USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better than Simple Trend Analyses?
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.3. Methods
2.3.1. Year Trend
2.3.2. Peak NDVI
2.3.3. Accumulated NDVI
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- World Bank. Global Strategy to Improve Agricultural and Rural Statistics; Report Number 56719-GLB; World Bank: Washington, DC, USA, 2011; Available online: http://www.fao.org/3/am082e/am082e00.pdf (accessed on 9 March 2021).
- Rosegrant, M.W.; Cline, S.A. Global food security: Challenges and policies. Science 2003, 302, 1917–1919. [Google Scholar] [CrossRef] [Green Version]
- Lobell, D.B.; Cassman, K.G.; Field, C.B. Crop Yield Gaps: Their Importance, Magnitudes, and Causes. Annu. Rev. Environ. Resour. 2009, 34, 179–204. [Google Scholar] [CrossRef] [Green Version]
- Carletto, C.; Jolliffe, D.; Banerjee, R. From Tragedy to Renaissance: Improving Agricultural Data for Better Policies. J. Dev. Stud. 2015, 51, 133–148. [Google Scholar] [CrossRef]
- US Department of Agriculture, National Agricultural Statistics Service. The Yield Forecasting Program of NASS, SMB Staff Report Number SMB 12-01. 2012. Available online: https://www.nass.usda.gov/Education_and_Outreach/Understanding_Statistics/Yield_Forecasting_Program.pdf (accessed on 25 March 2021).
- US Department of Agriculture, National Agricultural Statistics Service. Crop Production, Annual Summary; USDA-NASS: Washington, DC, USA, 2020; Available online: https://usda.library.cornell.edu/concern/publications/k3569432s (accessed on 17 March 2021).
- Vogel, F.A.; Bange, G.A. Understanding USDA Crop Forecasts. National Agricultural Statistics Service and World Agricultural Outlook Board, Office of the Chief Economist, US Department of Agriculture. 1999. Miscellaneous Publication No. 1554. Available online: https://www.nass.usda.gov/Education_and_Outreach/Understanding_Statistics/pub1554.pdf (accessed on 25 March 2021).
- US Department of Agriculture, World Agricultural Outlook Board. World Agricultural Supply and Demand Estimates (WASDE) Report; USDA-NASS: Washington, DC, USA, 2021. Available online: https://www.usda.gov/oce/commodity/wasde (accessed on 21 March 2021).
- Van Diepen, C.A.; Wolf, J.; Van Keulen, H.; Rappoldt, C. WOFOST: A simulation model of crop production. Soil Use Manag. 1989, 5, 16–24. [Google Scholar] [CrossRef]
- Jones, J.W.; Hoogenboom, G.; Porter, C.H.; Boote, K.J.; Batchelor, W.D.; Hunt, L.A.; Wilkens, P.W.; Singh, U.; Gijsman, A.J.; Ritchie, J.T. The DSSAT cropping system model. Eur. J. Agron. 2003, 18, 235–265. [Google Scholar] [CrossRef]
- 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. APSIM—Evolution towards a New Generation of Agricultural Systems Simulation. Environ. Modell. Softw. 2014, 62, 327–350. [Google Scholar] [CrossRef]
- Lambin, E.F.; Cashman, P.; Moody, A.; Parkhurst, B.H.; Pax, M.H.; Schaaf, C.B. Agricultural production monitoring in the Sahel using remote sensing: Present possibilities and research needs. J. Environ. Manag. 1993, 38, 301–322. [Google Scholar] [CrossRef]
- Fang, H.; Liang, S.; Hoogenboom, G. Integration of MODIS LAI and vegetation index products with the CSM-CERES-Maize model for corn yield estimation. Int. J. Remote Sens. 2011, 32, 1039–1065. [Google Scholar] [CrossRef]
- Ines, A.V.M.; Das, N.N.; Hansen, J.W.; Njoku, E.G. Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction. Remote Sens. Environ. 2013, 138, 149–164. [Google Scholar] [CrossRef] [Green Version]
