07-20 July FULL - page 441

European Commission. 2016. Towards future Copernicus service
components in support to agriculture? <
/
jrc/sites/jrcsh/files/Copernicus_concept_note_agriculture.pdf>
Accessed on 7 May 2020.
GISAT s.r.o. 2018. Sentinel-2 Agriculture: Czech Agriculture National
Demonstrator (CzechAgri) Final Report. <
-
sen2agri.org/wp-content/uploads/docs/CzechAgri%20Final%20
Report%201.2.pdf> Accessed on 7 May 2020.
Hagolle, O., M. Huc, D. Villa Pascual and G. Dedieu. 2010. A multi-
temporal method for cloud detection, applied to Formosat-2,
Ven
µ
s, Landsat and Sentinel-2 images.
Remote Sensing of
Environment
114 (8):1747–1755.
Hagolle, O., M. Huc, D. Villa Pascual and G. Dedieu. 2015. A
multi-temporal and multi-spectral method to estimate aerosol
optical thickness over land, for the atmospheric correction of
formosat-2, Landsat, Ven
µ
s and Sentinel-2 images.
Remote
Sensing
7 (3):2668–2691.
Hoberg, T., F. Rottensteiner, R. Q. Feitosa, and C. Heipke. 2015.
Conditional random fields for multitemporal and multiscale
classification of optical satellite imagery.
IEEE Transactions on
Geoscience and Remote Sensing
53 (2):659–673.
Inglada, Jordi. 2016. OTB Gapfilling, a temporal gapfilling for
image time series library, Zenodo. <
zenodo.45572>.
Inglada, J., M. Arias, B. Tardy, O. Hagolle, S. Valero, D. Morin,
G. Dedieu, G. Sepulcre, S. Bontemps and P. Defourny. 2015.
Assessment of an operational system for crop type map
production using high temporal and spatial resolution satellite
optical imagery.
Remote Sensing
7 (9):12356–12379.
Karlen, D. L., E. G. Hurley, S. S. Andrews, C. A. Cambardella, D.
W. Meek, M. D. Duffy and A. P. Mallarino. 2006. Crop rotation
effects on soil quality at three northern corn/soybean belt
locations.
Agronomy Journal
98 (3):484–495.
Kenduiywoa, B. K., D. Bargiel and U. Soergel. 2015. Spatial-temporal
conditional random fields crop classification from Terrasar-X
images.
ISPRS Annals of the Photogrammetry, Remote Sensing
and Spatial Information Sciences
2 (3):79.
Kussul, N., G. Lemoine, F. J. Gallego, S. V. Skakun, M. Lavreniuk
and A. Y. Shelestov. 2016. Parcel-based crop classification in
Ukraine using Landsat-8 data and Sentinel-1a data.
IEEE Journal
of Selected Topics in Applied Earth Observations and Remote
Sensing
9 (6):2500–2508.
Lee, J.-S. 1980. Digital image enhancement and noise filtering by use
of local statistics.
IEEE Transactions on Pattern Analysis and
Machine Intelligence
(2):165–168.
Leite, P.B.C., R. Q. Feitosa, A. R. Formaggio, G. A. da Costa, O. Pedro,
K. Pakzad and I. Del’Arco Sanches. 2011. Hidden Markov
models for crop recognition in remote sensing image sequences.
Pattern Recognition Letters
32 (1):19–26.
Li, H., C. Zhang, S. Zhang and P. M. Atkinson. 2019. Full year crop
monitoring and separability assessment with fully-polarimetric
L-band UAVSAR: A case study in the Sacramento Valley,
California.
International Journal of Applied Earth Observation
and Geoinformation
74:45–56.
Liu, D., K. Song, J.R.G. Townshend and P. Gong. 2008. Using local
transition probability models in Markov random fields for forest
change detection.
Remote Sensing of Environment
112 (5):2222–
2231.
Manning, C. D., P. Raghavan and H. Schütze. 2008.
Introduction to
Information Retrieval
, vol. 1 (1). Cambridge, England: Cambridge
University Press.
Osman, J., J. Inglada and J.-F. Dejoux. 2015. Assessment of a Markov
logic model of crop rotations for early crop mapping.
Computers
and Electronics in Agriculture
113:234–243.
Ottosen, T.-B.ø, S.T.E. Lommen and C. A. Skjãÿth. 2019. Remote
sensing of cropping practice in northern Italy using time-series
from sentinel-2.
Computers and Electronics in Agriculture
157:232–238.
Palchowdhuri, Y., R. Valcarce-Diňeiro, P. King and M. Sanabria-Soto.
2018. Classification of multi-temporal spectral indices for crop
type mapping: A case study in Coalville, UK.
The Journal of
Agricultural Science
156 (1):24–36.
Roscher, R., B. Waske and W. Förstner. 2017. Kernel discriminative
random fields for land cover classification. Pages 1–5 in
Proceedings of IAPR Workshop on Pattern Recognition in
Remote Sensing
(
PRRS
).
Sen2-Agri. Czech agriculture national demonstrator - final report,
2018. <
docs/CzechAgri%20Final%20Report%201.2.pdf> Accessed on
February 6, 2018
Siachalou, S.u, G. Mallinis and M. Tsakiri-Strati. 2015. A hidden
Markov models approach for crop classification: Linking crop
phenology to time series of multi-sensor remote sensing data.
Remote Sensing
, 7 (4): 3633–3650.
Veloso, A., S. Mermoz, A. Bouvet, T. Le Toan, M. Planells, J.-F. Dejoux
and E. Ceschia. 2017. Understanding the temporal behavior of
crops using Sentinel-1 and Sentinel-2-like data for agricultural
applications.
Remote Sensing of Environment
199:415–426.
Vuolo, F., M. Neuwirth, M. Immitzer, C. Atzberger, and W.-T. Ng.
2018. How much does multi-temporal Sentinel-2 data improve
crop type classification?
International Journal of Applied Earth
Observation and Geoinformation
72:122–130.
Wagner, B. N. and B. Waske. 2018. Combining Sentinel-1 and
Sentinel-2 data for improved land use and land cover mapping
of monsoon regions.
International Journal of Applied Earth
Observation and Geoinformation
73:595–604.
Whelen, T. and P. Siqueira. 2017. Use of time-series L-band UAVSAR
data for the classification of agricultural fields in the San Joaquin
Valley.
Remote Sensing of Environment
193:216–224.
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