Validation of two Huanjing-1A/B satellite-based FAO-56 models for estimating winter wheat crop evapotranspiration during mid-season

文献类型: 外文期刊

第一作者: Jin, Xiuliang

作者: Jin, Xiuliang;Yang, Guijun;Xue, Xuzhang;Xu, Xingang;Li, Zhenhai;Feng, Haikuan;Jin, Xiuliang;Yang, Guijun;Xue, Xuzhang;Xu, Xingang;Li, Zhenhai;Feng, Haikuan;Jin, Xiuliang;Yang, Guijun;Xue, Xuzhang;Xu, Xingang;Li, Zhenhai;Feng, Haikuan;Jin, Xiuliang

作者机构:

关键词: Crop evapotranspiration;FAO-56 model;Crop coefficient;Vegetation index;Winter wheat

期刊名称:AGRICULTURAL WATER MANAGEMENT ( 影响因子:4.516; 五年影响因子:5.12 )

ISSN:

年卷期:

页码:

收录情况: SCI

摘要: Crop evapotranspiration (ETc) is an important indicator used in managing agriculture water and monitoring crop growth. The objectives of this study were to: (1) analyze the seasonal dynamics of crop coefficients (K-c) and basal crop coefficient (K-cb) derived from vegetation indices (VIs) based on a time series of Huanjing (HJ) satellite images during 2011 and 2013; (2) investigate daily and monthly variations of ET, at key growth stages of winter wheat using lysimeter or eddy covariance systems; (3) compare the performance of two Huanjing-1A/B satellite-based FAO-56 models (the FAO-56 dual-crop coefficient model and the vegetation indices-reference evapotranspiration (Vls-ET0) method) to the ET, measurements; (4) select the best ETc model for estimating daily ETc (mm/day) at the Xiaotangshan experimental site and its surrounding farmland in conjunction with HJ satellite overpasses from March to May 2011. The VIs and concurrent ETc were acquired at the Xiaotangshan experimental site, Beijing, China, during the 2011 and 2013 winter wheat growing seasons. The results showed that the overall tendencies of crop coefficient patterns (K-cb and K-c), ETc and ETc first increased and then decreased at key growth stages of winter wheat. The cumulative ETc of water consumption was highest at the heading-filling stage in May. Similar changes in cumulative ETc were found during April-May 2011 and 2013. The estimation accuracy of ET, was better based on FAO-56 dual-crop coefficient model (R-2 = 0.88 and RMSE= 1.06 mm/day in 2011 and R-2 = 0.84 and RMSE= 0.55 mm/day in 2013) than the VI-ET0 method (R-2 = 0.77 and RMSE= 1.22 mm/day in 2011 and R-2 = 0.67 and RMSE = 0.81 mm/day in 2013). The results indicated that the FAO-56 dual-crop coefficient model and VI-ETo methods were used to estimate ET, in winter wheat. Two Huanjing-1A/B satellite-based FAO-56 models were used to timely estimate ET, during the winter wheat mid-season, and ET, was used to adjust agricultural water management practices. (C) 2017 Elsevier B.V. All rights reserved.

分类号: S2

  • 相关文献

[1]Estimation of Grain Protein Content in Winter Wheat by Using Three Methods with Hyperspectral Data. Xiu-liang Jin,Wang, Ji-hua,Xiu-liang Jin,Xin-gang Xu,Hai-kuan Feng,Xiao-yu Song,Qian Wang,Xiu-liang Jin,Xin-gang Xu,Hai-kuan Feng,Xiao-yu Song,Qian Wang,Xiu-liang Jin,Wang, Ji-hua,Guo, Wen-shan. 2014

[2]Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain. Bareth, Georg,Lenz-Wiedemann, Victoria I. S.,Koppe, Wolfgang,Gnyp, Martin L.,Bareth, Georg,Li, Fei,Lenz-Wiedemann, Victoria I. S.,Chen, Xinping,Gnyp, Martin L.,Li, Fei,Miao, Yuxin,Jia, Liangliang,Koppe, Wolfgang,Hennig, Simon D.,Jia, Liangliang,Chen, Xinping,Zhang, Fusuo,Laudien, Rainer. 2014

[3]Forecasting of Powdery Mildew disease with multi-sources of remote sensing information. Zhang, Jingcheng,Yuan, Lin,Nie, Chenwei,Wei, Liguang,Yang, Guijun,Zhang, Jingcheng,Yang, Guijun,Zhang, Jingcheng,Yang, Guijun,Zhang, Jingcheng,Yuan, Lin. 2014

[4]Comparison of ET partitioning and crop coefficients between partial plastic mulched and non -mulched maize fields. Gong, Daozhi,Mei, Xurong,Hao, Weiping,Wang, Hanbo,Gong, Daozhi,Mei, Xurong,Hao, Weiping,Wang, Hanbo,Mei, Xurong,Caylor, Kelly K..