- Huang, J.; Tian, L.; Liang, S.; Ma, H.; Becker-Reshef, I.; Huang, Y.; Su, W.; Zhang, X.; Zhu, D.; Wu, W. Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model. Agric. For. Meteorol. 2015, 204, 106–121. [Google Scholar] [CrossRef] [Green Version]
- Yang, H.S.; Dobermann, A.; Lindquist, J.L.; Walters, D.T.; Arkebauer, T.J.; Cassman, K.G. Hybrid-Maize—A maize simulation model that combines two crop modeling approaches. Field Crops Res. 2004, 87, 131–154. [Google Scholar] [CrossRef] [Green Version]
- Jin, Z.; Zhuang, Q.; Tan, Z.; Dukes, J.S.; Zheng, B.; Melillo, J.M. Do maize models capture the impacts of heat and drought stresses on yield? Using algorithm ensembles to identify successful approaches. Glob. Chang. Biol. 2016, 22, 3112–3126. [Google Scholar] [CrossRef] [PubMed]
- Li, Y.; Guan, K.; Schnitkey, G.D.; DeLucia, E.; Peng, B. Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States. Glob. Chang. Biol. 2019, 25, 2325–2337. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Jiang, Z.; Liu, C.; Ganapathysubramanian, B.; Hayes, D.; Sarkar, S. Predicting county-scale maize yields with publicly available data. Sci. Rep. 2020, 10, 14957. [Google Scholar] [CrossRef] [PubMed]
- Shahhosseini, M.; Hu, G.; Archontoulis, S.V. Forecasting corn yield with machine learning ensembles. Front. Plant Sci. 2020, 11, 1120. [Google Scholar] [CrossRef] [PubMed]
- Chipanshi, A.; Zhang, Y.; Kouadio, L.; Newlands, N.; Davidson, A.; Hill, H.; Warren, R.; Qian, B.; Daneshfar, B.; Bedard, F.; et al. Evaluation of the Integrated Canadian Crop Yield Forecaster (ICCYF) model for in-season prediction of crop yield across the Canadian agricultural landscape. Agric. For. Meteorol. 2015, 206, 137–150. [Google Scholar] [CrossRef] [Green Version]
- López-Lozano, R.; Duveiller, G.; Seguini, L.; Meroni, M.; García-Condado, S.; Hooker, J.; Leo, O.; Baruth, B. Towards regional grain yield forecasting with 1km-resolution EO biophysical products: Strengths and limitations at pan-European level. Agric. For. Meteorol. 2015, 206, 12–32. [Google Scholar] [CrossRef]
- Becker-Reshef, I.; Justice, C.; Sullivan, M.; Vermote, E.; Tucker, C.; Anyamba, A.; Small, J.; Pak, E.; Masuoka, E.; Schmaltz, J.; et al. Monitoring Global Croplands with Coarse Resolution Earth Observations: The Global Agriculture Monitoring (GLAM). Project. Remote Sens. 2010, 2, 1589. [Google Scholar] [CrossRef] [Green Version]
- Tucker, C.J.; Holben, B.N.; Elgin, J.H.; McMurtrey, J.E. Relationship of spectral data to grain yield variation. Photogramm. Eng. Remote Sens. 1980, 46, 657–666. [Google Scholar]
- Hatfield, J.L. Remote sensing estimators of potential and actual crop yield. Remote Sens. Environ. 1983, 13, 301–311. [Google Scholar] [CrossRef]
- Tucker, C.J.; Sellers, P.J. Satellite remote sensing of primary production. Int. J. Remote Sens. 1986, 7, 1395–1416. [Google Scholar] [CrossRef]
- Bartholome, E. Radiometric measurements and crop yield forecasting. Some observations over millet and sorghum experimental plots in Mali. Int. J. Remote Sens. 1988, 9, 1539–1552. [Google Scholar] [CrossRef]
- Johnson, D.M. A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products. Int. J. Appl. Earth Obs. 2016, 52, 65–81. [Google Scholar] [CrossRef] [Green Version]
- Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 1979, 8, 127–150. [Google Scholar] [CrossRef] [Green Version]
- Rasmussen, M.S. Assessment of millet yields and production in northern Burkina Faso using integrated NDVI from the AVHRR. Int. J. Remote Sens. 1992, 13, 3431–3442. [Google Scholar] [CrossRef]
- Groten, S.M.E. NDVI—Crop monitoring and early yield assessment of Burkina Faso. Int. J. Remote Sens. 1993, 14, 1495–1515. [Google Scholar] [CrossRef]
