.advwatres.2008.01.013   DOI
49 Jackson, T. and D.E. Le Vine, 1996. Mapping surface soil moisture using an aircraft-based passive microwave instrument: Algorithm and example, Journal of Hydrology, 184(1-2): 85-99. https://doi.org/10.1016/0022-1694(95)02969-9   DOI
50 Jang, K.C., S.K. Kang, H.W. Kim, and H.J. Kwon, 2009. Evaluation of shortwave irradiance and evapotranspiration derived from Moderate Resolution Imaging Spectroradiometer (MODIS), Asia-Pacific Journal of Atmospheric Sciences, 45(2): 233-246.
51 Li, Z.-L., R. Tang, Z. Wan, Y. Bi, C. Zhou, B. Tang, G. Yan, and X. Zhang, 2009. A review of current methodologies for regional evapotranspiration estimation from remotely sensed data, Sensors, 9(5): 3801-3853. https://doi.org/10.3390/s90503801   DOI
52 McDonough, K.R., S.L. Hutchinson, J.S. Hutchinson, J.L. Case, and V. Rahmani, 2018. Validation and assessment of SPoRT-LIS surface soil moisture estimates for water resources management applications, Journal of Hydrology, 566: 43-54. https://doi.org/10.1016/j.jhydrol.2018.09.007   DOI
53 Karthikeyan, L., M. Pan, N. Wanders, D.N. Kumar, and E.F. Wood, 2017. Four decades of microwave satellite soil moisture observations: Part 1. A review of retrieval algorithms, Advances in Water Resources, 109: 106-120. https://doi.org/10.1016/j.advwatres.2017.09.006   DOI
54 Jang, W.J., Y.W. Lee, J.W. Lee, and S.J. Kim, 2019. RNN-LSTM Based Soil Moisture Estimation Using Terra MODIS NDVI and LST, Journal of The Korean Society of Agricultural Engineers, 61(6): 123-132. https://doi.org/10.5389/KSAE.2019.61.6.123   DOI
55 Jeon, M.G., W.H. Nam, H.J. Lee, E.M. Hong, S.A. Hwang, and S.O. Hur, 2021b. Drought Risk Assessment for Upland Crops using Satellite-derived Evapotranspiration and Soil Available Water Capacity, Journal of the Korean Society of Hazard Mitigation, 21(1): 25-33. https://doi.org/10.9798/KOSHAM.2021.21.1.25   DOI
56 Jeong, S. and S.C. Shin, 2006. The Application of Satellite Imagery in Droughts Analysis of Large Area, Korea Spatial Information Society, 14(2): 55-62.
57 Jeong, J.H., J.J. Baik, and M.H. Choi, 2018. Estimation of dryness index based on COMS to monitoring the soil moisture status at the Korean peninsula, Journal of Korea Water Resources Association, 51(2): 89-98. https://doi.org/10.3741/JKWRA.2018.51.2.89   DOI
58 Jun, S.H., J.H. Park, K.O. Boo, and H.S. Kang, 2020. Analyzing off-line Noah land surface model spin-up behavior for initialization of global numerical weather prediction model, Journal of Korea Water Resources Association, 53(3): 181-191. https://doi.org/10.3741/JKWRA.2020.53.3.181   DOI
59 Kerr, Y.H., P. Waldteufel, J.P. Wigneron, S. Delwart, F. Cabot, J. Boutin, M.J. Escorihuela, J. Font, N. Reul, C. Gruhier, S.E. Juglea, M.R. Drinkwater, A. Hahne, M. Martin-Neira, and S. Mecklenburg, 2010. The SMOS Mission: New Tool for Monitoring Key Elements of the Global Water Cycle, Proceedings of the IEEE, 98(5): 666-687. https://doi.org/10.1109/JPROC.2010.2043032   DOI
60 Kim, J.H. and K.T. Kim, 2005. Estimation of Potential Evapotranspiration using LAI, Journal of the Korean Association of Geographic Information Studies, 8(4): 1-13.
61 Kim, G.S. and J.P. Kim, 2011. Correlation Analysis Between Soil Moisture Retrieved from Satellite Images and Ground Network Measurements, Journal of the Korean Association of Geographic Information Studies, 14(2): 69. https://doi.org/10.11108/kagis.2011.14.2.069   DOI
62 Chun, J.A., S.T. Kim, W.-S. Lee, and D. Kim, 2020. Assessment of Noah land surface model-based soil moisture using GRACE-observed TWSA and TWSC, Journal of Korea Water Resources Association, 53(4): 285-291. https://doi.org/10.3741/JKWRA.2020.53.4.285   DOI
63 Chung, J.H., M.B. Son, Y.W. Lee, and S.J. Kim, 2021. Estimation of Soil Moisture Using Sentinel-1 SAR Images and Multiple Linear Regression Model Considering Antecedent Precipitations, Korean Journal of Remote Sensing, 37(3): 515-530. https://doi.org/10.7780/kjrs.2021.37.3.12   DOI
64 Sheffield, J., E.F. Wood, M. Pan, H. Beck, G. Coccia, A. Serrat-Capdevila, and K. Verbist, 2018. Satellite remote sensing for water resources management: Potential for supporting sustainable development in data-poor regions, Water Resources Research, 54(12): 9724-9758. https://doi.org/10.1029/2017WR022437   DOI
65 Baik, J.J., J.H. Jeong, and M.H. Choi, 2018. Estimation of the optimal evapotranspiration by using satellite-and reanalysis model-based evapotranspiration estimations, Journal of Korea Water Resources Association, 51(3): 273-280. https://doi.org/10.3741/JKWRA.2018.51.3.273   DOI
66 Park, J.A. and G.S. Kim, 2011. Estimation of Spatial Distribution of Soil Moisture at Yongdam Dam Watershed Using Artificial Neural Networks, Journal of the Korean Geographical Society, 46(3): 319-330.
