Hao, P., Tang, H., Chen, Z., Meng, Q., & Kang, Y. (2020). Early-season crop type mapping using
30-m reference time series. Journal of Integrative Agriculture, 19(7), 1897–1911.
Houborg, R., & McCabe, M. F. (2018). A Cubesat enabled Spatio-temporal enhancement method
(CESTEM) utilizing planet, Landsat and MODIS data. Remote Sensing of Environment, 209,
211–226. https://doi.org/10.1016/j.rse.2018.02.067.
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of
the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing
of Environment, 83(1), 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2.
Jonsson, P., & Eklundh, L. (2004). TIMESAT—A program for analysing time-series of satellite
sensor data. Computational Geosciences, 30, 833–845.
Justice, C. O., Townshend, J. R. G., Vermote, E. F., Masuoka, E., Wolfe, R. E., Saleous, N., Roy,
D. P., & Morisette, J. (2002). An overview of MODIS land data processing and product status.
Remote Sensing of Environment, 83, 3–15.
Liu, L., Zhang, X., Yu, Y., Gao, F., & Yang, Z. (2018). Real-time monitoring of crop phenology in
the midwestern United States using VIIRS observations. Remote Sensing, 10, 1640. https://doi.
org/10.3390/rs10101540.
Lobell, D. B., Thau, D., Seifert, C., Engle, E., & Little, B. (2015). A scalable satellite-based crop
yield mapper. Remote Sensing of Environment, 164, 324–333.
Mu, Q., Heinsch, F. A., Zhao, M., & Running, S. W. (2007). Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment, 111, 519–536. https://doi.org/10.1016/j.rse.2007.04.015.
Myneni, R., Hoffman, S., Knyazikhin, Y., Privette, J., Glassy, J., Tian, Y., Wang, Y., Song, X.,
Zhang, Y., Smith, G., et al. (2002). Global products of vegetation leaf area and fraction absorbed
PAR from year one of MODIS data. Remote Sensing of Environment, 83, 214–231.
Richardson, A. D. (2018). Tracking seasonal rhythms of plants in diverse ecosystems with digital
camera imagery. New Phytologist, 222, 1742–1750. https://doi.org/10.1111/nph.15591.
Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C.,
Helder, D., Irons, J. R., Johnson, D. M., Kennedy, R., et al. (2014). Landsat-8: Science and
product vision for terrestrial global change research. Remote Sensing of Environment, 145,
154–172.
Sakamoto, T., Wardlow, B. D., Gitelson, A. A., Verma, S. B., Suyker, A. E., & Arkebauer, T. J.
(2010). A two-step filtering approach for detecting maize and soybean phenology with timeseries MODIS data. Remote Sensing of Environment, 114, 2146–2159.
Schaaf, C. B., Gao, F., Strahler, A. H., Lucht, W., Li, X., Tsang, T., Strugnell, N. C., Zhang, X., Jin,
Y., Muller, J. P., et al. (2002). First operational BRDF, albedo and nadir reflectance products
from MODIS. Remote Sensing of Environment, 83, 135–148.
Tucker, C. J., Holben, B. N., Elgin, J. H., & McMurtry, J. E., III. (1980). Relationship of spectral
data to grain yield variation. Photogrammetric Engineering and Remote Sensing, 46, 657–666.
Vermote, E. F., El Saleous, N. Z., & Justice, C. O. (2002). Atmospheric correction of MODIS data
in the visible to middle infrared: First results. Remote Sensing of Environment, 83, 97–111.
Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance
of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment, 185,
46–56.
Walthall, C. L., Hatfield, J., Backlund, P., et al. (2012). Climate change and agriculture in the
United States: Effects and adaptation (USDA Technical Bulletin 1935). Washington, DC:
USDA. 186 pages.
Wan, Z., Zhang, Y., Zhang, Q., & Li, Z.-L. (2002). Validation of the land-surface temperature
products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Remote
Sensing of Environment, 83, 163–180.
Woodcock, C. E., Allen, R., Anderson, M., Belward, A., Bindschadler, R., Cohen, W., Gao, F.,
Goward, S. N., Helder, D., Helmer, E., et al. (2008). Free access to Landsat imagery. Science,
320, 1011.