- Quarmby, N.; Mines, M.; Hindle, T.; Silleos, N. The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction. Int. J. Remote Sens. 1993, 14, 199–210. [Google Scholar] [CrossRef]
- Benedetti, R.; Rossini, P. On the use of NDVI profiles as a tool for agricultural statistics: The case study of wheat yield estimate and forecast in Emilia Romagna. Remote Sens. Environ. 1993, 45, 311–326. [Google Scholar] [CrossRef]
- Hayes, M.J.; Decker, W.L. Using NOAA AVHRR data to estimate maize production in the United States Corn Belt. Int. J. Remote Sens. 1996, 17, 3189–3200. [Google Scholar] [CrossRef]
- Maselli, F.; Rembold, F. Integration of LAC and GAC NDVI data to improve vegetation monitoring in semi-arid environments. Int. J. Remote Sens. 2002, 23, 2475–2488. [Google Scholar] [CrossRef]
- Anyamba, A.; Tucker, C.J. Historical Perspectives on AVHRR NDVI and Vegetation Drought Monitoring. In Remote Sensing for Drought: Innovative Monitoring Approaches; CRC Press/Taylor & Francis Publishers: New York, NY, USA, 2012; pp. 32–49. [Google Scholar]
- Holben, B.N. Characteristics of maximum-value composite images from temporal AVHRR data. Int. J. Remote Sens. 1986, 7, 1417–1434. [Google Scholar] [CrossRef]
- Doraiswamy, P.C.; Sinclair, T.R.; Hollinger, S.; Akhmedov, B.; Stern, A.; Prueger, J. Application of MODIS derived parameters for regional crop yield assessment. Remote Sens. Environ. 2005, 97, 192–202. [Google Scholar] [CrossRef]
- Reeves, M.C.; Zhao, M.; Running, S.W. Usefulness of limits of MODIS GPP for estimating wheat yield. Int. J. Remote Sens. 2005, 26, 1403–1421. [Google Scholar] [CrossRef]
- Labus, M.P.; Nielsen, G.A.; Lawrence, R.L.; Engel, R.; Long, D.S. Wheat yield estimates using multi-temporal NDVI satellite imagery. Int. J. Remote Sens. 2002, 23, 4169–4180. [Google Scholar] [CrossRef]
- Domenikiotis, C.; Spiliotopoulus, M.; Tsiros, E.; Dalezios, N.R. Early cotton yield assessment by the use of the NOAA/AVHRR derived vegetation condition index (VCI) in Greece. Int. J. Remote Sens. 2004, 25, 2807–2819. [Google Scholar] [CrossRef]
- Ferencz, C.; Bognár, P.; Lichtenberger, J.; Hamar, D.; Tarcsai, G.; Timár, G.; Molnár, G.; Pásztor, S.; Steinbach, P.; Székely, B.; et al. Crop yield estimation by satellite remote sensing. Int. J. Remote Sens. 2004, 25, 4113–4149. [Google Scholar] [CrossRef]
- Salazar, L.; Kogan, F.; Roytman, L. Use of remote sensing data for estimation of winter wheat yield in the United States. Int. J. Remote Sens. 2007, 28, 3795–3811. [Google Scholar] [CrossRef]
- Kogan, F.; Salazar, L.; Roytman, L. Forecasting crop production using satellite-based vegetation health indices in Kansas, USA. Int. J. Remote Sens. 2012, 33, 2798–2814. [Google Scholar] [CrossRef]
- Zhang, X.; Zhang, Q. Monitoring interannual variation in global crop yield using long-term AVHRR and MODIS observations. ISPRS J. Photogramm. Remote Sens. 2016, 114, 191–205. [Google Scholar] [CrossRef] [PubMed]
- Becker-Reshef, I.; Vermote, E.; Lindeman, M.; Justice, C. A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sens. Environ. 2010, 114, 1312–1323. [Google Scholar] [CrossRef]
- Mkhabela, M.S.; Bullock, P.; Raj, S.; Wang, S.; Yang, Y. Crop yield forecasting on the Canadian prairies using MODIS NDVI data. Agric. For. Meteorol. 2011, 151, 385–393. [Google Scholar] [CrossRef]
- Kouadio, L.; Newlands, N.K.; Davidson, A.; Zhang, Y.; Chipanshi, A. Assessing the performance of MODIS NDVI and EVI for seasonal crop yield forecasting at the ecodistrict scale. Remote Sens. 2014, 6, 10193–10214. [Google Scholar] [CrossRef] [Green Version]