67 Park, G., C. Kye, K. Lee, W. Yu, E.-H. Hwang, and D. Kang, 2021a. Calculation of Soil Moisture and Evapotranspiration of KLDAS applying Ground-Observed Meteorological Data, Korean Journal of Remote Sensing, 37(6-1): 1611-1623 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2021.37.6.1.10   DOI
68 Park, G.H., K.T. Lee, C.W. Kye, W.S. Yu, E.H. Hwang, and D.H. Kang, 2021b. Calculation of Soil Moisture and Evapotranspiration for KLDAS (Korea Land Data Assimilation System) using Hydrometeorological Data Set, Journal of the Korean Association of Geographic Information Studies, 24(4): 65-81. https://doi.org/10.11108/kagis.2021.24.4.065   DOI
69 Sandholt, I., K. Rasmussen, and J. Andersen, 2002. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status, Remote Sensing of Environment, 79(2-3): 213-224. https://doi.org/10.1016/S0034-4257(01)00274-7   DOI
70 Scott, C.A., W.G. Bastiaanssen, and M.-U.-D. Ahmad, 2003. Mapping root zone soil moisture using remotely sensed optical imagery, Journal of Irrigation and Drainage Engineering, 129(5): 326-335. https://doi.org/10.1061/(ASCE)0733-9437(2003)129:5(326)   DOI
71 Maheu, A., F. Anctil, E. Gaborit, V. Fortin, D.F. Nadeau, and R. Therrien, 2018. A field evaluation of soil moisture modelling with the Soil, Vegetation, and Snow (SVS) land surface model using evapotranspiration observations as forcing data, Journal of Hydrology, 558: 532-545. https://doi.org/10.1016/j.jhydrol.2018.01.065   DOI
72 Chai, S.S., B. Veenendaal, G. West, and J.P. Walker, 2008. Backpropagation neural network for soil moisture retrieval using NAFE '05 data: a comparison of different training algorithms, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37: 1345-1349.
73 Shin, S.-C. and T.-Y. An, 2004. Estimation of areal evapotranspiration using NDVI and temperature data, Journal of the Korean Association of Geographic Information Studies, 7(3): 79-89.
74 Bindlish, R., T.J. Jackson, E. Wood, H. Gao, P. Starks, D. Bosch, and V. Lakshmi, 2003. Soil moisture estimates from TRMM Microwave Imager observations over the Southern United States, Remote Sensing of Environment, 85(4): 507-515. https://doi.org/10.1016/S0034-4257(03)00052-X   DOI
75 Kalma, J.D., T.R. McVicar, and M.F. McCabe, 2008. Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data, Surveys in Geophysics, 29(4): 421-469. https://doi.org/10.1007/s10712-008-9037-z   DOI
76 Kerr, Y.H., P. Waldteufel, P. Richaume, J.P. Wigneron, P. Ferrazzoli, A. Mahmoodi, A.A. Bitar, F. Cabot, C. Gruhier, S.E. Juglea, D. Leroux, A. Mialon, and S. Delwart, 2012. The SMOS Soil Moisture Retrieval Algorithm, IEEE Transactions on Geoscience and Remote Sensing, 50(5): 1384-1403. https://doi.org/10.1109/TGRS.2012.2184548   DOI
77 Lee, S.J., S.W. Hong, J.I. Cho, and Y.W. Lee, 2017b Experimental Retrieval of Soil Moisture for Cropland in South Korea Using Sentinel-1 SAR Data, Korean Journal of Remote Sensing, 33(6): 947-960. https://doi.org/10.7780/kjrs.2017.33.6.1.4   DOI
78 Lee, H.J., W.H. Nam, D.H. Yoon, H.Y. Kim, S.B. Woo, and D.E. Kim, 2021. Drought Monitoring for Paddy Fields Using Satellite-derived Evaporative Stress Index, Journal of The Korean Society of Agricultural Engineers, 63(3): 47-57. https://doi.org/10.5389/KSAE.2021.63.3.047   DOI
79 Miralles, D.G., T. Holmes, R. De Jeu, J. Gash, A. Meesters, and A. Dolman, 2011. Global land-surface evaporation estimated from satellite-based observations, Hydrology and Earth System Sciences, 15(2): 453-469. https://doi.org/10.5194/hessd-7-8479-2010   DOI
80 Monteith, J.L., 1965b. The state and movement of water in living organisms, Cambridge University Press, Cambridge, UK, pp. 205-234.