[5]FIELD EVALUATION ON WATER PRODUCTIVITY OF WINTER WHEAT UNDER SPRINKLER OR SURFACE IRRIGATION IN THE NORTH CHINA PLAIN. Liu, Hai-Jun,Kang, Yaohu,Yao, Su-Mei,Sun, Ze-Qiang,Liu, Shi-Ping,Wang, Qing-Gai,Sun, Ze-Qiang. 2013

[6]Performance Assessment of Combining AquaCrop Model with Recalculating Air Temperature of Straw-mulching Maize for Estimating Evapo-transpiration and Yield. Yang, Ning,Sun, Zhanxiang,Feng, Liangshan,Bai, Wei,Xiang, Wuyan,Yang, Ning,Zhang, Lizhen,Zheng, Muzi. 2017

[7]Estimation of carotenoid content at the canopy scale using the carotenoid triangle ratio index from in situ and simulated hyperspectral data. Kong, Weiping,Huang, Wenjiang,Zhou, Xianfeng,Kong, Weiping,Zhou, Xianfeng,Song, Xiaoyu,Casa, Raffaele. 2016

[8]ESTIMATION OF LEAF AREA INDEX BY USING MULTI-ANGULAR HYPERSPECTRAL IMAGING DATA BASED ON THE TWO-LAYER CANOPY REFLECTANCE MODEL. Liao, Qinhong,Zhao, Chunjiang,Yang, Guijun,Wang, Jihua,Zhang, Dongyan,Liao, Qinhong,Zhang, Dongyan,Coburn, Craig,Wang, Zhijie. 2013

[9]Mapping paddy biomass with multiple vegetation indexes by using multispectral remotely sensed image. Gu, Xiaohe,Wang, Yancang,Song, Xiaoyu,Xu, Xingang. 2016

[10]Assessment of Aerial Agrichemical Spraying Effect Using Moderate-Resolution Satellite Imagery. Zhang Dong-Yan,Wang Xiu,Chen Li-ping,Ma Wei,Zhang Dong-Yan,Zhang Dong-Yan,Wang Xiu,Chen Li-ping,Li Bin,Ma Wei,Lan Yu-bin,Lan Yu-bin,Zhou Xin-gen. 2016

[11]Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method. Liang, Liang,Zhang, Lianpeng,Lin, Hui,Liang, Liang,Zhao, Shuhe,Liang, Liang,Di, Liping,Deng, Meixia,Qin, Zhihao.

[12]REMOTE SENSING OF THE SEASONAL NAKED CROPLANDS USING SERIES OF NDVI IMAGES AND PHENOLOGICAL FEATURE. Shan, Zhengying,Xu, Qingyen. 2013

[13]Assessment of Chlorophyll Content Using a New Vegetation Index Based on Multi-Angular Hyperspectral Image Data. Liao Qin-hong,Zhang Dong-yan,Liao Qin-hong,Zhang Dong-yan,Wang Ji-hua,Yang Gui-jun,Yang Hao,Liao Qin-hong,Zhang Dong-yan,Wang Da-cheng,Craig, Coburn,Wong Zhijie. 2014

[14]Crop growth condition monitoring and analyzing in county scale by time series MODIS medium-resolution data. Yu, Kun,Wang, Zhiming,Sun, Ling,Shan, Jie,Mao, Liangjun. 2013

[15]Exploring new spectral bands and vegetation indices for estimating nitrogen nutrition index of summer maize. Zhao, Ben,Duan, Aiwang,Liu, Zhandong,Chen, Zhifang,Zhang, Jiyang,Xiao, Junfu,Liu, Zugui,Qin, Anzhen,Ning, Dongfeng,Ata-Ul-Karim, Syed Tahir,Gong, Zhihong. 2018

[16]Spectral Reflectance and Vegetation Index Changes in Deciduous Forest Foliage Following Tree Removal: Potential for Deforestation Monitoring. Peng, D.,Hu, Y.,Li, Z..

[17]Comparison of two methods for monitoring leaf total chlorophyll content (LTCC) of wheat using field spectrometer data. Jin, X.,Wang, K.,Li, S.,Jin, X.,Diao, W.,Xiao, C.,Wang, K.,Li, S.,Wang, F.,Chen, B..

[18]Effects of vegetation indices to the spatial changes of desert environment using EOS/MODIS data: A case study to Sangong inland arid ecosystem. Lu, Liping,Qin, Zhihao,Qin, Zhihao,Gao, Maofang,Zhao, Chengyi,Li, Wenjuan. 2006

[19]Comparison of vegetation indices and red-edge parameters for estimating grassland cover from canopy reflectance data. Liu, Zhan-Yu,Huang, Jing-Feng,Wu, Xin-Hong,Dong, Yong-Ping. 2007

[20]New fast detection method of forest fire monitoring and application based on FY-1D/MVISR data. Feng, Jianzhong,Tang, Huajun,Zhou, Qingbo,Chen, Zhongxin,Bai, Linyan,Feng, Jianzhong. 2008

作者其他论文 更多>>