Scientia Agricultura Sinica ›› 2017, Vol. 50 ›› Issue (5): 871-880.doi: 10.3864/j.issn.0578-1752.2017.05.010

• SOIL & FERTILIZER·WATER-SAVING IRRIGATION·AGROECOLOGY & ENVIRONMENT • Previous Articles     Next Articles

Leaf Water Potential Estimating Models of Winter Wheat Based on Hyperspectral Remote Sensing

CHEN ZhiFang, SONG Ni, WANG JingLei, SUN JingSheng   

  1. Farmland Irrigation Research Institute, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Water Use and Regulation, Ministry of Agriculture, Xinxiang 453002, Henan
  • Received:2016-08-01 Online:2017-03-01 Published:2017-03-01

RichHTML

2

PDF

1114

Cited

1

Abstract: 【Objective】A model for fast, non-destructive and accurately monitoring leaf water potential of winter wheat was established with hyperspectra technology, it will provide a scientific basis for the precision irrigation management of winter wheat.【Method】Using the field trials of different water treatments, the canopy spectral reflectance, leaf water potential and soil moisture were synchronously determined in the growth period of winter wheat. Then the correlation between the hyperspectral vegetation indices and leaf water potential was analyzed. Using correlation analysis, regression analysis and other methods, four inversion models were constructed for estimating leaf water potential based on different water treatments.【Result】The canopy spectral reflectance of winter wheat had significant change characteristics in different water treatments and growth periods. In the visible wave band, the canopy reflectance of winter wheat was reduced gradually along with the increase of soil water content. But, in the near-infrared wave band, the canopy reflectance was increased with the increase of soil water content. With the development of wheat growth period, in the near-infrared wave band, the canopy reflectance at heading stage was higher than the reflectance at jointing stage. And after the filling stage, the reflectance of red and blue band was rose faster. The correlation between four vegetation indices and leaf water potential was all reached the significant level (P<0.05), and its absolute values of correlation coefficient were all above 0.711. Four vegetation indices could be used for quantitative monitoring leaf water potential of winter wheat. Under the field capacity of 70%, the absolute values of correlation coefficient |r| between the vegetation indexes of OSAVI and EVI and the leaf water potential were 0.75 and 0.771, respectively, they were lower than the |r| between the vegetation indexes of NDVI and RVI and leaf water potential, which the values of |r| were 0.808 and 0.896, respectively. But, under the field capacity of 50%, the results were just the opposite. The |r| between the vegetation indexes of OSAVI and EVI2 and the leaf water potential were 0.857 and 0.853, respectively, which were higher than the |r| between the vegetation indexes of NDVI and RVI and the leaf water potential, which the values of |r| were 0.711 and 0.792, respectively. The estimation values of 45 samples in prediction set were close to the measured values, the range of R2, MRE, and RMSE were 0.616-0.616, -17.50%—-12.52% and 0.102-0.133, respectively. Under the 70% FC water treatment, the estimating model of leaf water potential based on EVI2 had the highest R2, the value of R2 was 0.922, and under the 60% FC and 50% FC water treatments, because of considering the influence of soil background, the inversion models of leaf water potential based on OSAVI had the highest R2, the values of R2 were 0.922 and 0.856, respectively.【Conclusion】All the four vegetation indices could be used for quantitative monitoring leaf water potential of winter wheat. But, when the leaf water potential estimating models were built for different water treatments, the influence of soil background on canopy spectral should be considered. The research results could provide a technical basis for wheat precision irrigation management and also provide supporting models for the parametric inversion of the onboard data.

Key words: hyperspectral remote sensing, winter wheat, vegetation index, leaf water potential, estimation