81 Allen, R.G., M. Tasumi, and R. Trezza, 2007b. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)-Model, Journal of Irrigation and Drainage Engineering, 133(4): 380-394. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:4(380)   DOI
82 Bastiaanssen, W.G., M. Menenti, R. Feddes, and A. Holtslag, 1998. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation, Journal of Hydrology, 212: 198-212. https://doi.org/10.1016/S0022-1694(98)00253-4   DOI
83 Attema, E. and F.T. Ulaby, 1978. Vegetation modeled as a water cloud, Radio Science, 13 (2): 357-364. https://doi.org/10.1029/RS013i002p00357   DOI
84 Baik, J.J., J.M. Park, and M.H. Choi, 2016. Assessment of actual evapotranspiration using modified satellite-based priestley-taylor algorithm using MODIS products, Journal of Korea Water Resources Association, 49(11): 903-912. https://doi.org/10.3741/JKWRA.2016.49.11.903   DOI
85 Balsamo, G., A. Agusti-Parareda, C. Albergel, G. Arduini, A. Beljaars, J. Bidlot, E. Blyth, N. Bousserez, S. Boussetta, and A. Brown, 2018. Satellite and in situ observations for advancing global Earth surface modelling: A Review, Remote Sensing, 10(12): 2038. https://doi.org/10.7780/kjrs.2019.35.5.1.1   DOI
86 Petropoulos, G.P., G. Ireland, and B. Barrett, 2015. Surface soil moisture retrievals from remote sensing: Current status, products & future trends, Physics and Chemistry of the Earth, Parts A/B/C, 83: 36-56. https://doi.org/10.1016/j.pce.2015.02.009   DOI
87 Baik, J.J., S.K. Cho, S.C. Lee, and M.H. Choi, 2019. Analysis on Adequacy of the Satellite Soil Moisture Data (AMSR2, ASCAT, and ESACCI) in Korean Peninsula: With Classification of Freezing and Melting Periods, Korean Journal of Remote Sensing, 35(5-1): 625-636 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2019.35.5.1.1   DOI
88 Bhattarai, N., S.B. Shaw, L.J. Quackenbush, J. Im, and R. Niraula, 2016. Evaluating five remote sensing based single-source surface energy balance models for estimating daily evapotranspiration in a humid subtropical climate, International Journal of Applied Earth Observation and Geoinformation, 49: 75-86. https://doi.org/10.1016/j.jag.2016.01.010   DOI
89 Cai, X., Z.L. Yang, C.H. David, G.Y. Niu, and M. Rodell, 2014. Hydrological evaluation of the Noah-MP land surface model for the Mississippi River Basin, Journal of Geophysical Research: Atmospheres, 119(1): 23-38. https://doi.org/10.1029/WR015i002p00443   DOI
90 Senay, G.B., M.E. Budde, and J.P. Verdin, 2011. Enhancing the Simplified Surface Energy Balance (SSEB) approach for estimating landscape ET: Validation with the METRIC model, Agricultural Water Management, 98(4): 606-618. https://doi.org/10.1016/j.agwat.2010.10.014   DOI
91 Chae, H.S., Y.S. Song, and J.Y. Park, 2000. An Assessment of Areal Evaportranspiration Using Landsat TM Data, Journal of Korea Water Resources Association, 33(4): 471-482.
92 Chan, S.K., R. Bindlish, P.E. O'Neill, E. Njoku, T. Jackson, A. Colliander, F. Chen, M. Burgin, S. Dunbar, and J. Piepmeier, 2016. Assessment of the SMAP passive soil moisture product, IEEE Transactions on Geoscience and Remote Sensing, 54(8): 4994-5007. https://doi.org/10.1109/TGRS.2016.2561938   DOI
93 Cho, E., S.-U. Song, and C. Yoo, 2017. Analysis and Validation of Soil Moisture Data over the Korean Peninsula Simulated by the VIC Model, Journal of Wetlands Research, 19(1): 52-62. https://doi.org/10.1016/j.rse.2015.01.013   DOI
94 Lee, M.J., K.S. Han, and I.H. Kim, 2011. Estimation of Actual Evapotranspiration using Multi-Satellite Data over Korea Peninsula, The Journal of Korean Society for Geospatial Information Science, 19(4): 145-151.
