1 |
Park, S., S.-H. Cho, N.-W. Park, and H. Kim, 2021. Evaluation of spatio-temporal multi-sensor image fusion models for generating time-series Landsat images: A case study in Mt. Halla, Journal of Climate Research, 16(4): 291-306 (in Korean with English abstract). https://doi.org/10.14383/cri.2021.16.4.291
DOI
|
2 |
Zhu, X., J. Chen, F. Gao, X. Chen, and J.G. Masek, 2010. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions, Remote Sensing of Environment, 114(11): 2610-2623. https://doi.org/10.1016/j.rse.2010.05.032
DOI
|
3 |
Jin, Y., J. Zhu, S. Sung, and D.K. Lee, 2017. Application of satellite data spatiotemporal fusion in predicting seasonal NDVI, Korean Journal of Remote Sensing, 33(2): 149-158 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2017.33.2.4
DOI
|
4 |
Karagiannopoulou, A., A. Tsertou, G. Tsimiklis, and A. Amditis, 2022. Data fusion in earth observation and the role of citizen as a sensor: A scoping review of applications, methods and future trends, Remote Sensing, 14(5): 1263. https://doi.org/10.3390/rs14051263
DOI
|
5 |
Kim, Y. and N.-W. Park, 2019. Comparison of spatiotemporal fusion models of multiple satellite images for vegetation monitoring, Korean Journal of Remote Sensing, 35(6-3): 1209-1219 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2019.35.6.3.5
DOI
|
6 |
Kim, Y., P.C. Kyriakidis, and N.-W. Park, 2020. A cross-resolution, spatiotemporal geostatistical fusion model for combining satellite image time-series of different spatial and temporal resolutions, Remote Sensing, 12(10): 1553. https://doi.org/10.3390/rs12101553
DOI
|
7 |
Kown, S.-K., K.-M. Kim, and J. Lim, 2021. A study on pre-evaluation of tree species classification possibility of CAS500-4 using RapidEye satellite images, Korean Journal of Remote Sensing, 37(2): 291-304 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2021.37.2.9
DOI
|
8 |
U.S. Geological Survey, 2022a. Earth Explorer, https://earthexplorer.usgs.gov/, Accessed on Oct. 1, 2022.
|
9 |
Song, H. and B. Huang, 2013. Spatiotemporal satellite image fusion through one-pair image learning, IEEE Transactions on Geoscience and Remote Sensing, 51(4): 1883-1896. https://doi.org/10.1109/TGRS.2012.2213095
DOI
|
10 |
U.S. Department of Agriculture, 2022. National Agricultural Statistics Service, https://www.nass.usda.gov/, Accessed on Sep. 15, 2022.
|
11 |
U.S. Geological Survey, 2022b. Land Processes Distributed Active Archive Center, https://lpdaac.usgs.gov/, Accessed on Oct. 1, 2022.