- Shao, Y.; Campbell, J.B.; Taff, G.N.; Zheng, B. An analysis of cropland mask choice and ancillary data for annual corn yield forecasting using MODIS data. Int. J. Appl. Earth Obs. Geoinf. 2015, 38, 78–87. [Google Scholar] [CrossRef]
- Johnson, M.D.; Hsieh, W.W.; Cannon, A.J.; Davidson, A.; Bédard, F. Crop yield forecasting on the Canadian prairies by remotely sensed vegetation indices and machine learning methods. Agric. For. Meteorol. 2016, 218–219, 74–84. [Google Scholar] [CrossRef]
- Skakun, S.; Franch, B.; Vermote, E.; Roger, J.-C.; Becker-Reshef, I.; Justice, C.; Kussul, N. Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information and a Gaussian mixture model. Remote Sens. Environ. 2017, 195, 244–258. [Google Scholar] [CrossRef]
- Petersen, L.K. Real-time prediction of crop yields from MODIS relative vegetation health: A continent-wide analysis of Africa. Remote Sens. 2018, 10, 1726. [Google Scholar] [CrossRef] [Green Version]
- Funk, C.; Budde, M.E. Phenologically tuned MODIS NDVI-based production anomaly estimates for Zimbabwe. Remote Sens. Environ. 2009, 113, 115–125. [Google Scholar] [CrossRef]
- Huang, J.; Han, D. Meta-analysis of influential factors on crop yield estimation by remote sensing. Int. J. Remote Sens. 2014, 35, 2267–2295. [Google Scholar] [CrossRef]
- Weiss, M.; Jacob, F.; Duveiller, G. Remote sensing for agricultural applications: A meta-review. Remote Sens. Environ. 2020, 236, 111402. [Google Scholar] [CrossRef]
- Bolton, D.K.; Friedl, M.A. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agric. For. Meteorol. 2013, 173, 74–84. [Google Scholar] [CrossRef]
- Sakamoto, T.; Gitelson, A.A.; Arkebauer, T.J. MODIS-based corn grain yield estimation model incorporating crop phenology information. Remote Sens. Environ. 2013, 131, 215–231. [Google Scholar] [CrossRef]
- Johnson, D.M. An assessment of pre-and within-season remotely sensed variables for forecasting corn and soybean yields in the United States. Remote Sens. Environ. 2014, 141, 116–128. [Google Scholar] [CrossRef]
- Kouadio, L.; Duveiller, G.; Djaby, B.; El Jarroudi, M.; Defourny, P.; Tychon, B. Estimating regional wheat yield from the shape of decreasing curves of green area index temporal profiles retrieved from MODIS data. Int. J. Appl. Earth Obs. 2012, 18, 111–118. [Google Scholar] [CrossRef] [Green Version]
- Franch, B.; Vermote, E.F.; Skakun, S.; Roger, J.C.; Becker-Reshef, I.; Murphy, E.; Justice, C. Remote sensing based yield monitoring: Application to winter wheat in United States and Ukraine. Int. J. Appl. Earth Obs. 2019, 76, 112–127. [Google Scholar] [CrossRef]
- Prasad, A.K.; Chai, L.; Singh, R.P.; Kafatos, M. Crop yield estimation model for Iowa using remote sensing and surface parameters. Int. J. Appl. Earth Obs. Geoinf. 2006, 8, 26–33. [Google Scholar] [CrossRef]
- Kamir, E.; Waldner, F.; Hochman, Z. Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods. ISPRS J. Photogramm. 2020, 160, 124–135. [Google Scholar] [CrossRef]
- Schwalbert, R.A.; Amado, T.; Corassa, G.; Pott, L.P.; Prasad, P.V.V.; Ciampitti, I.A. Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil. Agric. For. Meteorol. 2020, 284, 107886. [Google Scholar] [CrossRef]
- Wolanin, A.; Mateo-Garciá, G.; Camps-Valls, G.; Gómez-Chova, L.; Meroni, M.; Duveiller, G.; Liangzhi, Y.; Guanter, L. Estimating and understanding crop yields with explainable deep learning in the Indian Wheat Belt. Environ. Res. Lett. 2020, 15, 024019. [Google Scholar] [CrossRef]
- US Department of Agriculture, National Agricultural Statistics Service. Quick Stats; USDA-NASS: Washington, DC, USA, 2021. Available online: http://www.nass.usda.gov/Quick Stats/ (accessed on 17 March 2021).