95 Lee, Y.G., B.S. Im, K.Y. Kim, and K.H. Rhee, 2020. Adequacy evaluation of the GLDAS and GLEAM evapotranspiration by eddy covariance method, Journal of Korea Water Resources Association, 53(10): 889-902. https://doi.org/10.3741/JKWRA.2020.53.10.889   DOI
96 Kustas, W.P. and J.M. Norman, 2000. A two-source energy balance approach using directional radiometric temperature observations for sparse canopy covered surfaces, Agronomy Journal, 92(5): 847-854. https://doi.org/10.2134/agronj2000.925847x   DOI
97 Kwon, H.J., S.C. Shin, and S.J. Kim, 2005. Climatic water balance analysis using NOAA/AVHRR satellite images, Journal of The Korean Society of Agricultural Engineers, 47(1): 3-9. https://doi.org/10.5389/KSAE.2005.47.1.003   DOI
98 Lee, Y.G., J.H. Lee, M.H. Choi, and S.W. Jung, 2015b. Evaluation of MODIS-derived Evapotranspiration According to the Water Budget Analysis, Journal of the Korean Water Resources Association, 48(10): 831-843. https://doi.org/10.3741/JKWRA.2015.48.10.831   DOI
99 Lee, J., M. Choi, and D. Kim, 2016a. Spatial merging of satellite based soil moisture and in-situ soil moisture using conditional merging technique, Journal of Korea Water Resources Association, 49(3): 263-273. https://doi.org/10.3741/JKWRA.2016.49.3.263   DOI
100 Lee, J.H., 2017. Assimilation of satellite based soil moisture data into a land surface model, Hongik University, Seoul, Korea, pp. 1-47.
101 Lee, Y.G., C.G. Jung, Y.H. Cho, and S.J. Kim, 2017a Estimation of soil moisture using multiple linear regression model and COMS land surface temperature data, Journal of The Korean Society of Agricultural Engineers, 59(1): 11-20. https://doi.org/10.5389/KSAE.2017.59.1.011   DOI
102 Ulaby, F.T., R.K. Moore, and A.K. Fung, 1986. Microwave remote sensing: Active and passive, Artech House, Norwood, MA, USA.
103 Sur, C.Y. and M.H. Choi, 2011. An intercomparison of two satellite data-based evapotranspiration approaches, Korean Wetlands Society, 13(3): 471-479. https://doi.org/10.17663/JWR.2011.13.3.471   DOI
104 Sur, C.Y., J.J. Lee, J.Y. Park, and M.H. Choi, 2012b. Spatial Estimation of Priestley-Taylor Based Potential Evapotranspiration Using MODIS Imageries: the Nak-dong river basin, Korean Journal of Remote Sensing, 28(5): 521-529 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2012.28.5.5   DOI
105 Tsang, L., J.A. Kong, and R.T. Shin, 1985. Theory of microwave remote sensing, John Wiley, Hoboken, NJ, USA.
106 Verstraeten, W.W., F. Veroustraete, and J. Feyen, 2008. Assessment of evapotranspiration and soil moisture content across different scales of observation, Sensors, 8(1): 70-117. https://doi.org/10.3390/s8010070   DOI
107 Rodell, M., P. Houser, A. Berg, and J. Famiglietti, 2005. Evaluation of 10 methods for initializing a land surface model, Journal of Hydrometeorology, 6(2): 146-155. https://doi.org/10.1175/JHM414.1   DOI
108 Rodell, M., P. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, K. Arsenault, B. Cosgrove, J. Radakovich, and M. Bosilovich, 2004. The global land data assimilation system, Bulletin of the American Meteorological Society, 85(3): 381-394. https://doi.org/10.1175/BAMS-85-3-381   DOI
109 Yao, Y., S. Liang, J. Cheng, S. Liu, J.B. Fisher, X. Zhang, K. Jia, X. Zhao, Q. Qin, B. Han, S. Han, G. Zhou, G. Zhou, Y. Li, and S. Zhao, 2013. MODIS-driven estimation of terrestrial latent heat flux in China based on a modified Priestley-Taylor algorithm, Agricultural and Forest Meteorology, 171: 187-202. https://doi.org/10.1016/j.agrformet.2012.11.016   DOI
110 Kwon, H.J., S.C. Shin, and S.J. Kim, 2004. Meteorological Water Balance Analysis using NOAA/AVHRR Satellite Images, Proc. of the Korea Water Resources Association Conference, Incheon, Korea, May 14-15, pp. 262-266.
111 Yoo, J.W., 2003. The Estimation of Evapotranspiration with SEBAL Model in the Geumgang Upper Basin, Korea, Seoul National University, Seoul, Republic of Korea.
112 Yoon, D.H., W.H. Nam, H.J. Lee, E.M. Hong, and T.G. Kim, 2020. Drought Hazard Assessment using MODIS-based Evaporative Stress Index (ESI) and ROC Analysis, Journal of The Korean Society of Agricultural Engineers, 62(3): 51-61. https://doi.org/10.5389/KSAE.2020.62.3.051   DOI
113 Monteith, J.L., 1965a. Evaporation and environment, Symposia of the Society for Experimental Biology, 19: 205-234.
114 Cui, Y., X. Chen, J. Gao, B. Yan, G. Tang, and Y. Hong, 2018. Global water cycle and remote sensing big data: overview, challenge, and opportunities, Big Earth Data, 2(3): 282-297. https://doi.org/10.1080/20964471.2018.1548052   DOI
115 Shin, S.-C., M.-H. Hwang, I.-H. Ko, and S.-J. Lee, 2006a. Suggestion of simple method to estimate evapotranspiration using vegetation and temperature information, Journal of Korea Water Resources Association, 39(4): 363-372. https://doi.org/10.3741/JKWRA.2006.39.4.363   DOI
116 Shin, S.C. and T.Y. An, 2007. Development of estimating method for areal evapotranspiration using satellite data, Journal of the Korean Association of Geographic Information Studies, 10(2): 71-81.
