Hong, S.-Y., Sudduth, K.-A., Kitchen, N.-R., et al. (2004). Comparison of remote sensing and crop
growth models for estimating within-field LAI variability. Korean journal of remote sensing,
20, 175–188.
Huang, Q., Zhou, Q., Wu, W., et al. (2012). Extraction of planting areas of major crops and crop
growth monitoring in northeast China. Intelligent Automation & Soft Computing, 18,
1023–1033. https://doi.org/10.1080/10798587.2008.10643307.
Huete, A. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25,
295–309. https://doi.org/10.1016/0034-4257(88)90106-X.
Hulley, G. C., & Hook, S. J. (2008). A new methodology for cloud detection and classification with
ASTER data. Geophysical Research Letters. https://doi.org/10.1029/2008GL034644.
Immitzer, M., Vuolo, F., & Atzberger, C. (2016). First experience with sentinel-2 data for crop and
tree species classifications in central Europe. Remote Sensing, 8, 166. https://doi.org/10.3390/
rs8030166.
Irwin, S. H., Sanders, D. R., & Good, D. L. (2014). Evaluation of selected USDA WAOB and
NASS forecasts and estimates in corn and soybeans.
JARS. (1999). Remote sensing notes.. Japan Association of Remote Sensing.
Jiao, X., Kovacs, J. M., Shang, J., et al. (2014). Object-oriented crop mapping and monitoring using
multi-temporal polarimetric RADARSAT-2 data. ISPRS Journal of Photogrammetry and
Remote Sensing, 96, 38–46. https://doi.org/10.1016/j.isprsjprs.2014.06.014.
Johnson, D. M., & Mueller, R. (2010). The 2009 cropland data layer. PE&RS, Photogrammetric
Engineering & Remote Sensing, 76, 1201–1205.
Jonsson, P., & Eklundh, L. (2002). Seasonality extraction by function fitting to time-series of
satellite sensor data. IEEE Transactions on Geoscience and Remote Sensing, 40, 1824–1832.
https://doi.org/10.1109/TGRS.2002.802519.
Karnieli, A., Bayasgalan, M., Bayarjargal, Y., et al. (2006). Comments on the use of the vegetation
health index over Mongolia. International Journal of Remote Sensing, 27, 2017–2024. https://
doi.org/10.1080/01431160500121727.
Kaufman, Y. J., Tanré, D., Gordon, H. R., et al. (1997). Passive remote sensing of tropospheric
aerosol and atmospheric correction for the aerosol effect. Journal of Geophysical Research:
Atmospheres, 102, 16815–16830.
Kautz, J. S. (2017). In-situ cameras for radiometric correction of remotely sensed data. The
University of Arizona.
Kim, D., & Kaluarachchi, J. (2015). Validating FAO AquaCrop using Landsat images and regional
crop information. Agricultural Water Management, 149, 143–155. https://doi.org/10.1016/j.
agwat.2014.10.013.
Kim, H.-O., & Yeom, J.-M. (2015). Sensitivity of vegetation indices to spatial degradation of
RapidEye imagery for paddy rice detection: A case study of South Korea. GIScience & Remote
Sensing, 52, 1–17. https://doi.org/10.1080/15481603.2014.1001666.
King, D., Jones, R. J. A., & Thomasson, A. J. (Eds.). (1995). European land information systems
for agro-environmental monitoring. Joint Research Centre, European Commission,
Luxembourg.
Kogan, F. N. (1995). Droughts of the late 1980s in the United States as derived from NOAA polarorbiting satellite data. Bulletin of the American Meteorological Society, 76, 655–668.
Kogan, F., & Sullivan, J. (1993). Development of global drought-watch system using NOAA/
AVHRR data. Advances in Space Research, 13, 219–222. https://doi.org/10.1016/0273-1177
(93)90548-P.
Kussul, N., Lavreniuk, M., Skakun, S., & Shelestov, A. (2017). Deep learning classification of land
cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters,
14, 778–782. https://doi.org/10.1109/LGRS.2017.2681128.
Lei, T. C., Wan, S., & Chou, T. Y. (2008). The comparison of PCA and discrete rough set for
feature extraction of remote sensing image classification – A case study on rice classification,
Taiwan. Computational Geosciences, 12, 1–14. https://doi.org/10.1007/s10596-007-9057-7.
