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References

Aksoy, S., Yalniz, I.Z., and Tasdemir, K., 2012. Automatic Detection and Segmentation of Orchards Using Very High Resolution Imagery. IEEE Transactions on Geoscience and Remote Sensing, 50 (8), 3117–3131.

Atzberger, C., 2013. Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs. Remote Sensing, 5 (2), 949–981.

Blaschke, T., 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65 (1), 2–16.

Bolton, D.K. and Friedl, M.., 2013. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agric. For. Meteorol., 173, 74–84.

Boschetti, M., Stroppiana, D., Brivio, P. a., and Bocchi, S., 2009. Multi-year monitoring of rice crop phenology through time series analysis of MODIS images. International Journal of Remote Sensing, 30 (18), 4643–4662.

Candiago, S., Remondino, F., De Giglio, M., Dubbini, M., and Gattelli, M., 2015. Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images. Remote Sensing, 7 (4), 4026–4047.

Chen, B., Qiu, F., Wu, B., and Du, H., 2015. Image Segmentation Based on Constrained Spectral Variance Difference and Edge Penalty. Remote Sensing, 7 (5), 5980–6004.

Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., He, C., Han, G., Peng, S., Lu, M., Zhang, W., Tong, X., and Mills, J., 2015. Global land cover mapping at 30m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103, 7–27.

Clevers, J.G.P.W., 1989. Application of a weighted infrared-red vegetation index for estimating leaf Area Index by Correcting for Soil Moisture. Remote Sensing of Environment, 29 (1), 25–37.

Confalonieri, R., Francone, C., and Foi, M., 2014. The pocketlai smartphone app: An alternative method for leaf area index estimation. In: Proceedings of the 7th International Congress on Environmental Modelling and Software, San Diego, CA, USA. 15–19.

Conrad, C., Fritsch, S., Zeidler, J., Rücker, G., and Dech, S., 2010. Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data. Remote Sensing, 2 (4), 1035–1056.

Dempewolf, J., Adusei, B., Becker-Reshef, I., Hansen, M., Potapov, P., Khan, A., and Barker, B., 2014. Wheat Yield Forecasting for Punjab Province from Vegetation Index Time Series and Historic Crop Statistics. Remote Sensing, 6 (10), 9653–9675.

Dorigo, W.A., Zurita-Milla, R., de Wit, A.J.W., Brazile, J., Singh, R., and Schaepman, M.E., 2007. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. International Journal of Applied Earth Observation and Geoinformation, 9 (2), 165–193.

Duro, D.C., Franklin, S.E., and Dubé, M.G., 2012. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment, 118, 259–272.

Forkuor, G., Conrad, C., Thiel, M., Landmann, T., and Barry, B., 2015. Evaluating the sequential masking classification approach for improving crop discrimination in the Sudanian Savanna of West Africa. Computers and Electronics in Agriculture, 118, 380–389.

Forkuor, G., Conrad, C., Thiel, M., Ullmann, T., and Zoungrana, E., 2014. Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa. Remote Sensing, 6 (7), 6472–6499.

Francone, C., Pagani, V., Foi, M., Cappelli, G., and Confalonieri, R., 2014. Field Crops Research Comparison of leaf area index estimates by ceptometer and PocketLAI smart app in canopies with different structures ଝ. Field Crops Research, 155, 38–41.

Fritz, S., McCallum, I., Schill, C., Perger, C., Grillmayer, R., Achard, F., Kraxner, F., and Obersteiner, M., 2009. Geo-Wiki.Org: The Use of Crowdsourcing to Improve Global Land Cover. Remote Sensing, 1 (3), 345–354.

Gherboudj, I., Magagi, R., Berg, A.A., and Toth, B., 2011. Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data. Remote Sensing of Environment, 115 (1), 33–43.

Haack, B. and Bechdol, M., 2000. Integrating multisensor data and RADAR texture measures for land cover mapping. Computers & Geosciences, 26 (4), 411–421.

Jin, H. and Eklundh, L., 2014. A physically based vegetation index for improved monitoring of plant phenology. Remote Sensing of Environment, 152, 512–525.

Li, W., Weiss, M., Waldner, F., Defourny, P., Demarez, V., Morin, D., Hagolle, O., and Baret, F., 2015. A Generic Algorithm to Estimate LAI, FAPAR and FCOVER Variables from SPOT4_HRVIR and Landsat Sensors: Evaluation of the Consistency and Comparison with Ground Measurements. Remote Sensing, 7 (12), 15494–15516.

Liu, J., Pattey, E., and Jégo, G., 2012. Assessment of vegetation indices for regional crop green LAI estimation from Landsat images over multiple growing seasons. Remote Sensing of Environment, 123, 347–358.

Maki, M. and Homma, K., 2014. Empirical Regression Models for Estimating Multiyear Leaf Area Index of Rice from Several Vegetation Indices at the Field Scale. Remote Sensing, 6 (6), 4764–4779.

McNairn, H., Champagne, C., Shang, J., Holmstrom, D., and Reichert, G., 2009. Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories. ISPRS Journal of Photogrammetry and Remote Sensing, 64 (5), 434–449.

Möller, M., Lymburner, L., and Volk, M., 2007. The comparison index: A tool for assessing the accuracy of image segmentation. International Journal of Applied Earth Observation and Geoinformation, 9 (3), 311–321.

Morel, J., Todoroff, P., Bégué, A., Bury, A., Martiné, J.-F., and Petit, M., 2014. Toward a Satellite-Based System of Sugarcane Yield Estimation and Forecasting in Smallholder Farming Conditions: A Case Study on Reunion Island. Remote Sensing, 6 (7), 6620–6635.

Muller, E. and Décamps, H., 2001. Modeling soil moisture–reflectance. Remote Sensing of Environment, 76 (2), 173–180.