117 Shin, H.J., R. Ha, M.J. Park, and S.J. Kim, 2010. Estimation of spatial evapotranspiration using the relationship between MODIS NDVI and morton ET-For Chungjudam watershed, Journal of The Korean Society of Agricultural Engineers, 52(1): 19-24. https://doi.org/10.5389/KSAE.2010.52.1.019   DOI
118 Shin, Y.C., K.S. Choi, Y.H. Jung, J.E. Yang, and K.J. Lim, 2016b. Soil Moisture Estimation and Drought Assessment at the Spatio-Temporal Scales using Remotely Sensed Data: (II) Drought, Journal of Korean Society on Water Environment, 32(1): 70-79. https://doi.org/10.15681/KSWE.2016.32.1.60   DOI
119 Yoon, D.H., W.H. Nam, H.J. Lee, E.M. Hong, T.G. Kim, D.E. Kim, A.K. Shin, and M.D. Svoboda, 2018. Application of evaporative stress index (ESI) for satellite-based agricultural drought monitoring in South Korea, Journal of the Korean Society of Agricultural Engineers, 60(6): 121-131. https://doi.org/10.5389/KSAE.2018.60.6.121   DOI
120 Mo, T., B.J. Choudhury, T.J. Schmugge, J.R. Wang, and T.J. Jackson, 1982. A model for microwave emission from vegetation-covered fields, Journal of Geophysical Research, 87(C13): 11229-11237. https://doi.org/10.1029/JC087iC13p11229   DOI
121 Hur, Y.M. and M.H. Choi, 2011. Advanced Microwave Scanning Radiometer E Soil Moisture Evaluation for Haenam Flux Monitoring Network Site, Korean Journal of Remote Sensing, 27(2): 131-140 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2011.27.2.131   DOI
122 Anderson, M., J. Norman, G. Diak, W. Kustas, and J. Mecikalski, 1997. A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing, Remote Sensing of Environment, 60(2): 195-216. https://doi.org/10.1016/S0034-4257(96)00215-5   DOI
123 Brutsaert, W. and H. Stricker, 1979. An advection-aridity approach to estimate actual regional evapotranspiration, Water Resources Research, 15(2): 443-450.   DOI
124 Choi, M.H., G.T. Hwang, and T.W. Kim, 2011. Characteristics of Greenup and Senescence for Evapotranspiration in Gyeongan Watershed Using Landsat Imagery, KSCE Journal of Civil Engineering, 31(1B): 29. https://doi.org/10.12652/Ksce.2011.31.1B.029   DOI
125 Shin, Y., T. Lee, S. Kim, H.-W. Lee, K.-S. Choi, J. Kim, and G. Lee, 2017. Development of Agricultural Drought Assessment Approach Using SMAP Soil Moisture Footprints, Journal of The Korean Society of Agricultural Engineers, 59(1): 57-70. https://doi.org/10.5389/KSAE.2017.59.1.057   DOI
126 Shuttleworth, W.J. and J. Wallace, 1985. Evaporation from sparse crops-an energy combination theory, Quarterly Journal of the Royal Meteorological Society, 111(469): 839-855. https://doi.org/10.1002/qj.49711146910   DOI
127 Cho, S.K., J.H. Jeong, S.C. Lee, and M.H. Choi, 2021. Estimation of soil moisture based on Sentinel-1 SAR data: Assessment of soil moisture estimation in different vegetation condition, Journal of Korea Water Resources Association, 54(2): 81-91. https://doi.org/10.3741/JKWRA.2021.54.2.81   DOI
128 Sur, C.Y., S.J. Han, J.H. Lee, and M.H. Choi, 2012a. Estimation of Satellite-based Spatial Evapotranspiration and Validation of Fluxtower Measurements by Eddy Covariance Method, Korean Journal of Remote Sensing, 28(4): 435-448 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2012.28.4.7   DOI
129 Dubois, P.C., J. Van Zyl, and T. Engman, 1995. Measuring soil moisture with imaging radars, IEEE Transactions on Geoscience and Remote Sensing, 33(4): 915-926. https://doi.org/10.1109/36.406677   DOI
130 Draper, C.S., J.P. Walker, P.J. Steinle, R.A. De Jeu, and T.R. Holmes, 2009. An evaluation of AMSR-E derived soil moisture over Australia, Remote Sensing of Environment, 113 (4): 703-710. https://doi.org/10.1016/j.rse.2008.11.011   DOI
131 Fung, A.K., Z. Li, and K.-S. Chen, 1992. Backscattering from a randomly rough dielectric surface, IEEE Transactions on Geoscience and Remote Sensing, 30(2): 356-369. https://doi.org/10.1109/36.134085   DOI
132 Agam, N., W.P. Kustas, M.C. Anderson, J.M. Norman, P.D. Colaizzi, T.A. Howell, J.H. Prueger, T.P. Meyers, and T.B. Wilson, 2010. Application of the Priestley-Taylor approach in a two-source surface energy balance model, Journal of Hydrometeorology, 11(1): 185-198. https://doi.org/10.1175/2009JHM1124.1   DOI
133 Engman, E.T., 1990. Progress in microwave remote sensing of soil moisture, Canadian Journal of Remote Sensing, 16(3): 6-14. https://doi.org/10.1080/07038992.1990.11487620   DOI
134 Lee, Y.G., C.G. Jung, S.R. Ahn, and S.J. Kim, 2016c. Estimation of spatial evapotranspiration using Terra MODIS satellite image and SEBAL model in mixed forest and rice paddy area, Journal of Korea Water Resources Association, 49(3): 227-239. https://doi.org/10.11108/kagis.2015.18.1.090   DOI
