[논문]위성기반 증발산량 및 토양수분량 산정 국내 연구동향

최가영; 조영현

doi:10.7780/kjrs.2022.38.6.1.14

[국내논문] 위성기반 증발산량 및 토양수분량 산정 국내 연구동향
Research Status of Satellite-based Evapotranspiration and Soil Moisture Estimations in South Korea 원문보기

대한원격탐사학회지 = Korean journal of remote sensing, v.38 no.6 pt.1, 2022년, pp.1141 - 1180

최가영 (K-water연구원 수자원환경연구소) , 조영현 (K-water연구원 수자원환경연구소)

초록
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최근 수문 및 수자원 분야에서 위성영상의 활용성이 높아짐에 따라 관련 전용 위성 개발연구와 연계하여 위성을 활용한 증발산량과 토양수분량 산정 연구의 필요성이 강조되고 있다. 본 연구에서는 이러한 위성을 기반으로 증발산량 및 토양수분량의 국내 연구현황과 그 산정 방법론을 조사하여 현재까지의 연구동향을 파악하고자 하였다. 국내 연구현황을 세부 방법론 별로 살펴본 결과 일반적으로 증발산량의 경우는 Surface Energy Balance Algorithm for Land (SEBAL), Mapping Evapotranspiration with Internalized Calibration (METRIC)과 같은 에너지수지 기반 모형과 Penman-Monteith (PM) 및 Priestley-Taylor (PT) 산출식을 기반으로 산정되었으며, 토양수분량의 경우 능동형(AMSR-E, AMSR2, MIRAS, SMAP) 및 수동형(ASCAT, SAR)와 같은 마이크로파 센서를 통한 산정이 주를 이루었다. 통계적 측면에서는 증발산량 및 토양수분량 공통적으로 회귀식 및 인공지능을 이용한 산출사례를 찾을 수 있었다. 또한 위성기반 자료들을 이용한 Evaporative Stress Index (ESI), Temperature-Vegetation Dryness Index (TVDI), Soil Moisture Deficit Index (SMDI) 등의 다양한 지표를 산정하여 가뭄 특성파악에 적용한 연구 사례도 다수 있었으며, 지표모형(Land Surface Model, LSM)을 기반으로 하여 위성 다중센서에서 얻을 수 있는 주요 자료들을 활용해 증발산량과 토양수분량의 수문순환인자를 산출하기도 하였다. 본 논문에서는 이렇게 기존 연구사례 조사 및 내용파악 과정을 통해 위성을 활용한 주요 세부 방법론을 비교·검토 제시함으로써 관련 연구분야 기준 참고자료로의 활용 및 향후 위성기반 관련 수문순환 자료 산출 고도화 연구의 초석을 다지고자 한다.

Abstract ▼ AI-Helper

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.

주제어

표/그림 (7)

그림 Fig. 1. Global evapotranspiration and soil moisture map: (a) Global annual MOD16 evapotranspiration over 2000-2006 (Mu et al., 2013), (b) Global soil moisture map from SMAP between May 4 and May 11, 2015 (Source: https://www.jpl.nasa.gov/images/pia19337-high-resolution-global-soil-moisture-map).
표 Table 1. Satellite-based evapotranspiration estimation research summary in South Korea
표 Table 1. Continued
표 Table 2. Satellite-based soil moisture estimation research summary in South Korea
표 Table 2. Continued
표 Table 2. Continued
표 Table 3. Microwave sensors for satellite-based soil moisture estimation

AI 본문요약
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문제 정의

특히, 물리적 방법으로 산정된 증발산량과 토양수분량은 가뭄 해석에도 유용한 수문인자이다. 국내에서도 최근 가뭄 분석 연구들이 늘어남에 따라 다양한 관련 지표가 활용되어왔는데, 본 절에서는 증발산량 및 토양수분량 산정과 관련이 있는 대표적인 지수에 대해 기술하고자 한다.
한편, 공통적인 주제로는 가뭄 모니터링 등에 활용을 위한 가뭄 및 지표 수분량에 대한 지수를 산출하는 내용이 있었으며, 지표모형(LSM)을 사용하여 증발산량과 토양수분량을 산정하기도 하였다. 따라서, 본 절에서는 각각의 세부 방법론에 대한 설명을 통해 위성영상 자료를 활용한 증발산량 및 토양수분량 산정의 체계적 분류 및 관련 이론적 근거를 제시하고자 한다.
본 연구에서는 위성을 기반으로 한 증발산량 및 토양 수분량의 국내 연구현황과 그 산정 방법론을 조사하여 현재까지의 연구동향을 파악하고자 하였다. 1995년이후 2021년까지 국내학술지에 등재된 문헌을 기준으로 각각의 자료 산출과정을 방법별로 정리하였으며, 국내의 현황뿐만 아니라 연관된 국외사례도 추가 기술하여 위성을 활용한 관련 연구의 포괄적인 검토가 되도록 하였다.
이러한 상기 과정을 통해 위성을 활용한 증발산량 및 토양수분량 자료 산정의 주요 세부 방법론을 비교·검토 및 제시하여 관련 연구분야 기준 참고자료로의 활용 및 향후 위성영상 정보를 바탕으로 한 관련 수문 순환 자료 산출 고도화 연구의 초석을 다지고자 한다.

