|
|
|
|
|
| Corn Yield Forecasting in Northeast China Using Remotely Sensed Spectral Indices and Crop Phenology Metrics |
| WANG Meng, TAO Fu-lu , SHI Wen-jiao |
1、Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, P.R.China
2、University of Chinese Academy of Sciences, Beijing 100049, P.R.China |
|
|
|
摘要 Early crop yield forecasting is important for food safety as well as large-scale food related planning. The phenology-adjusted spectral indices derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to develop liner regression models with the county-level corn yield data in Northeast China. We also compared the different spectral indices in predicting yield. The results showed that, using Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI) and Land Surface Water Index (LSWI), the best time to predict corn yields was 55-60 days after green-up date. LSWI showed the strongest correlation (R2=0.568), followed by EVI (R2=0.497) and NDWI (R2=0.495). The peak correlation between Wide Dynamic Range Vegetation Index (WDRVI) and yield was detected 85 days after green-up date (R2=0.506). The correlation was generally low for Normalized Difference Vegetation Index (NDVI) (R2=0.385) and no obvious peak correlation existed for NDVI. The coefficients of determination of the different spectral indices varied from year to year, which were greater in 2001 and 2004 than in other years. Leave-one-year-out approach was used to test the performance of the model. Normalized root mean square error (NRMSE) ranged from 7.3 to 16.9% for different spectral indices. Overall, our results showed that crop phenology-tuned spectral indices were feasible and helpful for regional corn yield forecasting.
Abstract Early crop yield forecasting is important for food safety as well as large-scale food related planning. The phenology-adjusted spectral indices derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to develop liner regression models with the county-level corn yield data in Northeast China. We also compared the different spectral indices in predicting yield. The results showed that, using Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI) and Land Surface Water Index (LSWI), the best time to predict corn yields was 55-60 days after green-up date. LSWI showed the strongest correlation (R2=0.568), followed by EVI (R2=0.497) and NDWI (R2=0.495). The peak correlation between Wide Dynamic Range Vegetation Index (WDRVI) and yield was detected 85 days after green-up date (R2=0.506). The correlation was generally low for Normalized Difference Vegetation Index (NDVI) (R2=0.385) and no obvious peak correlation existed for NDVI. The coefficients of determination of the different spectral indices varied from year to year, which were greater in 2001 and 2004 than in other years. Leave-one-year-out approach was used to test the performance of the model. Normalized root mean square error (NRMSE) ranged from 7.3 to 16.9% for different spectral indices. Overall, our results showed that crop phenology-tuned spectral indices were feasible and helpful for regional corn yield forecasting.
|
|
Received: 29 October 2013
Accepted:
|
| Fund: This study was supported by the National Basic Research Program of China (2010CB950902), the National Natural Science Foundation of China (41371002), and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA05090310). |
|
Corresponding Authors:
TAO Fu-lu, Tel: +86-10-64888269, E-mail: taofl@igsnrr.ac.cn
|
| About author: TAO Fu-lu, Tel: +86-10-64888269, E-mail: taofl@igsnrr.ac.cn |
Cite this article:
WANG Meng, TAO Fu-lu , SHI Wen-jiao.
2014.
Corn Yield Forecasting in Northeast China Using Remotely Sensed Spectral Indices and Crop Phenology Metrics. Journal of Integrative Agriculture, 13(7): 1538-1545.
|
| Balaghi R, Tychon B, Eerens H, Jlibene M. 2008. Empiricalregression models using NDVI, rainfall and temperaturedata for the early prediction of wheat grain yieldsin Morocco. International Journal of Applied EarthObservation and Geoinformation, 10, 438-452Becker-Reshef I, Vermote E, Lindeman M, Justice C. 2010. Ageneralized regression-based model for forecasting winterwheat yields in Kansas and Ukraine using MODIS data.Remote Sensing of Environment, 114, 1312-1323Bolton D K, Friedl M A. 2013. Forecasting crop yield usingremotely sensed vegetation indices and crop phenologymetrics. Agricultural and Forest Meteorology, 173, 74-84Chandrasekar K, Sesha S M, Roy P, Dwevedi R. 2010. LandSurface Water Index (LSWI) response to rainfall and NDVIusing the MODIS Vegetation Index product. InternationalJournal of Remote Sensing, 31, 3987-4005Chen J, Jonsson P, Tamura M, Gu Z H, Matsushita B, EklundhL. 