135 Goodfellow, I., Y. Bengio, and A. Courville, 2016. Deep learning, MIT press, Cambridge, MA, USA.
136 Engman, E.T. and N. Chauhan, 1995. Status of microwave soil moisture measurements with remote sensing, Remote Sensing of Environment, 51(1): 189-198. https://doi.org/10.1016/0034-4257(94)00074-W   DOI
137 Friedl, M.A., 2002. Forward and inverse modeling of land surface energy balance using surface temperature measurements, Remote Sensing of Environment, 79(2-3): 344-354. https://doi.org/10.1016/j.rse.2007.06.025   DOI
138 Fujii, H., T. Koike, and K. Imaoka, 2009. Improvement of the AMSR-E algorithm for soil moisture estimation by introducing a fractional vegetation coverage dataset derived from MODIS data, Journal of the Remote Sensing Society of Japan, 29(1): 282-292. https://doi.org/10.11440/rssj.29.282   DOI
139 Monteith, J.L., 1981. Evaporation and surface temperature, Quarterly Journal of the Royal Meteorological Society, 107(451): 1-27. https://doi.org/10.1002/qj.49710745102   DOI
140 Lee, T.H., S.W. Kim, and Y.C. Shin, 2018c. Development of Landsat-based Downscaling Algorithm for SMAP Soil Moisture Footprints, Journal of the Korean Society of Agricultural Engineers, 60(4): 49-54. https://doi.org/10.5389/KSAE.2018.60.4.049   DOI
141 Park, I., K.S. Kim, B.C. Han, Y.J. Choung, B.Y. Gu, J.T. Han, and J. Kim, 2021c. A Study for Monitoring Soil Liquefaction Occurred by Earthquakes Using Soil Moisture Indices Derived from the Multi-temporal Landsat Satellite Imagery Acquired in Pohang, South Korea, Journal of the Korean Association of Geographic Information Studies, 24(1): 126-137. https://doi.org/10.11108/kagis.2021.24.1.126   DOI
142 Barrett, B., E. Dwyer, and P. Whelan. 2009. Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques, Remote Sensing, 1(3): 210-242. https://doi.org/10.3390/rs1030210   DOI
143 Norman, J.M., M.C. Anderson, W.P. Kustas, A.N. French, J. Mecikalski, R. Torn, G.R. Diak, T.J. Schmugge, and B.C.W. Tanner, 2003. Remote sensing of surface energy fluxes at 10-1 m pixel resolutions, Water Resources Research, 39(8): 1-18. https://doi.org/10.1029/2002WR001775   DOI
144 Jeon, H.H., S.K. Cho, I.M. Chung, and M.H. Choi, 2021a. Evaluation of satellite-based evapotranspiration and soil moisture data applicability in Jeju Island, Journal of Korea Water Resources Association, 54(10): 835-848. https://doi.org/10.3741/JKWRA.2021.54.10.835   DOI
145 Jeong, S.T., K.C. Jang, S.K. Kang, J. Kim, H. Kondo, M. Gamo, J. Asanuma, N. Saigusa, S. Wang, and S. Han, 2009. Evaluation of MODIS-derived Evapotranspiration at the Flux Tower Sites in East Asia, Korean Journal of Agricultural and Forest Meteorology, 11(4): 174-184. https://doi.org/10.5532/KJAFM.2009.11.4.174   DOI
146 Cho, E.S., M.H. Choi, and W. Wagner, 2015. An assessment of remotely sensed surface and root zone soil moisture through active and passive sensors in northeast Asia, Remote Sensing of Environment, 160: 166-179. https://doi.org/10.1016/j.rse.2015.01.013   DOI
147 Ahmad, M.D., T. Biggs, H. Turral, and C. A. Scott, 2006. Application of SEBAL approach and MODIS time-series to map vegetation water use patterns in the data scarce Krishna river basin of India, Water Science and Technology, 53(10): 83-90. https://doi.org/10.2166/wst.2006.301   DOI
148 Allen, R.G., L.S. Pereira, D. Raes, and M. Smith, 1998. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56, Food and Agriculture Organization of the United Nations, Rome, Italy.
149 Allen, R.G., M. Tasumi, and A. Morse, 2005. Satellite-based evapotranspiration by METRIC and Landsat for western states water management, Proc. of US Bureau of Reclamation Evapotranspiration Workshop, Fort Collins, CO, USA, Feb. 8-10, pp. 8-10.