제안 방법

본 연구에서는 위성을 기반으로 한 증발산량 및 토양 수분량의 국내 연구현황과 그 산정 방법론을 조사하여 현재까지의 연구동향을 파악하고자 하였다. 1995년이후 2021년까지 국내학술지에 등재된 문헌을 기준으로 각각의 자료 산출과정을 방법별로 정리하였으며, 국내의 현황뿐만 아니라 연관된 국외사례도 추가 기술하여 위성을 활용한 관련 연구의 포괄적인 검토가 되도록 하였다. 이러한 상기 과정을 통해 위성을 활용한 증발산량 및 토양수분량 자료 산정의 주요 세부 방법론을 비교·검토 및 제시하여 관련 연구분야 기준 참고자료로의 활용 및 향후 위성영상 정보를 바탕으로 한 관련 수문 순환 자료 산출 고도화 연구의 초석을 다지고자 한다.
최근 수문 및 수자원분야에서 위성영상의 활용성이 높아짐에 따라 관련 전용 위성의 개발연구와 연계하여 위성을 활용한 증발산량과 토양수분량 산정 연구의 필요성이 강조되고 있다. 본 연구에서는 그간의 관련 국내 연구현황을 여러 문헌조사와 국외 이론 등의 검토를 통해 세부 방법론 별로 살펴보았다.
이렇게 본 연구에서는 위성영상을 활용하여 증발산량과 토양수분량을 산출한 연구문헌의 수집 및 검토를 통하여 국내 관련 연구현황을 분석, 정리하였다. 이를 바탕으로 세부 산정방법별 분류가 가능했으며, 향후에는 본 연구에서 검토한 국내 연구뿐만 아니라 국외에서 진행된 더 많은 다양한 연구들을 추가하여 보다 일반적인 위성기반 증발산량 및 토양수분량 산정 연구의 방법론 검토와 관련 자료 산정의 고도화 관련 연구를 진행할 예정이다.

대상 데이터

본 연구에서 수집된 위성기반 증발산량 및 토양수분량 산정 관련 국내문헌은 총 69건으로 증발산량 31건, 토양수분량 35건, 그리고 공통 3건이다. 그 내용은 아래의 절에 주요 방법별로 기술하였으며, Table 1과 2에도 각각의 문헌 별 주요내용을 간략하게 정리하였다.

성능/효과

앞 절에서 위성기반 증발산량 및 토양수분량 산정의 국내 연구현황을 살펴본 결과, 증발산량은 대부분 에너지수지식 및 증발산 산출식을 사용하거나 NDVI와 증발산량과의 상관관계를 고려하여 여러 위성영상 보조 자료와 함께 회귀분석을 이용하는 방법이 많았고, 토양 수분량의 경우는 각 위성에 탑재된 마이크로파 센서의 알고리즘을 이용한 산출과 각종 위성자료와의 통계적 방법으로 산정하는 방식이 주를 이루었다. 한편, 공통적인 주제로는 가뭄 모니터링 등에 활용을 위한 가뭄 및 지표 수분량에 대한 지수를 산출하는 내용이 있었으며, 지표모형(LSM)을 사용하여 증발산량과 토양수분량을 산정하기도 하였다.

후속연구

이를 바탕으로 세부 산정방법별 분류가 가능했으며, 향후에는 본 연구에서 검토한 국내 연구뿐만 아니라 국외에서 진행된 더 많은 다양한 연구들을 추가하여 보다 일반적인 위성기반 증발산량 및 토양수분량 산정 연구의 방법론 검토와 관련 자료 산정의 고도화 관련 연구를 진행할 예정이다. 본 연구의 내용이 관련 위성기반 증발산 및 토양수분 분야 연구자들에게 기준 참고자료로서 유용하게 사용되기를 기대한다.
이렇게 본 연구에서는 위성영상을 활용하여 증발산량과 토양수분량을 산출한 연구문헌의 수집 및 검토를 통하여 국내 관련 연구현황을 분석, 정리하였다. 이를 바탕으로 세부 산정방법별 분류가 가능했으며, 향후에는 본 연구에서 검토한 국내 연구뿐만 아니라 국외에서 진행된 더 많은 다양한 연구들을 추가하여 보다 일반적인 위성기반 증발산량 및 토양수분량 산정 연구의 방법론 검토와 관련 자료 산정의 고도화 관련 연구를 진행할 예정이다. 본 연구의 내용이 관련 위성기반 증발산 및 토양수분 분야 연구자들에게 기준 참고자료로서 유용하게 사용되기를 기대한다.

