JIA-2019-11

2642 ZHANG Xi-wang et al. Journal of Integrative Agriculture 2019, 18(11): 2628–2643 Gumma M K, Mohanty S, Nelson A, Arnel R, Mohammed I A, Das S R. 2015. Remote sensing based change analysis of rice environments in Odisha, India. Journal of Environmental Management , 148 , 31–41. Hilker T, Wulder M A, Coops N C, Linke J, McDermid G, Masek J G, Gao F, White J C. 2009. A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sensing of Environment , 113 , 1613–1627. Ju J C, Roy D P. 2008. The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally. Remote Sensing of Environment , 112 , 1196–1211. Kasetkasem T, Arora M K, Varshney P K. 2005. Super- resolution land cover mapping using a Markov random field based approach. Remote Sensing of Environment , 96 , 302–314. Li J, Narayanan R M. 2004. Integrated spectral and spatial information mining in remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing , 42 , 673–685. Li X, Ling F, Foody GM, Ge Y, Zhang Y, Du Y. 2017. Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps. Remote Sensing of Environment , 196 , 293–311. Liang S. 2004. Quantitative Remote Sending of Land Surfaces . John Wiley, New Jersey, USA. Ling F, Li X, Xiao F, Fang S, Du Y. 2012. Object-based sub- pixel mapping of buildings incorporating the prior shape information from remotely sensed imagery. International Journal of Applied Earth Observation and Geoinformation , 18 , 283–292. Liu H O, Huete A R. 1995. A feedback based modification of the NDVI to minimize canopy background and atmosphere noise. IEEE Transactions on Geoscience and Remote Sensing , 33 , 457–465. Liu K, Shi W, Zhang H. 2011. A fuzzy topology-based maximum likelihood classification. ISPRS Journal of Photogrammetry and Remote Sensing , 66 , 103–114. Liu J, Zhuang D, Luo D, Xiao X. 2003. Land-cover classification of China: Integrated analysis of AVHRR imagery and geophysical data. International Journal of Remote Sensing , 24 , 2485–2500. Lobell D B, Asner G P. 2004. Cropland distributions from temporal unmixing of MODIS data. Remote Sensing of Environment , 93 , 412–422. Löw F, Michel U, Dech S, Conrad C. 2013. Impact of feature selection on the accuracy and spatial uncertainty of per- field crop classification using Support Vector Machines. ISPRS Journal of Photogrammetry and Remote Sensing , 85 , 102–119. MacDonald R B, Hall F G. 1980. Global crop forecasting. Science , 208 , 670–679. Madosela S, Cho M A, Ramoelo A, Mutanga O, Naidoo L. 2018. Estimating tree species diversity in the savannah using NDVI and woody canopy cover. International Journal of Applied Earth Observation and Geoinformation , 66 , 106–115. Mennis J, Guo D. 2009. Spatial data mining and geographic knowledge discovery - an introduction. Computers , Environment and Urban Systems , 33 , 403–408. Moody A, Woodcock C E. 1995. The influence of scale and the spatial characteristics of landscapes on land-cover mapping using remote sensing. Landscape Ecology , 10 , 363–379. Murakami T, Ogawa S, Ishitsuka N, Kumagai K, Saito G. 2001. Crop discrimination with multitemporal SPOT/HRV data in the Saga Plains, Japan. International Journal of Remote Sensing , 22 , 1335–1348. NBSSOH (NBS Survey office in Henan). 2011. Henan Statistical Yearbook . China Statistics Press, China. (in Chinese) Van Niel T G, McVicar T R. 2004. Determining temporal windows for crop discrimination with remote sensing: A case study in south-eastern Australia. Computers and Electronics in Agriculture , 45 , 91–108. Nitze I, Barrett B, Cawkwell F. 2015. Temporal optimisation of image acquisition for land cover classification with Random Forest and MODIS time-series. International Journal of Applied Earth Observation and Geoinformation , 34 , 136–146. Oetter D R, Cohen W B, Berterretche M, Maiersperger T K, Kennedy R E. 2001. Land cover mapping in an agricultural setting using multiseasonal Thematic Mapper data. Remote Sensing of Environment , 76 , 139–155. Otukei J R, Blaschke T. 2010. Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation , 12S , S27–S31. Ozdogan M. 2010. The spatial distribution of crop types from MODIS data: Temporal unmixing using independent component analysis. Remote Sensing of Environment , 114 , 1190–1204. Ozdogan M, Woodcock C E. 2006. Resolution dependent errors in remote sensing of cultivated areas. Remote Sensing of Environment , 103 , 203–217. Pan Y, Li L, Zhang J, Liang S, Zhu X, Sulla-Menashe D. 2012. Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index. Remote Sensing of Environment , 119 , 234–242. Pan Z, Huang J, Zhou Q, Wang L, Cheng Y, Zhang H, Blackburn G A, Yan J, Liu J. 2015. Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data. International Journal of Applied Earth Observation and Geoinformation , 34 , 188–197. Poggio L, Gimona A. 2013. Modelling high resolution RS data with the aid of coarse resolution data and ancillary data. International Journal of Applied Earth Observation and Geoinformation , 23 , 360–371. Potapov P, Hansen M C, Stehman S V, Loveland T R, Pittman K. 2008. Combining MODIS and landsat imagery to estimate and map boreal forest cover loss. Remote Sensing of Environment , 112 , 3708–3719. Potgieter A B, Lawson K, Huete A R. 2013. Determining crop acreage estimates for specific winter crops using shape attributes from sequential MODIS imagery. International Journal of Applied Earth Observation and Geoinformation ,