150 Njoku, E.G. and S.K. Chan, 2006. Vegetation and surface roughness effects on AMSR-E land observations, Remote Sensing of Environment, 100(2): 190-199. https://doi.org/10.1016/j.rse.2005.10.017   DOI
151 Otkin, J.A., M.C. Anderson, C. Hain, I. E. Mladenova, J.B. Basara, and M. Svoboda, 2013. Examining rapid onset drought development using the thermal infrared-based evaporative stress index, Journal of Hydrometeorology, 14(4): 1057-1074. https://doi.org/10.1175/JHM-D-12-0144.1   DOI
152 Kim, G.S. and H.G. Park, 2010. Soil moisture estimation using CART algorithm and ancillary Data, Journal of Korea Water Resources Association, 43(7): 597-608. https://doi.org/10.3741/JKWRA.2010.43.7.597   DOI
153 Kim, G.S. and J.A. Park, 2011. Development of a Soil Moisture Estimation Model Using Artificial Neural Networks and Classification and Regression Tree (CART), KSCE Journal of Civil Engineering, 31(2B): 155-163. https://doi.org/10.12652/Ksce.2011.31.2B.155   DOI
154 Kim, M.J., G.S. Kim, and J.E. Yi, 2015. Bias Correction of AMSR2 Soil Moisture Data Using Ground Observations, Journal of The Korean Society of Agricultural Engineers, 57(4): 61-71. https://doi.org/10.5389/KSAE.2015.57.4.061   DOI
155 Kim, D.S. and K.J. Kim, 2017. Downscaling Advanced Microwave Scanning Radiometer 2 (AMSR2) soil moisture data using regression-kriging, Journal of the Korean Cartographic Association, 17(2): 99-110. https://doi.org/10.4172/2169-0049.1000   DOI
156 Kim, S.K., H.L. Kim, and M.H. Choi, 2016c. Evaluation of satellite-based soil moisture retrieval over the Korean peninsula: using AMSR2 LPRM algorithm and ground measurement data, Journal of Korea Water Resources Association, 49(5): 423-429. https://doi.org/10.3741/JKWRA.2016.49.5.423   DOI
157 Kim, S.W., Y.C. Shin, T.H. Lee, S.H. Lee, K.S. Choi, Y.S. Park, K.J. Lim, and J.J. Kim, 2017a. Characteristics of Soil Moisture Distributions at the Spatio-Temporal Scales Based on the Land Surface Features Using MODIS Images, Journal of The Korean Society of Agricultural Engineers, 59(6): 29-37. https://doi.org/10.5389/KSAE.2017.59.6.029   DOI
158 Kite, G., M. Danard, and B. Li, 1998. Simulating long series of streamflow using data from an atmospheric model, Hydrological Sciences Journal, 43(3): 391-407. https://doi.org/10.1080/02626669809492134   DOI
159 Sugita, M. and W. Brutsaert, 1991. Daily evaporation over a region from lower boundary layer profiles measured with radiosondes, Water Resources Research, 27(5): 747-752. https://doi.org/10.1029/90WR02706   DOI
160 Su, Z., 2002. The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes, Hydrology and Earth System Sciences, 6(1): 85-100. https://doi.org/10.5194/hess-6-85-2002   DOI
161 Sun, D. and M. Kafatos, 2007. Note on the NDVI-LST relationship and the use of temperature-related drought indices over North America, Geophysical Research Letters, 34(24): 1-4. https://doi.org/10.1029/2007GL031485   DOI
162 Sunwoo, W.Y., D.E. Kim, S.H. Hwang, and M.H. Choi, 2014. Analysis of Regional Antecedent Wetness Conditions Using Remotely Sensed Soil Moisture and Point Scale Rainfall Data, Korean Journal of Remote Sensing, 30(5): 587-596 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2014.30.5.4   DOI
163 Lee, T.H., S.W. Kim, Y.H. Jung, and Y.C. Shin, 2018b. Assessment of Agricultural Drought Using Satellite-based TRMM/GPM Precipitation Images: At the Province of Chungcheongbuk-do, Journal of the Korean Society of Agricultural Engineers, 60(4): 73-82. https://doi.org/10.5389/KSAE.2018.60.4.073   DOI
164 Kustas, W.P. and J.M. Norman, 1999a. Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperatures for partial canopy cover, Agricultural and Forest Meteorology, 94(1): 13-29. https://doi.org/10.1016/S0168-1923(99)00005-2   DOI
165 Mu, Q., M. Zhao, and S.W. Running, 2011. Improvements to a MODIS global terrestrial evapotranspiration algorithm, Remote Sensing of Environment, 115(8): 1781-1800. https://doi.org/10.1016/j.rse.2011.02.019   DOI
166 Shuttleworth, W.J. and R.J. Gurney, 1990. The theoretical relationship between foliage temperature and canopy resistance in sparse crops, Quarterly Journal of the Royal Meteorological Society, 116(492): 497-519. https://doi.org/10.1002/qj.49711649213   DOI
167 Van Dam, J.C., J. Huygen, J. Wesseling, R. Feddes, P. Kabat, P. Van Walsum, P. Groenendijk, and C. Van Diepen, 1997. Theory of SWAP version 2.0; Simulation of water flow, solute transport and plant growth in the Soil-Water-Atmosphere-Plant environment, DLO Winand Staring Centre, Wageningen, Netherlands.