- US Department of Agriculture, Foreign Agricultural Service. Global Agricultural Monitoring. 2021. Available online: https://glam1.gsfc.nasa.gov/ (accessed on 17 March 2021).
- Van Leeuwen, W.J.D.; Huete, A.R.; Laing, T.W. MODIS vegetation index compositing approach: A prototype with AVHRR data. Remote Sens. Environ. 1999, 69, 264–280. [Google Scholar] [CrossRef]
- Huete, A.; Didan, K.; Miura, T.; Rodriguez, E.P.; Gao, X.; Ferreira, L.G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 2002, 83, 195–213. [Google Scholar] [CrossRef]
- US Department of Agriculture, National Agricultural Statistics Service. Cropland Data Layer; USDA-NASS: Washington, DC, USA, 2016. Available online: https://www.nass.usda.gov/Research_and_Science/Cropland/Release/index.php (accessed on 17 March 2021).
- Gilmore, E.C., Jr.; Rogers, J.S. Heat Units as a Method of Measuring Maturity in Corn. Agron. J. 1958, 50, 611–615. [Google Scholar] [CrossRef]
- Lobell, D.B.; Thau, D.; Seifert, C.; Engle, E.; Little, B. A scalable satellite-based crop yield mapper. Remote Sens. Environ. 2015, 164, 324–333. [Google Scholar] [CrossRef]
- Battude, M.; Al Bitar, A.; Morin, D.; Cros, J.; Huc, M.; Sicre, C.M.; Le Dantec, V.; Demarez, V. Estimating maize biomass and yield over large areas using high spatial and temporal resolution Sentinel-2 like remote sensing data. Remote Sens. Environ. 2016, 184, 668–681. [Google Scholar] [CrossRef]
- Jin, Z.; Azzari, G.; Lobell, D.B. Improving the accuracy of satellite-based high-resolution yield estimation: A test of multiple scalable approaches. Agric. For. Meteorol. 2017, 247, 207–220. [Google Scholar] [CrossRef]
- Gao, F.; Anderson, M.; Daughtry, C.; Johnson, D. Assessing the variability of corn and soybean yields in central Iowa using high spatiotemporal resolution multi-satellite imagery. Remote Sens. 2018, 10, 1489. [Google Scholar] [CrossRef] [Green Version]
- Lai, Y.R.; Pringle, M.J.; Kopittke, P.M.; Menzies, N.W.; Orton, T.G.; Dang, Y.P. An empirical model for prediction of wheat yield, using time-integrated Landsat NDVI. Int. J. Appl. Earth Obs. 2018, 72, 99–108. [Google Scholar] [CrossRef]
- Kang, Y.; Özdoğan, M. Field-level crop yield mapping with Landsat using a hierarchical data assimilation approach. Remote Sens. Environ. 2019, 228, 144–163. [Google Scholar] [CrossRef]
