Li, A., Liang, S., Wang, A., & Qin, J. (2007). Estimating crop yield from multi-temporal satellite
data using multivariate regression and neural network techniques. Photogrammetric Engineering & Remote Sensing, 73, 1149–1157. https://doi.org/10.14358/PERS.73.10.1149.
Lopiano, K. K., Lamas, A. C., Abreu, D. A., et al. (2011). Adjusting the June area survey estimate
of the number of US farms for misclassification and non-response. United States Department of
Agriculture, National Agricultural Statistics Service.
Löw, F., Michel, U., Dech, S., & Conrad, C. (2013). Impact of feature selection on the accuracy and
spatial uncertainty of per-field crop classification using support vector machines. ISPRS Journal
of Photogrammetry and Remote Sensing, 85, 102–119.
Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving
classification performance. International Journal of Remote Sensing, 28, 823–870. https://doi.
org/10.1080/01431160600746456.
Lu, D., Ge, H., He, S., et al. (2008). Pixel-based Minnaert correction method for reducing
topographic effects on a landsat 7 ETM+ image. Photogrammetric Engineering & Remote
Sensing, 74, 1343–1350. https://doi.org/10.14358/PERS.74.11.1343.
Lyapustin, A., Wang, Y., & Frey, R. (2008). An automatic cloud mask algorithm based on time
series of MODIS measurements. Journal of Geophysical Research. https://doi.org/10.1029/
2007JD009641.
Ma, M., & Veroustraete, F. (2006). Reconstructing pathfinder AVHRR land NDVI time-series data
for the Northwest of China. Advances in Space Research, 37, 835–840. https://doi.org/10.1016/
j.asr.2005.08.037.
Maas, S. J. (1988). Use of remotely-sensed information in agricultural crop growth models.
Ecological Modelling, 41, 247–268. https://doi.org/10.1016/0304-3800(88)90031-2.
MacDonald, R. B., & Hall, F. G. (1980). Global crop forecasting. Science, 208, 670–679.
Marshall, W., & Boshuizen, C. (2013). Planet labs’ remote sensing satellite system.
Martínez-Casasnovas, J. A., Martín-Montero, A., & Auxiliadora Casterad, M. (2005). Mapping
multi-year cropping patterns in small irrigation districts from time-series analysis of Landsat TM
images. European Journal of Agronomy, 23, 159–169. https://doi.org/10.1016/j.eja.2004.11.
004.
Mather, P. M., & Koch, M. (2011). Computer processing of remotely-sensed images: An introduction, 4th ed., 1. impr. Oxford: Wiley-Blackwell.
McNairn, H., & Protz, R. (1993). Mapping corn residue cover on agricultural fields in Oxford
County, Ontario, Using Thematic Mapper. Canadian Journal of Remote Sensing, 19, 152–159.
https://doi.org/10.1080/07038992.1993.10874543.
Meng, J., & Wu, B. (2008). Study on the crop condition monitoring methods with remote sensing.
In J. Chen (Ed.), The International Archives of the Photogrammetry (pp. 945–956). Beijing:
Remote Sensing and Spatial Information Sciences. International Society for Photogrammetry
and Remote Sensing.
Mladenova, I. E., Bolten, J. D., Crow, W. T., et al. (2017). Intercomparison of soil moisture,
evaporative stress, and vegetation indices for estimating corn and soybean yields over the
U.S. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,
10, 1328–1343. https://doi.org/10.1109/JSTARS.2016.2639338.
Mueller, R. (2013). VegScape: A NASS Web Service-based U.S. Crop Condition Monitoring
System. United States Department of Agriculture.
Murthy, C. S., Raju, P. V., & Badrinath, K. V. S. (2003). Classification of wheat crop with multitemporal images: Performance of maximum likelihood and artificial neural networks. International Journal of Remote Sensing, 24, 4871–4890. https://doi.org/10.1080/
0143116031000070490.
Nagol, J. R., Sexton, J. O., Anand, A., et al. (2017). Isolating type-specific phenologies through
spectral unmixing of satellite time series. International Journal of Digital Earth 1–13.
NASS. (2012). The yield forecasting program of NASS. The Statistical Methods Branch, Statistics
Division, National Agricultural Statistics Service, U.S. Department of Agriculture, Washington,
DC., USA.
