최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기대한원격탐사학회지 = Korean journal of remote sensing, v.37 no.2, 2021년, pp.211 - 220
Land Surface Temperature (LST) is one of the useful parameters to diagnose the growth and development of crop and to detect crop stress. Unmanned Aerial Vehicle (UAV)-based LST (LSTUAV) can be estimated in the regional spatial scale due to miniaturization of thermal infrared camera and development o...
An, J.H., S.H. Jeon, E.Y. Choi, H.M. Kang, J.K. Na, and K.Y. Choi, 2021. Effect of Irrigation Starting Point of Soil on Chlorophyll Fluorescence, Stem Sap Flux Relative Rate and Leaf Temperature of Cucumber in Greenhouse, Journal of Bio-Environment Control, 30(1): 46-55 (in Korean with English abstract).
Bates, J.S., C. Montzka, M. Schmidt, and F. Jonard, 2021. Estimating Canopy Density Parameters Time-Series for Winter Wheat Using UAS Mounted LiDAR, Remote Sensing, 13(4): 710.
Bian, J., Z. Zhang, J. Chen, H. Chen, C. Cui, X. Li, S. Chen, and Q. Fu, 2019. Simplified evaluation of cotton water stress using high resolution unmanned aerial vehicle thermal imagery, Remote Sensing, 11(3): 267.
Camino, C., P. Zarco-Tejada, and V. Gonzalez-Dugo, 2018. Effects of Heterogeneity within Tree Crowns on Airborne-Quantified SIF and the CWSI as Indicators of Water Stress in the Context of Precision Agriculture, Remote Sensing, 10(4): 604.
Cho, A.-R. and M.-S. Suh, 2013. Evaluation of Land Surface Temperature Operationally Retrieved from Korean Geostationary Satellite (COMS) Data, Remote Sensing, 5(8): 3951-3970.
DeJonge, K.C., S. Taghvaeian, T.J. Trout, and L.H. Comas, 2015. Comparison of canopy temperaturebased water stress indices for maize, Agricultural Water Management, 156: 51-62.
Duan, S.-B., Z.-L. Li, H. Li, F.-M. Gottsche, H. Wu, W. Zhao, P. Leng, X. Zahng, and C. Coll, 2019. Validation of Collection 6 MODIS land surface temperature product using in situ measurements, Remote Sensing of Environment, 225: 16-29.
Guillevic, P.C., J.C. Biard, G.C. Hulley, J.L. Privette, S.J. Hook, A. Olioso, F.M. Gottsche, R. Radocinski, M.O. Roman, Y. Yu, and I. Csiszar, 2014. Validation of Land Surface Temperature products derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) using ground-based and heritage satellite measurements, Remote Sensing of Environment, 154: 19-37.
Idso, S.B., R.D. Jackson, P.J.J. Pinter, R.J. Reginato, and J.L. Hatfield, 1981. Normalizing the stress degree-day parameter for environmental variability, Agricultural Meteorology, 24: 45-55.
Jackson, R.D., S.B. Idso, R.J. Reginato, and P.J. Pinter, 1981. Canopy temperature as a crop water stress indicator, Water Resources Research, 17(4): 1133-1138.
Jones, H.G., R. Serraj, B.R. Loveys, L. Xiong, A. Wheaton, and A.H. Price, 2009. Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field, Functional Plant Biology, 36(11): 978.
Kelly, J., N. Kljun, P.O. Olsson, L. Mihai, B. Liljeblad, P. Weslien, L. Klemedtsson, and L. Eklundh, 2019. Challenges and best practices for deriving temperature data from an uncalibrated UAV thermal infrared camera, Remote Sensing, 11(5): 567.
Lee, K.S., 2019. Atmospheric correction issues of optical imagery in land remote sensing, Korean Journal of Remote Sensing, 35(6-3): 1299-1312 (in Korean with English abstract).
Na, S.I., H.Y. Ahn, C.W. Park, S.Y. Hong, K.H. So, and K.D. Lee, 2020. Crop Water Stress Index (CWSI) Mapping for Evaluation of Abnormal Growth of Spring Chinese Cabbage Using Drone-based Thermal Infrared Image, Korean Journal of Remote Sensing, 36(5-1): 667-677 (in Korean with English abstract).
Olioso, A., 1995. Estimating the difference between brightness and surface temperatures for a vegetal canopy, Agriculture Forest and Meteorology, 72(3-4): 237-242.
Ryu, J.H., K.S. Han, J. Cho, C.S. Lee, H.J. Yoon, J.M. Yeom, and M.L. Ou, 2015. Estimating midday near-surface air temperature by weighted consideration of surface and atmospheric moisture conditions using COMS and SPOT satellite data, International Journal of Remote Sensing, 36(13): 3503-3518.
Ryu, J.H., S.I. Na, and J. Cho, 2020. Inter-Comparison of Normalized Difference Vegetation Index Measured from Different Footprint Sizes in Cropland, Remote Sensing, 12(18): 2980.
Sandholt, I., K. Rasmussen, and J.A. Andersen, 2002. simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status, Remote Sensing of Environment, 79(2-3): 213-224.
Song, B. and K. Park, 2020. Verification of Accuracy of unmanned aerial vehicle (UAV) land surface temperature images using in-situ data, Remote Sensing, 12(2): 288.
Solangi, G.S., A.A. Siyal, and P. Siyal, 2019. Spatiotemporal dynamics of land surface temperature and its impact on the vegetation, Civil Engineering Journal, 5(8): 1753-1763.
Zhang, L., H. Zhang, Y. Niu, and W. Han, 2019. Mapping maize water stress based on UAV multispectral remote sensing, Remote Sensing, 11(6): 605.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
오픈액세스 학술지에 출판된 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.