10 Crop Pattern and Status Monitoring
199
growth models for estimating within-field LAI variability. Korean journal of remote sensing,
20, 175–188.
Huang, Q., Zhou, Q., Wu, W., et al. (2012). Extraction of planting areas of major crops and crop
growth monitoring in northeast China. Intelligent Automation & Soft Computing, 18,
1023–1033. https://doi.org/10.1080/10798587.2008.10643307.
Huete, A. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25,
295–309. https://doi.org/10.1016/0034-4257(88)90106-X.
Hulley, G. C., & Hook, S. J. (2008). A new methodology for cloud detection and classification with
ASTER data. Geophysical Research Letters. https://doi.org/10.1029/2008GL034644.
Immitzer, M., Vuolo, F., & Atzberger, C. (2016). First experience with sentinel-2 data for crop and
tree species classifications in central Europe. Remote Sensing, 8, 166. https://doi.org/10.3390/
rs8030166.
Irwin, S. H., Sanders, D. R., & Good, D. L. (2014). Evaluation of selected USDA WAOB and
NASS forecasts and estimates in corn and soybeans.
JARS. (1999). Remote sensing notes.. Japan Association of Remote Sensing.
Jiao, X., Kovacs, J. M., Shang, J., et al. (2014). Object-oriented crop mapping and monitoring using
multi-temporal polarimetric RADARSAT-2 data. ISPRS Journal of Photogrammetry and
Remote Sensing, 96, 38–46. https://doi.org/10.1016/j.isprsjprs.2014.06.014.
Johnson, D. M., & Mueller, R. (2010). The 2009 cropland data layer. PE&RS, Photogrammetric
Engineering & Remote Sensing, 76, 1201–1205.
Jonsson, P., & Eklundh, L. (2002). Seasonality extraction by function fitting to time-series of
satellite sensor data. IEEE Transactions on Geoscience and Remote Sensing, 40, 1824–1832.
https://doi.org/10.1109/TGRS.2002.802519.
Karnieli, A., Bayasgalan, M., Bayarjargal, Y., et al. (2006). Comments on the use of the vegetation
health index over Mongolia. International Journal of Remote Sensing, 27, 2017–2024. https://
doi.org/10.1080/01431160500121727.
Kaufman, Y. J., Tanré, D., Gordon, H. R., et al. (1997). Passive remote sensing of tropospheric
aerosol and atmospheric correction for the aerosol effect. Journal of Geophysical Research:
Atmospheres, 102, 16815–16830.
Kautz, J. S. (2017). In-situ cameras for radiometric correction of remotely sensed data. The
University of Arizona.
Kim, D., & Kaluarachchi, J. (2015). Validating FAO AquaCrop using Landsat images and regional
crop information. Agricultural Water Management, 149, 143–155. https://doi.org/10.1016/j.
agwat.2014.10.013.
Kim, H.-O., & Yeom, J.-M. (2015). Sensitivity of vegetation indices to spatial degradation of
RapidEye imagery for paddy rice detection: A case study of South Korea. GIScience & Remote
Sensing, 52, 1–17. https://doi.org/10.1080/15481603.2014.1001666.
King, D., Jones, R. J. A., & Thomasson, A. J. (Eds.). (1995). European land information systems
for agro-environmental monitoring. Joint Research Centre, European Commission,
Luxembourg.
Kogan, F. N. (1995). Droughts of the late 1980s in the United States as derived from NOAA polarorbiting satellite data. Bulletin of the American Meteorological Society, 76, 655–668.
Kogan, F., & Sullivan, J. (1993). Development of global drought-watch system using NOAA/
AVHRR data. Advances in Space Research, 13, 219–222. https://doi.org/10.1016/0273-1177
(93)90548-P.
Kussul, N., Lavreniuk, M., Skakun, S., & Shelestov, A. (2017). Deep learning classification of land
cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters,
14, 778–782. https://doi.org/10.1109/LGRS.2017.2681128.
Lei, T. C., Wan, S., & Chou, T. Y. (2008). The comparison of PCA and discrete rough set for
feature extraction of remote sensing image classification – A case study on rice classification,
Taiwan. Computational Geosciences, 12, 1–14. https://doi.org/10.1007/s10596-007-9057-7.
10 Crop Pattern and Status Monitoring
199