200
E. G. Yu and Z. Yang
data using multivariate regression and neural network techniques. Photogrammetric Engineering & Remote Sensing, 73, 1149–1157. https://doi.org/10.14358/PERS.73.10.1149.
Lopiano, K. K., Lamas, A. C., Abreu, D. A., et al. (2011). Adjusting the June area survey estimate
of the number of US farms for misclassification and non-response. United States Department of
Agriculture, National Agricultural Statistics Service.
Löw, F., Michel, U., Dech, S., & Conrad, C. (2013). Impact of feature selection on the accuracy and
spatial uncertainty of per-field crop classification using support vector machines. ISPRS Journal
of Photogrammetry and Remote Sensing, 85, 102–119.
Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving
classification performance. International Journal of Remote Sensing, 28, 823–870. https://doi.
org/10.1080/01431160600746456.
Lu, D., Ge, H., He, S., et al. (2008). Pixel-based Minnaert correction method for reducing
topographic effects on a landsat 7 ETM+ image. Photogrammetric Engineering & Remote
Sensing, 74, 1343–1350. https://doi.org/10.14358/PERS.74.11.1343.
Lyapustin, A., Wang, Y., & Frey, R. (2008). An automatic cloud mask algorithm based on time
series of MODIS measurements. Journal of Geophysical Research. https://doi.org/10.1029/
2007JD009641.
Ma, M., & Veroustraete, F. (2006). Reconstructing pathfinder AVHRR land NDVI time-series data
for the Northwest of China. Advances in Space Research, 37, 835–840. https://doi.org/10.1016/
j.asr.2005.08.037.
Maas, S. J. (1988). Use of remotely-sensed information in agricultural crop growth models.
Ecological Modelling, 41, 247–268. https://doi.org/10.1016/0304-3800(88)90031-2.
MacDonald, R. B., & Hall, F. G. (1980). Global crop forecasting. Science, 208, 670–679.
Marshall, W., & Boshuizen, C. (2013). Planet labs’ remote sensing satellite system.
Martínez-Casasnovas, J. A., Martín-Montero, A., & Auxiliadora Casterad, M. (2005). Mapping
multi-year cropping patterns in small irrigation districts from time-series analysis of Landsat TM
images. European Journal of Agronomy, 23, 159–169. https://doi.org/10.1016/j.eja.2004.11.
004.
Mather, P. M., & Koch, M. (2011). Computer processing of remotely-sensed images: An introduction, 4th ed., 1. impr. Oxford: Wiley-Blackwell.
McNairn, H., & Protz, R. (1993). Mapping corn residue cover on agricultural fields in Oxford
County, Ontario, Using Thematic Mapper. Canadian Journal of Remote Sensing, 19, 152–159.
https://doi.org/10.1080/07038992.1993.10874543.
Meng, J., & Wu, B. (2008). Study on the crop condition monitoring methods with remote sensing.
In J. Chen (Ed.), The International Archives of the Photogrammetry (pp. 945–956). Beijing:
Remote Sensing and Spatial Information Sciences. International Society for Photogrammetry
and Remote Sensing.
Mladenova, I. E., Bolten, J. D., Crow, W. T., et al. (2017). Intercomparison of soil moisture,
evaporative stress, and vegetation indices for estimating corn and soybean yields over the
U.S. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,
10, 1328–1343. https://doi.org/10.1109/JSTARS.2016.2639338.
Mueller, R. (2013). VegScape: A NASS Web Service-based U.S. Crop Condition Monitoring
System. United States Department of Agriculture.
Murthy, C. S., Raju, P. V., & Badrinath, K. V. S. (2003). Classification of wheat crop with multitemporal images: Performance of maximum likelihood and artificial neural networks. International Journal of Remote Sensing, 24, 4871–4890. https://doi.org/10.1080/
0143116031000070490.
Nagol, J. R., Sexton, J. O., Anand, A., et al. (2017). Isolating type-specific phenologies through
spectral unmixing of satellite time series. International Journal of Digital Earth 1–13.
NASS. (2012). The yield forecasting program of NASS. The Statistical Methods Branch, Statistics
Division, National Agricultural Statistics Service, U.S. Department of Agriculture, Washington,
DC., USA.
200
E. G. Yu and Z. Yang