- Hunt, M.L.; Blackburn, G.A.; Carrasco, L.; Redhead, J.W.; Rowland, C.S. High resolution wheat yield mapping using Sentinel-2. Remote Sens. Environ. 2019, 233, 111410. [Google Scholar] [CrossRef]
- Kayad, A.; Sozzi, M.; Gatto, S.; Marinello, F.; Pirotti, F. Monitoring within-field variability of corn yield using sentinel-2 and machine learning techniques. Remote Sens. 2019, 11, 2873. [Google Scholar] [CrossRef] [Green Version]
- Skakun, S.; Kalecinski, N.I.; Brown, M.G.L.; Johnson, D.M.; Vermote, E.F.; Roger, J.-C.; Franch, B. Assessing within-field corn and soybean yield variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 satellite imagery. Remote Sens. 2021, 13, 872. [Google Scholar] [CrossRef]
- Azzari, G.; Jain, M.; Lobell, D.B. Towards fine resolution global maps of crop yields: Testing multiple methods and satellites in three countries. Remote Sens. Environ. 2017, 202, 129–141. [Google Scholar] [CrossRef]
- Lambert, M.-J.; Traoré, P.C.S.; Blaes, X.; Baret, P.; Defourny, P. Estimating smallholder crops production at village level from Sentinel-2 time series in Mali′s cotton belt. Remote Sens. Environ. 2018, 216, 647–657. [Google Scholar] [CrossRef]
Crop | Region | Model Performance | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Trend | Peak NDVI | Accumulated NDVI | ||||||||
R2 | SE 1 | CV | R2 | SE 1 | CV | R2 | SE 1 | CV | ||
Corn | USA | 0.48 | 11.4 | 7.4 | 0.88 | 5.6 | 3.5 | 0.93 | 4.3 | 2.7 |
Illinois | 0.29 | 22.0 | 12.8 | 0.82 | 11.0 | 6.6 | 0.91 | 7.7 | 4.5 | |
Indiana | 0.26 | 20.1 | 12.5 | 0.77 | 11.1 | 7.0 | 0.87 | 8.3 | 5.2 | |
Iowa | 0.32 | 14.2 | 8.1 | 0.64 | 10.4 | 5.9 | 0.78 | 8.2 | 4.6 | |
Kansas | 0.02 | 15.7 | 12.0 | 0.24 | 13.8 | 10.6 | 0.36 | 12.7 | 9.8 | |
Minnesota | 0.48 | 11.5 | 6.8 | 0.71 | 8.5 | 5.0 | 0.84 | 6.3 | 3.8 | |
Missouri | 0.23 | 24.8 | 17.9 | 0.53 | 19.4 | 14.0 | 0.58 | 18.4 | 13.2 | |
Nebraska | 0.62 | 10.6 | 6.4 | 0.84 | 6.9 | 4.2 | 0.89 | 5.8 | 3.5 | |
Ohio | 0.33 | 19.2 | 12.3 | 0.83 | 9.6 | 6.2 | 0.77 | 11.3 | 7.3 | |
South Dakota | 0.59 | 14.3 | 10.7 | 0.68 | 12.6 | 9.4 | 0.79 | 10.2 | 7.6 | |
Wisconsin | 0.60 | 11.1 | 7.3 | 0.90 | 5.5 | 3.6 | 0.78 | 8.2 | 5.4 | |
Soybeans | USA | 0.72 | 2.6 | 5.8 | 0.62 | 3.0 | 6.8 | 0.73 | 2.5 | 5.7 |
Arkansas | 0.80 | 2.9 | 7.0 | 0.04 | 6.5 | 15.4 | 0.27 | 5.6 | 13.4 | |
Illinois | 0.68 | 4.0 | 7.9 | 0.28 | 6.0 | 11.9 | 0.54 | 4.8 | 9.5 | |
Indiana | 0.54 | 3.8 | 7.7 | 0.54 | 3.8 | 7.7 | 0.65 | 3.3 | 6.7 | |
Iowa | 0.36 | 4.9 | 9.6 | 0.48 | 4.4 | 8.7 | 0.61 | 3.8 | 7.5 | |