|
12 |
Xue, J., Y. Leung, and T. Fung, 2017. A Bayesian data fusion approach to spatio-temporal fusion of remotely sensed images, Remote Sensing, 9(12): 1310. https://doi.org/10.3390/rs9121310
DOI
|
13 |
Yang, S., L. Gu, X. Li, T. Jiang, and R. Ren, 2020. Crop classification method based on optimal feature selection and hybrid CNN-RF networks for multi-temporal remote sensing imagery, Remote Sensing, 12(19): 3119. https://doi.org/10.3390/rs12193119
DOI
|
14 |
Ye, C.-S., 2021. Object-based image classification by integrating multiple classes in Hue channel images, Korean Journal of Remote Sensing, 37(6-3): 2011-2025 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2021.37.6.3.9
DOI
|
15 |
Kwak, G.-H. and N.-W. Park, 2022. Unsupervised domain adaptation with adversarial self-training for crop classification using remote sensing images, Remote Sensing, 14(18): 4639. https://doi.org/10.3390/rs14184639
DOI
|
16 |
Kim, Y., G.-H. Kwak, K.-D. Lee, S.-I. Na, C.-W. Park, and N.-W. Park, 2018. Performance evaluation of machine learning and deep learning algorithms in crop classification: Impact of hyper-parameters and training sample size, Korean Journal of Remote Sensing, 34(5): 811-827 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2018.34.5.9
DOI
|
17 |
Lark, T.J., I.H. Schelly, and H.K. Gibbs, 2021. Accuracy, bias, and improvements in mapping crops and cropland across the United States using the USDA cropland data layer, Remote Sensing, 13(5): 968. https://doi.org/10.3390/rs13050968
DOI
|
18 |
Kwak, G.-H. and N.-W. Park, 2019. Impact of texture information on crop classification with machine learning and UAV images, Remote Sensing, 9(4): 643. https://doi.org/10.3390/app9040643
DOI
|
19 |
Gao, F., J. Masek, M. Schwaller, and F. Hall, 2006. On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance, IEEE Transactions on Geoscience and Remote Sensing, 44(8): 2207-2218. https://doi.org/10.1109/TGRS.2006.872081
DOI
|
20 |
Shen, Y., X. Zhang, and Z. Yang, 2022. Mapping corn and soybean phenometrics at field scales over the United States Corn Belt by fusing time series of Landsat 8 and Sentinel-2 data with VIIRS data, ISPRS Journal of Photogrammetry and Remote Sensing, 186: 55-69. https://doi.org/10.1016/j.isprsjprs.2022.01.023
DOI
|
21 |
Ghamisi, P., B. Rasti, N. Yokoya, Q. Wang, B. Hofle, L. Bruzzone, F. Bovolo, M. Chi, K. Anders, R. Gloageun, P.M. Atkinson, and J.A. Benediktsson, 2019. Multisource and multitemporal data fusion in remote sensing: A comprehensive review of the state of the art, IEEE Geoscience and Remote Sensing Magazine, 7(1): 6-39. https://doi.org/10.1109/MGRS.2018.2890023
DOI
|
22 |
Huang, B. and H. Song, 2012. Spatiotemporal reflectance fusion via sparse representation, IEEE Transactions on Geoscience and Remote Sensing, 50(10): 3707-3716. https://doi.org/10.1109/TGRS.2012.2186638
DOI
|
23 |
Montero, P. and J.A. Vilar, 2014. TSclust: An R package for time series clustering, Journal of Statistical Software, 62(1): 1-43. https://doi.org/10.18637/jss.v062.i01
DOI
|
24 |
Kyriakidis, P.C., 2004. A geostatistical framework for area-to-point spatial interpolation, Geographical Analysis, 36(3): 259-289. https://doi.org/10.1111/j.1538-4632.2004.tb01135.x
DOI
|
25 |
Kyriakidis P.C. and A.G. Journel, 2001. Stochastic modeling of atmospheric pollution: A spatial time-series framework, Part 1: Methodology, Atmospheric Environment, 35(13): 2331-2337. https://doi.org/10.1016/S1352-2310(00)00541-0
DOI
|
26 |
Kyriakidis, P.C., N.L. Miller, and J. Kim, 2004. A spatial time series framework for simulating daily precipitation at regional scales, Journal of Hydrology, 297(1-4): 236-255. https://doi.org/10.1016/j.jhydrol.2004.04.022
DOI
|
27 |
Park, N.-W., Y. Kim, and G.-H. Kwak, 2019. An overview of theoretical and practical issues in spatial downscaling of coarse resolution satellite-derived products, Korean Journal of Remote Sensing, 35(4): 589-607. https://doi.org/10.7780/kjrs.2019.35.4.8
DOI
|
28 |
Pelletier, C., S. Valero, J. Inglada, N. Champion, and G. Dedieu, 2016. Assessing the robustness of random forests to map land cover with high resolution satellite image time series over large areas, Remote Sensing of Environment, 187: 156-168. https://doi.org/10.1016/j.rse.2016.10.010
DOI
|