2 Remote Sensing for Agriculture
23
30-m reference time series. Journal of Integrative Agriculture, 19(7), 1897–1911.
Houborg, R., & McCabe, M. F. (2018). A Cubesat enabled Spatio-temporal enhancement method
(CESTEM) utilizing planet, Landsat and MODIS data. Remote Sensing of Environment, 209,
211–226. https://doi.org/10.1016/j.rse.2018.02.067.
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of
the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing
of Environment, 83(1), 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2.
Jonsson, P., & Eklundh, L. (2004). TIMESAT—A program for analysing time-series of satellite
sensor data. Computational Geosciences, 30, 833–845.
Justice, C. O., Townshend, J. R. G., Vermote, E. F., Masuoka, E., Wolfe, R. E., Saleous, N., Roy,
D. P., & Morisette, J. (2002). An overview of MODIS land data processing and product status.
Remote Sensing of Environment, 83, 3–15.
Liu, L., Zhang, X., Yu, Y., Gao, F., & Yang, Z. (2018). Real-time monitoring of crop phenology in
the midwestern United States using VIIRS observations. Remote Sensing, 10, 1640. https://doi.
org/10.3390/rs10101540.
Lobell, D. B., Thau, D., Seifert, C., Engle, E., & Little, B. (2015). A scalable satellite-based crop
yield mapper. Remote Sensing of Environment, 164, 324–333.
Mu, Q., Heinsch, F. A., Zhao, M., & Running, S. W. (2007). Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment, 111, 519–536. https://doi.org/10.1016/j.rse.2007.04.015.
Myneni, R., Hoffman, S., Knyazikhin, Y., Privette, J., Glassy, J., Tian, Y., Wang, Y., Song, X.,
Zhang, Y., Smith, G., et al. (2002). Global products of vegetation leaf area and fraction absorbed
PAR from year one of MODIS data. Remote Sensing of Environment, 83, 214–231.
Richardson, A. D. (2018). Tracking seasonal rhythms of plants in diverse ecosystems with digital
camera imagery. New Phytologist, 222, 1742–1750. https://doi.org/10.1111/nph.15591.
Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C.,
Helder, D., Irons, J. R., Johnson, D. M., Kennedy, R., et al. (2014). Landsat-8: Science and
product vision for terrestrial global change research. Remote Sensing of Environment, 145,
154–172.
Sakamoto, T., Wardlow, B. D., Gitelson, A. A., Verma, S. B., Suyker, A. E., & Arkebauer, T. J.
(2010). A two-step filtering approach for detecting maize and soybean phenology with timeseries MODIS data. Remote Sensing of Environment, 114, 2146–2159.
Schaaf, C. B., Gao, F., Strahler, A. H., Lucht, W., Li, X., Tsang, T., Strugnell, N. C., Zhang, X., Jin,
Y., Muller, J. P., et al. (2002). First operational BRDF, albedo and nadir reflectance products
from MODIS. Remote Sensing of Environment, 83, 135–148.
Tucker, C. J., Holben, B. N., Elgin, J. H., & McMurtry, J. E., III. (1980). Relationship of spectral
data to grain yield variation. Photogrammetric Engineering and Remote Sensing, 46, 657–666.
Vermote, E. F., El Saleous, N. Z., & Justice, C. O. (2002). Atmospheric correction of MODIS data
in the visible to middle infrared: First results. Remote Sensing of Environment, 83, 97–111.
Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance
of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment, 185,
46–56.
Walthall, C. L., Hatfield, J., Backlund, P., et al. (2012). Climate change and agriculture in the
United States: Effects and adaptation (USDA Technical Bulletin 1935). Washington, DC:
USDA. 186 pages.
Wan, Z., Zhang, Y., Zhang, Q., & Li, Z.-L. (2002). Validation of the land-surface temperature
products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Remote
Sensing of Environment, 83, 163–180.
Woodcock, C. E., Allen, R., Anderson, M., Belward, A., Bindschadler, R., Cohen, W., Gao, F.,
Goward, S. N., Helder, D., Helmer, E., et al. (2008). Free access to Landsat imagery. Science,
320, 1011.
2 Remote Sensing for Agriculture
23