168 Lee, S.J., K.J. Kim, Y.H. Kim, J.W. Kim, S.W. Park, Y.S. Yun, N.R. Kim, and Y.W. Lee, 2018a. Deep Learning-based Estimation and Mapping of Evapotranspiration in Cropland using Local Weather Prediction Model and Satellite Data, Journal of the Korean Cartographic Association, 18(3): 105-116. https://doi.org/10.16879/jkca.2018.18.3.105   DOI
169 De Jeu, R.D. and M. Owe, 2003. Further validation of a new methodology for surface moisture and vegetation optical depth retrieval, International Journal of Remote Sensing, 24(22): 4559-4578. https://doi.org/10.1080/0143116031000095934   DOI
170 Eagleman, J.R. and W.C. Lin, 1976. Remote sensing of soil moisture by a 21-cm passive radiometer, Journal of Geophysical Research, 81(21): 3660-3666. https://doi.org/10.1029/JC081i021p03660   DOI
171 Lee, T.H., S.W. Kim, and Y.C. Shin, 2018d. Development of Landsat-based Downscaling Algorithm for SMAP soil moisture footprints, Journal of the Korean Society of Agricultural Engineers, 60(4): 49-54. https://doi.org/10.5389/KSAE.2018.60.4.049   DOI
172 Shin, Y.C., K.S. Choi, Y.H. Jung, J.E. Yang, and K.J. Lim, 2016a. Soil Moisture Estimation and Drought Assessment at the Spatio-Temporal Scales using Remotely Sensed Data: (I) Soil Moisture, Journal of Korean Society on Water Environment, 32(1): 60-69. https://doi.org/10.15681/KSWE.2016.32.1.60   DOI
173 Roerink, G., Z. Su, and M. Menenti, 2000. S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance, Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 25(2): 147-157. https://doi.org/10.1016/S1464-1909(99)00128-8   DOI
174 Running, S.W. and R.R. Nemani, 1988. Relating seasonal patterns of the AVHRR vegetation index to simulated photosynthesis and transpiration of forests in different climates, Remote Sensing of Environment, 24(2): 347-367. https://doi.org/10.1016/0034-4257(88)90034-X   DOI
175 Running, S.W., Q. Mu, M. Zhao, and A. Moreno, 2017. MODIS global terrestrial evapotranspiration (ET) product (NASA MOD16A2/A3) NASA earth observing system MODIS land algorithm, NASA, Washington, D.C., USA.
176 Allen, R.G., M. Tasumi, A. Morse, R. Trezza, J.L. Wright, W. Bastiaanssen, W. Kramber, I. Lorite, and C.W. Robison, 2007a. Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)-Applications, Journal of Irrigation and Drainage Engineering, 133(4): 395-406. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:4(395)   DOI
177 Njoku, E.G., T.J. Jackson, V. Lakshmi, T.K. Chan, and S.V. Nghiem, 2003. Soil moisture retrieval from AMSR-E, IEEE Transactions on Geoscience and Remote Sensing, 41(2): 215-229. https://doi.org/10.1109/TGRS.2002.808243   DOI
178 Oh, Y., K. Sarabandi, and F.T. Ulaby, 1992. An empirical model and an inversion technique for radar scattering from bare soil surfaces, IEEE Transactions on Geoscience and Remote Sensing, 30(2): 370-381. https://doi.org/10.1109/36.134086   DOI
179 Shin, S.C., M. Sawamoto, and C.H. Kim, 1995. Estimation of evapotranspiration using NOAA-AVHRR data, Water for Future, 28(1): 71-80.
180 Shin, S.C., S. Jeong, K.T. Kim, J.H. Kim, and J.S. Park, 2006b. Drought detection and estimation of water deficit using NDVI, Journal of the Korean Association of Geographic Information Studies, 9(2): 102-114.
181 Chae, S.H., S.H. Park, and M.J. Lee, 2017. A study on the observation of soil moisture conditions and its applied possibility in agriculture using land surface temperature and NDVI from landsat-8 OLI/TIRS satellite image, Korean Journal of Remote Sensing, 33(6-1): 931-946 (in Korean with English abstract). http://dx.doi.org/10.7780/kjrs.2017.33.6.1.3   DOI
182 Chen, K., S. Yen, and W. Huang, 1995. A simple model for retrieving bare soil moisture from radar-scattering coefficients, Remote Sensing of Environment, 54(2): 121-126. https://doi.org/10.1016/0034-4257(95)00129-O   DOI
183 Choudhury, B., T. Schmugge, and T. Mo, 1982. A parameterization of effective soil temperature for microwave emission, Journal of Geophysical Research: Oceans, 87(C2): 1301-1304. https://doi.org/10.1029/JC087iC02p01301   DOI
184 Cleugh, H.A., R. Leuning, Q. Mu, and S.W. Running, 2007. Regional evaporation estimates from flux tower and MODIS satellite data, Remote Sensing of Environment, 106(3): 285-304. https://doi.org/10.1016/j.rse.2006.07.007   DOI