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with China’s CropWatch system. International Journal of Digital Earth, 7, 113–137.
Wu, B., Gommes, R., Zhang, M., et al. (2015). Global crop monitoring: A satellite-based hierarchical approach. Remote Sensing, 7, 3907–3933. https://doi.org/10.3390/rs70403907.
Yang, X., Zhu, W., Pan, Y., & Jia, B. (2007). Spatial sampling design for crop acreage estimation.
Yang, Z., Zhao, H., Di, L., Yu, G. (2009). A comparison of vegetation indices for corn and soybean
vegetation condition monitoring. In 2009 IEEE international geoscience and remote sensing
symposium (IGARSS 2009). IEEE, Cape Town, South Africa, p IV-801-IV-804.
Yang, C., Everitt, J. H., & Murden, D. (2011a). Evaluating high resolution SPOT 5 satellite imagery
for crop identification. Computers and Electronics in Agriculture, 75, 347–354. https://doi.org/
10.1016/j.compag.2010.12.012.
Yang, Z., Di, L., Yu, G., & Chen, Z. (2011b). Vegetation condition indices for crop vegetation
condition monitoring. In Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE
International. IEEE, pp. 3534–3537.
Yang, Z., Yu, G., Di, L., Zhang, B. (2013). Web service-based vegetation condition monitoring
system-VegScape. In Proceeding of iEEE IGARSS’2013.
Yang, Z., Hu, L., Yu, G., et al. (2016). Web service-based SMAP soil moisture data visualization,
dissemination and analytics based on vegscape framework. IEEE, pp 3624–3627.
Yazdani, R., Ryerson, A. R., & Derenyi, E. (1981). Vegetation change detection in an area—A
simple approach for use with geo-data base. In Proceedings of the 7th Canadian symposium on
remote sensing. pp. 88–92.
You, J., Li, X., Low, M., et al. (2017). Deep Gaussian process for crop yield prediction based on
remote sensing data.
Yu, G., Di, L., Yang, Z., et al. (2012a). Crop condition assessment using high temporal resolution
satellite images. In The first international conference on agro-geoinformatics 2012. IEEE,
Shanghai, China.
Yu, G., Di, L., Yang, Z., et al. (2012b). Corn growth stage estimation using time series vegetation
index. In 2012 first international conference on agro-geoinformatics (Agro-Geoinformatics).
pp. 1–6.
Zarco-Tejada, P. J., Ustin, S. L., & 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, 641. https://doi.org/10.2134/agronj2003.0257.
Zhang, X., Zhang, M., Zheng, Y., & Wu, B. (2016). Crop mapping using PROBA-V time series
data at the Yucheng and Hongxing farm in China. Remote Sensing, 8, 915. https://doi.org/10.
3390/rs8110915.
Zhu, Z., & Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat
imagery. Remote Sensing of Environment, 118, 83–94.
10 Crop Pattern and Status Monitoring
203
with China’s CropWatch system. International Journal of Digital Earth, 7, 113–137.
Wu, B., Gommes, R., Zhang, M., et al. (2015). Global crop monitoring: A satellite-based hierarchical approach. Remote Sensing, 7, 3907–3933. https://doi.org/10.3390/rs70403907.
Yang, X., Zhu, W., Pan, Y., & Jia, B. (2007). Spatial sampling design for crop acreage estimation.
Yang, Z., Zhao, H., Di, L., Yu, G. (2009). A comparison of vegetation indices for corn and soybean
vegetation condition monitoring. In 2009 IEEE international geoscience and remote sensing
symposium (IGARSS 2009). IEEE, Cape Town, South Africa, p IV-801-IV-804.
Yang, C., Everitt, J. H., & Murden, D. (2011a). Evaluating high resolution SPOT 5 satellite imagery
for crop identification. Computers and Electronics in Agriculture, 75, 347–354. https://doi.org/
10.1016/j.compag.2010.12.012.
Yang, Z., Di, L., Yu, G., & Chen, Z. (2011b). Vegetation condition indices for crop vegetation
condition monitoring. In Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE
International. IEEE, pp. 3534–3537.
Yang, Z., Yu, G., Di, L., Zhang, B. (2013). Web service-based vegetation condition monitoring
system-VegScape. In Proceeding of iEEE IGARSS’2013.
Yang, Z., Hu, L., Yu, G., et al. (2016). Web service-based SMAP soil moisture data visualization,
dissemination and analytics based on vegscape framework. IEEE, pp 3624–3627.
Yazdani, R., Ryerson, A. R., & Derenyi, E. (1981). Vegetation change detection in an area—A
simple approach for use with geo-data base. In Proceedings of the 7th Canadian symposium on
remote sensing. pp. 88–92.
You, J., Li, X., Low, M., et al. (2017). Deep Gaussian process for crop yield prediction based on
remote sensing data.
Yu, G., Di, L., Yang, Z., et al. (2012a). Crop condition assessment using high temporal resolution
satellite images. In The first international conference on agro-geoinformatics 2012. IEEE,
Shanghai, China.
Yu, G., Di, L., Yang, Z., et al. (2012b). Corn growth stage estimation using time series vegetation
index. In 2012 first international conference on agro-geoinformatics (Agro-Geoinformatics).
pp. 1–6.
Zarco-Tejada, P. J., Ustin, S. L., & 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, 641. https://doi.org/10.2134/agronj2003.0257.
Zhang, X., Zhang, M., Zheng, Y., & Wu, B. (2016). Crop mapping using PROBA-V time series
data at the Yucheng and Hongxing farm in China. Remote Sensing, 8, 915. https://doi.org/10.
3390/rs8110915.
Zhu, Z., & Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat
imagery. Remote Sensing of Environment, 118, 83–94.
10 Crop Pattern and Status Monitoring
203