참고문헌 (184)

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

상세보기
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

상세보기
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.
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.
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)

상세보기
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)

상세보기
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

상세보기
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

상세보기
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

상세보기
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.
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

상세보기
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

상세보기
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.
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

원문보기 상세보기
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

원문보기 상세보기
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

원문보기 상세보기
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

원문보기 상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
Brutsaert, W. and H. Stricker, 1979. An advection-aridity approach to estimate actual regional evapotranspiration, Water Resources Research, 15(2): 443-450.

상세보기
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

상세보기
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.
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

원문보기 상세보기
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.

상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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

원문보기 상세보기
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

원문보기 상세보기
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

원문보기 상세보기
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

상세보기
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

원문보기 상세보기
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

원문보기 상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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
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

상세보기
Goodfellow, I., Y. Bengio, and A. Courville, 2016. Deep learning, MIT press, Cambridge, MA, USA.
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

상세보기
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.
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

상세보기
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

원문보기 상세보기
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

상세보기
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.

상세보기
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

원문보기 상세보기
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

원문보기 상세보기
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

상세보기
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.
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

원문보기 상세보기
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

원문보기 상세보기
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

원문보기 상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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.
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

원문보기 상세보기
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

원문보기 상세보기
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

원문보기 상세보기
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

원문보기 상세보기
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

원문보기 상세보기
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
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

원문보기 상세보기
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

원문보기 상세보기
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

원문보기 상세보기
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

원문보기 상세보기
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
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

원문보기 상세보기
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

상세보기
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

상세보기
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

상세보기
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.
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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.
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

원문보기 상세보기
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.
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

원문보기 상세보기
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

원문보기 상세보기
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

원문보기 상세보기
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

원문보기 상세보기
Lee, J.H., 2017. Assimilation of satellite based soil moisture data into a land surface model, Hongik University, Seoul, Korea, pp. 1-47.
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

원문보기 상세보기
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

원문보기 상세보기
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

상세보기
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

원문보기 상세보기
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

원문보기 상세보기
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

원문보기 상세보기
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

원문보기 상세보기
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

원문보기 상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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 2.0.CO;2

상세보기
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

상세보기
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

상세보기
Monteith, J.L., 1965a. Evaporation and environment, Symposia of the Society for Experimental Biology, 19: 205-234.

상세보기
Monteith, J.L., 1965b. The state and movement of water in living organisms, Cambridge University Press, Cambridge, UK, pp. 205-234.
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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.
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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.
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

원문보기 상세보기
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

원문보기 상세보기
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

원문보기 상세보기
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

원문보기 상세보기
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

상세보기
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

상세보기
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

상세보기
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 2.3.CO;2

상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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.
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

상세보기
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)

상세보기
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

상세보기
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

상세보기
Shin, S.C., M. Sawamoto, and C.H. Kim, 1995. Estimation of evapotranspiration using NOAA-AVHRR data, Water for Future, 28(1): 71-80.
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.
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

원문보기 상세보기
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.
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.
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

원문보기 상세보기
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

원문보기 상세보기
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

원문보기 상세보기
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

원문보기 상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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

원문보기 상세보기
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

원문보기 상세보기
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

원문보기 상세보기
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

원문보기 상세보기
Tsang, L., J.A. Kong, and R.T. Shin, 1985. Theory of microwave remote sensing, John Wiley, Hoboken, NJ, USA.
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.
Ulaby, F.T., R.K. Moore, and A.K. Fung, 1986. Microwave remote sensing: Active and passive, Artech House, Norwood, MA, USA.
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

상세보기
Wagner, W., 1998. Soil moisture retrieval from ERS scatterometer data, Vienna University of Technology, Vienna, Austria.
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

상세보기
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
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

상세보기
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.
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

상세보기
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

상세보기
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

상세보기
Yoo, J.W., 2003. The Estimation of Evapotranspiration with SEBAL Model in the Geumgang Upper Basin, Korea, Seoul National University, Seoul, Republic of Korea.
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

원문보기 상세보기
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

원문보기 상세보기
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.
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

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표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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