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A scalable satellite-based crop yield mapper
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High-Resolution Global Maps of 21st-Century Forest Cover Change
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., et al. (2013) · Science · doi:10.1126/science.1244693 · Cited by 11,704
Self-citationAPSIM – evolution towards a new generation of agricultural systems simulation
Holzworth, D. P., Huth, N. I., deVoil, P. G., Zurcher, E. J., Herrmann, N. I., McLean, G., Chenu, K., van Oosterom, E. J., Snow, V., Murphy, C., et al. (2014) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2014.07.009 · Cited by 1,756
Boryan, C., Yang, Z., Mueller, R., & Craig, M. (2011) · Geocarto International · doi:10.1080/10106049.2011.562309 · Cited by 1,212
The critical role of extreme heat for maize production in the United States
Lobell, D. B., Hammer, G. L., McLean, G., Messina, C., Roberts, M. J., & Schlenker, W. (2013) · Nature Climate Change · doi:10.1038/nclimate1832 · Cited by 1,026
Self-citationArchontoulis, S. V., Miguez, F. E., & Moore, K. J. (2014) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2014.04.009 · Cited by 130
Research works printed but not linked: 23
- Baez-Gonzalez, 2002, Using satellite and field data with crop growth modeling to monitor and estimate corn yield in Mexico, Crop Science, 42, 1943, 10.2135/cropsci2002.1943 · DOI
- Becker-Reshef, 2010, Monitoring global croplands with coarse resolution earth observations: The global agriculture monitoring (GLAM) project, Remote Sensing, 2, 1589, 10.3390/rs2061589 · DOI · Paper page
- Clevers, 1997, A simplified approach for yield prediction of sugar beet based on optical remote sensing data, Remote Sensing of Environment, 61, 221, 10.1016/S0034-4257(97)00004-7 · doi:10.1016/s0034-4257(97)00004-7
- Daly, 2008, Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States, nternational journal of Climatology, 28, 2031, 10.1002/joc.1688 · DOI · Paper page
- Fisher, 1921, On the “probable error” of a coefficient of correlation deduced from a small sample, Metron, 1, 3
- Gillies, 1997, A verification of the “triangle” method for obtaining surface soil water content and energy fluxes from remote measurements of the normalized difference vegetation index (NDVI) and surface radiant temperature, International Journal of Remote Sensing, 18, 3145, 10.1080/014311697217026 · DOI
- Gitelson, 2003, Remote estimation of leaf area index and green leaf biomass in maize canopies, Geophysical Research Letters, 30, 1248, 10.1029/2002GL016450 · DOI · Paper page
- Hammer, 2009, Can changes in canopy and/or root system architecture explain historical maize yield trends in the US corn belt?, Crop Science, 49, 299, 10.2135/cropsci2008.03.0152 · DOI · Paper page
- Inoue, 2014, Capability of C-band backscattering coefficients from high-resolution satellite SAR sensors to assess biophysical variables in paddy rice, Remote Sensing of Environment, 140, 257, 10.1016/j.rse.2013.09.001 · DOI · Paper page
- Lobell, 2013, The use of satellite data for crop yield gap analysis, Field Crops Research, 143, 56, 10.1016/j.fcr.2012.08.008 · DOI · Paper page
- Lobell, 2003, Remote sensing of regional crop production in the Yaqui Valley, Mexico: Estimates and uncertainties, Agriculture, Ecosystems and Environment, 94, 205, 10.1016/S0167-8809(02)00021-X · DOI · Paper page
- Lobell, 2005, Combining field surveys, remote sensing, and regression trees to understand yield variations in an irrigated wheat landscape, Agronomy Journal, 97, 241, 10.2134/agronj2005.0241a · DOI · Paper page
- Lobell, 2014, Greater sensitivity to drought accompanies maize yield increase in the U.S. Midwest, Science, 344, 516, 10.1126/science.1251423 · DOI · Paper page
- Macdonald, 1980, Global crop forecasting, Science, 208, 670, 10.1126/science.208.4445.670 · DOI
- Masek, 2006, A Landsat surface reflectance dataset for North America, 1990–2000, Geoscience and Remote Sensing Letters IEEE, 3, 68, 10.1109/LGRS.2005.857030 · DOI · Paper page
- Moulin, 1998, Combining agricultural crop models and satellite observations: From field to regional scales, International Journal of Remote Sensing, 19, 1021, 10.1080/014311698215586 · DOI · Paper page
- Nguy-Robertson, 2012, Green leaf area index estimation in maize and soybean: Combining vegetation indices to achieve maximal sensitivity, Agronomy Journal, 104, 1336, 10.2134/agronj2012.0065 · DOI · Paper page
- Otkin, 2014, Examining the Relationship between Drought Development and Rapid Changes in the Evaporative Stress Index, Journal of Hydrometeorology, 15, 938, 10.1175/JHM-D-13-0110.1 · DOI · Paper page
- Sehgal, 2005, Farm-level yield mapping for precision crop management by linking remote sensing inputs and a crop simulation model, Journal of the Indian Society of Remote Sensing, 33, 131, 10.1007/BF02990002 · doi:10.1007/bf02990002
- Shanahan, 2001, Use of remote-sensing imagery to estimate corn grain yield, Agronomy Journal, 93, 583, 10.2134/agronj2001.933583x · DOI · Paper page
- Sibley, 2014, Testing remote sensing approaches for assessing yield variability among maize fields, Agronomy Journal, 106, 24, 10.2134/agronj2013.0314 · DOI
- Viña, 2011, Comparison of different vegetation indices for the remote assessment of green leaf area index of crops, Remote Sensing of Environment, 115, 3468, 10.1016/j.rse.2011.08.010 · DOI · Paper page
- Wiegand, 1990, Multisite analyses of spectral biophysical data for corn, Remote Sensing of Environment, 33, 1, 10.1016/0034-4257(90)90051-M · doi:10.1016/0034-4257(90)90051-m
Cited by
Google Earth Engine: Planetary-scale geospatial analysis for everyone
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.06.031 · Cited by 14,719
Self-citationRemote sensing for agricultural applications: A meta-review
Weiss, M., Jacob, F., & Duveiller, G. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111402 · Cited by 1,877
Zhang, Y., Kong, D., Gan, R., Chiew, F. H. S., McVicar, T. R., Zhang, Q., & Yang, Y. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.12.031 · Cited by 963
Cai, Y., Guan, K., Peng, J., Wang, S., Seifert, C., Wardlow, B., & Li, Z. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.02.045 · Cited by 540
Self-citationSchwalbert, R. A., Amado, T., Corassa, G., Pott, L. P., Prasad, P. V. V., & Ciampitti, I. A. (2020) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2019.107886 · Cited by 403
Metadata
- Authors
- Lobell, D. B., Thau, D., Seifert, C., Engle, E., & Little, B.
- Venue
- Remote Sensing of Environment
- Publisher
- Elsevier BV
- Published
- 2015-07
- Volume
- 164
- Pages
- 324-333
- ISSN
- 0034-4257 (ISSN-L)
- OpenAlex
- W612661449
- Cited by
- 602
- References
- 5
- FWCI
- 22.73
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Funders
- Terman Fellowship, Earth Engine Research Award
- Betsy and Jesse Fink Foundation
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- The paper
- Lobell, D. B., Thau, D., Seifert, C., Engle, E., & Little, B. (2015). A scalable satellite-based crop yield mapper. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2015.04.021
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
