References
Anderson, M. C., Kustas, W. P., & Norman, J. M. (2007). Upscaling flux observations from local to
continental scales using thermal remote sensing. Agronomy Journal, 99, 240–254.
Anderson, M. C., Kustas, W. P., Alfieri, J. G., Gao, F., Hain, C., Prueger, J. H., Evett, S. R.,
Colaizzi, P. D., Howell, T. A., & Chaves, J. L. (2012). Mapping daily evapotranspiration at
Landsat spatial scales during the BEAREX’08 field campaign. Advances in Water Resources,
50, 162–177. https://doi.org/10.1016/j.advwatres.2012.06.005.
Anderson, M. C., Zolin, C. A., Sentelhas, P. C., Hain, C. R., Semmens, K., Tugrul Yilmaz, M., Gao,
F., Otkin, J. A., & Tetrault, R. (2016). The Evaporative Stress Index as an indicator of
agricultural drought in Brazil: An assessment based on crop yield impacts. Remote Sensing of
Environment, 174, 82–99.
Bolton, D. K., Gray, J. M., Melaas, E. K., Moon, M., Eklundh, L., & Friedl, M. A. (2020).
Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery.
Remote Sensing of Environment, 140, 111685. https://doi.org/10.1016/j.rse.2020.111685.
Boryan, C., Yang, Z., Mueller, R., & Craig, M. (2011). Monitoring US agriculture: The US
Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer
program. Geocarto International, 26, 341–358.
Calvet, J.-C., Wigneron, J.-P., Walker, J. P., Karbou, F., Chanzy, A., & Albergel, C. (2011).
Sensitivity of passive microwave observations to soil moisture and vegetation water content:
L-band to W-band. IEEE Transactions on Geoscience and Remote Sensing, 49(4), 1190–1199.
Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote, E. F., Roger, J.-C., Skakun, S., & Justice,
C. O. (2018). The harmonized Landsat and Sentinel-2 surface reflectance data set. Remote
Sensing of Environment, 219, 145–161. https://doi.org/10.1016/j.rse.2018.09.002.
Das, N., Entekhabi, D., Dunbar, R. S., Kim, S., Yueh, S., Colliander, A., Neill, P. E. O., & Jackson,
T. (2018). SMAP/Sentinel-1 L2 radiometer/radar 30-second scene 3 km EASE-grid soil moisture, version 2. NASA National Snow and Ice Data Center Distributed Active Archive Center.
https://doi.org/10.5067/KE1CSVXMI95Y.
Dwyer, J., Roy, D., Sauer, B., Jenkerson, C., Zhang, H., & Lymburner, L. (2018). Analysis ready
data: Enabling analysis of the Landsat archive. Remote Sensing, 10, 1363. https://doi.org/10.
3390/rs10091363.
Gao, F., Masek, J., Schwaller, M., & Hall, F. (2006). On the blending of the Landsat and MODIS
surface reflectance: Predict daily Landsat surface reflectance. IEEE Transactions on Geoscience
and Remote Sensing, 44, 2207–2218.
Gao, F., Anderson, M. C., Zhang, X., Yang, Z., Alfieri, J. G., Kustas, W. P., Mueller, R., Johnson,
D., & Prueger, J. H. (2017). Toward mapping crop progress at field scales through fusion of
Landsat and MODIS imagery. Remote Sensing of Environment, 188, 9–25.
Gao, F., Anderson, M., Daughtry, C., & Johnson, D. (2018). Assessing variability of corn and
soybean yields in central Iowa using high spatiotemporal resolution multi-satellite imagery.
Remote Sensing, 10, 1489. https://doi.org/10.3390/rs10091489.
Gao, F., Anderson, M. C., Daughtry, C. S., Karnieli, A., Hively, W. D., & Kustas, W. P. (2020a). A
within-season approach for detecting early growth stages in corn and soybean using high
temporal and spatial resolution imagery. Remote Sensing of Environment, 242, 111752.
https://doi.org/10.1016/j.rse.2020.111752.
Gao, F., Anderson, M., & Hively, W. D. (2020b). Detecting cover crop end-of-season termination
within the season using VENμS and Sentinel-2 satellite imagery. Remote Sensing, 12, 3524.
https://doi.org/10.3390/rs12213524.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google
Earth engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment,
202, 18–27.