Van Niel, T.G. and McVicar, T.R., 2004. Determining temporal windows for crop discrimination with remote sensing: a case study in south-eastern Australia. Computers and Electronics in Agriculture, 45 (1–3), 91–108.

Novack, T., Esch, T., Kux, H., and Stilla, U., 2011. Machine Learning Comparison between WorldView-2 and QuickBird-2-Simulated Imagery Regarding Object-Based Urban Land Cover Classification. Remote Sensing, 3 (12), 2263–2282.

Peña-Barragán, J.M., Ngugi, M.K., Plant, R.E., and Six, J., 2011. Object-based crop identification using multiple vegetation indices, textural features and crop phenology. Remote Sensing of Environment, 115 (6), 1301–1316.

Peng, Y. and Gitelson, A.A., 2012. Remote estimation of gross primary productivity in soybean and maize based on total crop chlorophyll content. Remote Sensing of Environment, 117, 440–448.

Rajan, N., Maas, S.J., and Kathilankal, J.C., 2010. Estimating Crop Water Use of Cotton in the Texas High Plains All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information stor. Agronomy Journal, 102, 1641–1651.

Richter, K., Hank, T.B., Vuolo, F., Mauser, W., and D’Urso, G., 2012. Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping. Remote Sensing, 4 (12), 561–582.

Rodrigues, F.A., Bramley, R.G. V, and Gobbett, D.L., 2015. Proximal soil sensing for Precision Agriculture: Simultaneous use of electromagnetic induction and gamma radiometrics in contrasting soils. Geoderma, 243–244, 183–195.

Roy, D.P., Wulder, M.A., Loveland, T.R., C.E., W., Allen, R.G., Anderson, M.C., Helder, D., Irons, J.R., Johnson, D.M., Kennedy, R., Scambos, T.A., Schaaf, C.B., Schott, J.R., Sheng, Y., Vermote, E.F., Belward, A.S., Bindschadler, R., Cohen, W.B., Gao, F., Hipple, J.D., Hostert, P., Huntington, J., Justice, C.O., Kilic, A., Kovalskyy, V., Lee, Z.P., Lymburner, L., Masek, J.G., McCorkel, J., Shuai, Y., Trezza, R., Vogelmann, J., Wynne, R.H., and Zhu, Z., 2014. Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154–172.

Shang, J., Liu, J., Huffman, T., Qian, B., Pattey, E., Wang, J., Zhao, T., Geng, X., Kroetsch, D., Dong, T., and Lantz, N., 2014. Estimating plant area index for monitoring crop growth dynamics using Landsat-8 and RapidEye images. Journal of Applied Remote Sensing, 8 (1), 85196.

Shang, J., Liu, J., Ma, B., Zhao, T., Jiao, X., Geng, X., Huffman, T., Kovacs, J.M., and Walters, D., 2015. Mapping spatial variability of crop growth conditions using RapidEye data in Northern Ontario, Canada. Remote Sensing of Environment, 168, 113–125.

Sheoran, A. and Haack, B., 2013. Classification of California agriculture using quad polarization radar data and Landsat Thematic Mapper data. GIScience & Remote Sensing, 50 (1), 50–63.

Son, N.-T., Chen, C.-F., Chen, C.-R., Duc, H.-N., and Chang, L.-Y., 2013. A Phenology-Based Classification of Time-Series MODIS Data for Rice Crop Monitoring in Mekong Delta, Vietnam. Remote Sensing, 6 (1), 135–156.

Susan Moran, M., 2000. Soil moisture evaluation using multi-temporal synthetic aperture radar (SAR) in semiarid rangeland. Agricultural and Forest Meteorology, 105 (1–3), 69–80.

Thorp, K.R., Wang, G., West, A.L., Moran, M.S., Bronson, K.F., White, J.W., and Mon, J., 2012. Estimating crop biophysical properties from remote sensing data by inverting linked radiative transfer and ecophysiological models. Remote Sensing of Environment, 124, 224–233.

Tucker, C.J., Vanpraet, C.L., Sharman, M.J., and van Ittersum, G., 1985. Satellite remote sensing of total herbaceous biomass production in the Senegalese Sahel: 1980–1984. Remote Sensing of Environment, 17, 233–249.

Turner, D.P., Cohen, W.B., Kennedy, R.E., Fassnacht, K.S., and Briggs, J.M., 1999. Relationships between Leaf Area Index and Landsat TM Spectral Vegetation Indices across Three Temperate Zone Sites. Remote Sensing of Environment, 70 (1), 52–68.

Viña, A., Gitelson, A.A., Nguy-Robertson, A.L., and Peng, Y., 2011. Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sensing of Environment, 115 (12), 3468–3478.

De Wit, A.J.W. and Clevers, J.G.P.W., 2004. Efficiency and accuracy of per-field classification for operational crop mapping. International Journal of Remote Sensing, 25 (20), 4091–4112.

Yuan, J., Wang, D., and Li, R., 2014. Remote Sensing Image Segmentation by Combining Spectral and Texture Features. IEEE Transactions on Geoscience and Remote Sensing, 52 (1), 16–24.

Zarco-Tejada, P.J., Ustin, S.L., and Whiting, M.L., 2005. Temporal and Spatial Relationships between Within-Field Yield Variability in Cotton and High-Spatial Hyperspectral Remote Sensing Imagery. Agronomy Journal, 97 (3), 641.

Zurita-Milla, R., Laurent, V.C.E., and van Gijsel, J.A.E., 2015. Visualizing the ill-posedness of the inversion of a canopy radiative transfer model: A case study for Sentinel-2. International Journal of Applied Earth Observation and Geoinformation, 43, 7–18.