2004. A simple method for reconstructing a high-qualityNDVI time-series data set based on the Savitzky-Golayfilter. Remote Sensing of Environment, 91, 332-344Dong M Y, Jiang Y, Zhang D Y, Wu Z F. 2013. Spatiotemporalchange in the climatic growing season in Northeast Chinaduring 1960-2009 Theoretical and Applied Climatology,111, 693-701Estes L, Bradley B, Beukes H, Hole D, Lau M, OppenheimerM, Schulze R, Tadross M, Turner W. 2013. Comparingmechanistic and empirical model projections of cropsuitability and productivity: Implications for ecologicalforecasting. Global Ecology and Biogeography, 22,1007-1018Funk C, Budde M E. 2009. Phenologically-tuned MODISNDVI-based production anomaly estimates for Zimbabwe.Remote Sensing of Environment, 113, 115-125 Hill M J, Donald G E. 2003. Estimating spatio-temporalpatterns of agricultural productivity in fragmentedlandscapes using AVHRR NDVI time series. RemoteSensing of Environment, 84, 367-384Huang J, Wang X, Li X, Tian H, Pan Z. 2013. Remotely sensedrice yield prediction using multi-temporal NDVI dataderived from NOAA’s-AVHRR. PLOS ONE, 8, e70816.Huete A, Liu H, Batchily K, van Leeuwen W. 1997. Acomparison of vegetation indices over a global set of TMimages for EOS-MODIS. Remote Sensing of Environment,59, 440-451Jönsson P, Eklundh L. 2004. TIMESAT - A program foranalyzing time-series of satellite sensor data. Computers& Geosciences, 30, 833-845Kastens J H, Kastens T L, Kastens D L A, Price K P, MartinkoE A, Lee R Y. 2005. Image masking for crop yieldforecasting using AVHRR NDVI time series imagery.Remote Sensing of Environment, 99, 341-356Li Z, Tang H, Yang P, Wu W, Zhong X C, Zhou Q, ZhangL, Zhou J. 2012. Spatio-temporal responses of croplandphenophases to climate change in Northeast China. Journalof Geographical Sciences, 22, 29-45Liu J Y, Liu M L, Tian H Q, Zhuang D F, Zhang Z X, Zhang W,Tang X M, Deng X Z. 2005. Spatial and temporal patternsof China’s cropland during 1990-2000: An analysis basedon Landsat TM data Remote Sensing of Environment, 98,442-456Liu J Y, Liu M L, Zhuang D F, Zhang Z X, Deng X Z. 2003a.Study on spatial pattern of land-use change in China during1995-2000 Science in China (Series D), 46, 373-384Liu J Y, Zhuang D F, Luo D, Xiao X. 2003b. Land-coverclassification of China: Integrated analysis of AVHRRimagery and geophysical data. International Journal ofRemote Sensing, 24, 2485-2500Liu Z, Yang X, Hubbard K G, Lin X. 2012. Maize potentialyields and yield gaps in the changing climate of northeastChina. Global Change Biology, 18, 3441-3454Mkhabela M S, Bullock P, Raj S, Wang S, Yang Y 2011. Cropyield forecasting on the Canadian Prairies using MODISNDVI data. Agricultural and Forest Meteorology, 151,385-393Mkhabela M S, Mkhabela M S, Mashinini N N. 2005. Earlycorn yield forecasting in the four agro-ecological regionsof Swaziland using NDVI data derived from NOAA’s-AVHRR. Agricultural and Forest Meteorology, 129, 1-9Moriondo M, Maselli F, Bindi M. 2007. A simple modelof regional wheat yield based on NDVI data. EuropeanJournal of Agronomy, 26, 266-274Mulianga B, Bégué A, Simoes M, Todoroff P. 2013.Forecasting regional sugarcane yield based on time integraland spatial aggregation of MODIS NDVI. Remote Sensing,5, 2184-2199Ren J, Chen Z, Zhou Q, Tang H. 2008. Regional yieldestimation for winter wheat with MODIS-NDVI data inShandong, China. International Journal of Applied EarthObservation and Geoinformation, 10, 403-413Sakamoto T, Gitelson A A, Arkebauer T J. 2013. MODISbasedcorn grain yield estimation model incorporating cropphenology information. Remote Sensing of Environment,131, 215-231Schaaf C B, Gao F, Strahler A H, Lucht W, Li X, Tsang T,Strugnell N C, Zhang X, Jin Y, Muller J P. 2002. Firstoperational BRDF, albedo nadir reflectance products fromMODIS. Remote Sensing of Environment, 83, 135-148Schut A, Stephens D, Stovold R, Adams M, Craig R. 2009.Improved wheat yield and production forecasting with amoisture stress index, AVHRR and MODIS data. Cropand Pasture Science, 60, 60-70Seiler R A, Kogan F, Wei G, Vinocur M. 2007. Seasonal andinterannual responses of the vegetation and production ofcrops in Cordoba-Argentina assessed by AVHRR derivedvegetation indices. Advances in Space Research, 39, 88-94Tao F, Zhang S, Zhang Z. 2012. Spatiotemporal changes ofwheat phenology in China under the effects of temperature,day length and cultivar thermal characteristics. EuropeanJournal of Agronomy, 43, 201-212Tucker C J. 1979. Red and photographic infrared linearcombinations for monitoring vegetation. Remote Sensingof Environment, 8, 127-150Tucker C, Holben B, Elgin Jr J, McMurtrey III J. 1980.Relationship of spectral data to grain yield variation.Photogrammetric Engineering and Remote Sensing, 46,657-666Wang Q, Adiku S, Tenhunen J, Granier A. 2005. On therelationship of NDVI with leaf area index in a deciduousforest site. Remote Sensing of Environment, 94, 244-255. |
| No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