22
F. Gao
Anderson, M. C., Kustas, W. P., & Norman, J. M. (2007). Upscaling flux observations from local to
continental scales using thermal remote sensing. Agronomy Journal, 99, 240–254.
Anderson, M. C., Kustas, W. P., Alfieri, J. G., Gao, F., Hain, C., Prueger, J. H., Evett, S. R.,
Colaizzi, P. D., Howell, T. A., & Chaves, J. L. (2012). Mapping daily evapotranspiration at
Landsat spatial scales during the BEAREX’08 field campaign. Advances in Water Resources,
50, 162–177. https://doi.org/10.1016/j.advwatres.2012.06.005.
Anderson, M. C., Zolin, C. A., Sentelhas, P. C., Hain, C. R., Semmens, K., Tugrul Yilmaz, M., Gao,
F., Otkin, J. A., & Tetrault, R. (2016). The Evaporative Stress Index as an indicator of
agricultural drought in Brazil: An assessment based on crop yield impacts. Remote Sensing of
Environment, 174, 82–99.
Bolton, D. K., Gray, J. M., Melaas, E. K., Moon, M., Eklundh, L., & Friedl, M. A. (2020).
Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery.
Remote Sensing of Environment, 140, 111685. https://doi.org/10.1016/j.rse.2020.111685.
Boryan, C., Yang, Z., Mueller, R., & Craig, M. (2011). Monitoring US agriculture: The US
Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer
program. Geocarto International, 26, 341–358.
Calvet, J.-C., Wigneron, J.-P., Walker, J. P., Karbou, F., Chanzy, A., & Albergel, C. (2011).
Sensitivity of passive microwave observations to soil moisture and vegetation water content:
L-band to W-band. IEEE Transactions on Geoscience and Remote Sensing, 49(4), 1190–1199.
Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote, E. F., Roger, J.-C., Skakun, S., & Justice,
C. O. (2018). The harmonized Landsat and Sentinel-2 surface reflectance data set. Remote
Sensing of Environment, 219, 145–161. https://doi.org/10.1016/j.rse.2018.09.002.
Das, N., Entekhabi, D., Dunbar, R. S., Kim, S., Yueh, S., Colliander, A., Neill, P. E. O., & Jackson,
T. (2018). SMAP/Sentinel-1 L2 radiometer/radar 30-second scene 3 km EASE-grid soil moisture, version 2. NASA National Snow and Ice Data Center Distributed Active Archive Center.
https://doi.org/10.5067/KE1CSVXMI95Y.
Dwyer, J., Roy, D., Sauer, B., Jenkerson, C., Zhang, H., & Lymburner, L. (2018). Analysis ready
data: Enabling analysis of the Landsat archive. Remote Sensing, 10, 1363. https://doi.org/10.
3390/rs10091363.
Gao, F., Masek, J., Schwaller, M., & Hall, F. (2006). On the blending of the Landsat and MODIS
surface reflectance: Predict daily Landsat surface reflectance. IEEE Transactions on Geoscience
and Remote Sensing, 44, 2207–2218.
Gao, F., Anderson, M. C., Zhang, X., Yang, Z., Alfieri, J. G., Kustas, W. P., Mueller, R., Johnson,
D., & Prueger, J. H. (2017). Toward mapping crop progress at field scales through fusion of
Landsat and MODIS imagery. Remote Sensing of Environment, 188, 9–25.
Gao, F., Anderson, M., Daughtry, C., & Johnson, D. (2018). Assessing variability of corn and
soybean yields in central Iowa using high spatiotemporal resolution multi-satellite imagery.
Remote Sensing, 10, 1489. https://doi.org/10.3390/rs10091489.
Gao, F., Anderson, M. C., Daughtry, C. S., Karnieli, A., Hively, W. D., & Kustas, W. P. (2020a). A
within-season approach for detecting early growth stages in corn and soybean using high
temporal and spatial resolution imagery. Remote Sensing of Environment, 242, 111752.
https://doi.org/10.1016/j.rse.2020.111752.
Gao, F., Anderson, M., & Hively, W. D. (2020b). Detecting cover crop end-of-season termination
within the season using VENμS and Sentinel-2 satellite imagery. Remote Sensing, 12, 3524.
https://doi.org/10.3390/rs12213524.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google
Earth engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment,
202, 18–27.
22
F. Gao