Kansas | 0.28 | 6.4 | 17.9 | 0.77 | 3.6 | 10.2 | 0.85 | 2.9 | 8.3 | |
Minnesota | 0.42 | 4.2 | 9.6 | 0.07 | 5.3 | 12.2 | 0.47 | 4.0 | 9.2 | |
Missouri | 0.43 | 4.8 | 11.9 | 0.66 | 3.8 | 9.3 | 0.66 | 3.7 | 9.1 | |
Nebraska | 0.64 | 4.0 | 7.7 | 0.87 | 2.4 | 4.7 | 0.90 | 2.1 | 4.1 | |
North Dakota | 0.15 | 3.7 | 11.4 | 0.11 | 3.8 | 11.7 | 0.18 | 3.6 | 11.2 | |
Ohio | 0.57 | 4.1 | 8.7 | 0.64 | 3.8 | 8.0 | 0.62 | 3.9 | 8.2 | |
South Dakota | 0.62 | 3.9 | 10.1 | 0.47 | 4.6 | 11.8 | 0.60 | 4.0 | 10.2 | |
Spring Wheat | USA | 0.58 | 3.8 | 8.8 | 0.40 | 4.5 | 6.8 | 0.60 | 3.6 | 8.6 |
Minnesota | 0.37 | 6.2 | 11.5 | 0.47 | 5.7 | 10.6 | 0.33 | 6.3 | 11.9 | |
Montana | 0.34 | 5.1 | 16.8 | 0.76 | 3.1 | 10.1 | 0.81 | 2.7 | 8.9 | |
North Dakota | 0.55 | 4.7 | 11.4 | 0.26 | 6.1 | 14.6 | 0.52 | 4.9 | 11.7 | |
Winter Wheat | USA | 0.48 | 3.2 | 6.9 | 0.08 | 4.2 | 9.2 | 0.21 | 3.9 | 8.4 |
Colorado | 0.26 | 7.5 | 21.8 | 0.52 | 6.0 | 17.5 | 0.40 | 6.8 | 19.7 | |
Idaho | 0.24 | 6.4 | 7.6 | 0.23 | 6.4 | 7.6 | 0.45 | 5.4 | 6.5 | |
Kansas | 0.15 | 7.0 | 17.2 | 0.18 | 6.8 | 16.8 | 0.40 | 5.9 | 14.5 | |
Oklahoma | 0.03 | 6.9 | 22.1 | 0.40 | 5.4 | 17.3 | 0.25 | 6.0 | 19.3 | |
Montana | 0.48 | 4.3 | 10.1 | 0.53 | 4.1 | 9.7 | 0.64 | 3.6 | 8.4 | |
Washington | 0.21 | 7.0 | 10.5 | 0.37 | 6.3 | 9.4 | 0.67 | 4.5 | 6.7 | |
Cotton | USA | 0.24 | 52.7 | 6.4 | 0.16 | 55.1 | 6.7 | 0.09 | 57.4 | 7.0 |
Georgia | 0.27 | 99.8 | 11.9 | 0.19 | 105.1 | 12.5 | 0.00 | 116.9 | 14.0 | |
Texas | 0.05 | 91.4 | 13.8 | 0.42 | 71.1 | 10.7 | 0.35 | 75.6 | 11.4 |
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Johnson, D.M.; Rosales, A.; Mueller, R.; Reynolds, C.; Frantz, R.; Anyamba, A.; Pak, E.; Tucker, C. USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better than Simple Trend Analyses? Remote Sens. 2021, 13, 4227. https://doi.org/10.3390/rs13214227
Johnson DM, Rosales A, Mueller R, Reynolds C, Frantz R, Anyamba A, Pak E, Tucker C. USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better than Simple Trend Analyses? Remote Sensing. 2021; 13(21):4227. https://doi.org/10.3390/rs13214227
Chicago/Turabian StyleJohnson, David M., Arthur Rosales, Richard Mueller, Curt Reynolds, Ronald Frantz, Assaf Anyamba, Ed Pak, and Compton Tucker. 2021. "USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better than Simple Trend Analyses?" Remote Sensing 13, no. 21: 4227. https://doi.org/10.3390/rs13214227