[1]    Kramer P J. Water Relations of Plants. New York: Academic Press, 1983.
[2]    Turner N C. Techniques and experimental approaches for the measurement of plant water status. Plant and Soil, 1981, 58(1): 339-366.
[3]    杨海清, 姚建松, 何勇. 基于反射光谱技术的植物叶片SPAD值预测建模方法研究. 光谱学与光谱分析, 2009, 29(6): 1607-1610.
Yang H Q, Yao J S, He Y. SPAD prediction of leave based on reflection spectroscopy. Spectroscopy and Spectral Analysis, 2009, 29(6): 1607-1610. (in Chinese)
[4]    杨峰, 范亚民, 李建龙, 钱育蓉, 王艳, 张洁. 高光谱数据估测稻麦叶面积指数和叶绿素密度. 农业工程学报, 2010, 26(2): 237-243.
Yang F, Fan Y M, Li J L, Qian Y R, Wang Y, Zhang J. Estimating LAI and CCD of rice and wheat using hyperspectral remote sensing data. Transactions of the Chinese Society of Agricultural Engineering,2010, 26(2): 237-243. (in Chinese)
[5]    潘蓓, 赵庚星, 朱西存, 刘海腾, 梁爽, 田大德. 利用高光谱植被指数估测苹果树冠层叶绿素含量. 光谱学与光谱分析, 2013, 33(8): 2203-2206.
Pan B, Zhao G X, Zhu X C, Liu H T, Liang S, Tian D D. Estimation of chlorophyll content in apple tree canopy based on hyperspectral parameters. Spectroscopy and Spectral Analysis, 2013, 33(8): 2203-2206. (in Chinese)
[6]    李颖, 薛利红, 潘复燕, 杨林章. 氮磷互作对水稻冠层光谱的影响及其PNN识别. 中国农业科学, 2014, 47(14): 2742-2750.
Li Y, Xue L H, Pan F Y, Yang L Z. Effects of interaction of N and P on rice canopy spectral reflectance and its PNN identification. Scientia Agricultura Sinica, 2014, 47(14) : 2742-2750. (in Chinese)
[7]    覃夏, 王绍华, 薛利红. 江西鹰潭地区早稻氮素营养光谱诊断模型的构建与应用. 中国农业科学, 2011, 44(4): 691-698.
Tan X, Wang S H, Xue L H. Nitrogen nutrition diagnosis of early rice with NDVI and its application for nitrogen topdressing recommendation at Yingtan, Jiangxi province. Scientia Agricultura Sinica, 2011, 44(4): 691-698. (in Chinese)
[8]    梁亮, 杨敏华, 邓凯东, 张连蓬, 林卉, 刘志霄. 一种估测小麦冠层氮含量的新高光谱指数. 生态学报, 2011, 31(21): 6594-6605.
Liang L, Yang M H, Deng K D, Zhang L P, Lin H, Liu Z X. A new hyperspectral index for the estimation of nitrogen contents of wheat canopy. Acta Ecologica Sinica, 2011, 31(21): 6594-6605. (in Chinese)
[9]    翟清云, 张娟娟, 熊淑萍, 刘娟, 杨阳, 马新明. 基于不同土壤质地的小麦叶片氮含量高光谱差异及监测模型构建. 中国农业科学, 2013, 46(13): 2655-2667.
Zhai Q Y, Zhang J J, Xiong S P, Liu J, Yang Y, Ma X M. Research on hyperspectral differences and monitoring model of leaf nitrogen content in wheat based on different soil textures. Scientia Agricultura Sinica, 2013, 46(13): 2655-2667. (in Chinese)
[10]   刘冰峰, 李军, 赵刚峰, Naveed Tahir, 贺佳. 夏玉米叶片全氮含量高光谱遥感估算模型研究. 植物营养与肥料学报, 2012, 18(4): 813-824.
Liu B F, Li J, Zhao G F, Naveed Tahir, He J. Total nitrogen content estimation models of summer maize leaves using hyperspectral remote sensing. Plant Nutrition and Fertilizer Science, 2012, 18(4): 813-824. (in Chinese)
[11]   周丽丽, 冯汉宇, 阎忠敏, 刘克, 周顺利. 玉米叶片氮含量的高光谱估算及其品种差异. 农业工程学报, 2010, 26(8): 195-199.
Zhou L L, Feng H Y, Yan Z M, Liu K, Zhou S L. Hyperspectral diagnosis of leaf N concentration of maize and variety difference. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(8): 195-199. (in Chinese)
[12]   刘炜, 常庆瑞, 郭曼, 邢东兴, 员永生. 夏玉米可见/近红外光小波主成分提取与氮素含量神经网络检测. 红外与毫米波学报, 2011, 30(1): 48-54.
Liu W, Chang Q R, Guo M, Xing D X, Yuan Y S. Detection of leaf nitrogen content of summer corn using visible/near infrared spectra. Journal of Infrared and Millimeter Waves, 2011, 30(1): 48-54. (in Chinese)
[13]   潘蓓, 赵庚星, 朱西存, 王娜娜. 基于高光谱的苹果树冠层磷素状况估测模型研究. 红外, 2012, 33(6): 27-31.
Pan B, Zhao G X, Zhu X C, Wang N N. Estimation of phosphorus content in apple tree canopy based on hyperspectrum. Infrared, 2012, 33(6): 27-31. (in Chinese)
[14]   Chen M, Glaz B, Gilbert A R, Daroub H S, Barton E F, Wan Y S. Near-infrared reflectance spectroscopy analysis of phosphorus in sugarcane leaves. Agronomy Journal, 2002, 94(6): 1324-1331.
[15]   林芬芳, 丁晓东, 付志鹏, 邓劲松, 沈掌泉. 基于互信息理论的 水稻磷素营养高光谱诊断. 光谱学与光谱分析, 2009, 29(9): 2467-2470.
Lin F F, Ding X D, Fu Z P, Deng J S, Shen Z Q. Application of mutual information to variable selection in diagnosis of phosphorus nutrition in rice. Spectroscopy and Spectral Analysis, 2009, 29(9): 2467-2470. (in Chinese)
[16]   Vaesen K, Gilliams S, Nackaerts K, Coppin P. Ground-measured spectral signatures as indicators of ground cover and leaf area index: The case of paddy rice. Field Crops Research, 2001, 69(1): 13-25.
[17]   姚付启, 蔡焕杰, 王海江, 张倩, 王健. 基于平稳小波变换的冬小麦覆盖度高光谱监测. 农业机械学报, 2012, 43(3): 173-180.
Yao F Q, Cai H J, Wang H J, Zhang Q, Wang J. Monitoring winter wheat percentage vegetation cover based on stationary wavelet transformation derived from hyperspectral reflectance. Transactions of the Chinese Society for Agricultural Machinery, 2012, 43(3): 173-180. (in Chinese)
[18]   Patel N, Patnaik C, Dutta S, Shekh A, Dave A. Study of crop growth parameters using airborne imaging spectrometer data. International Journal of Remote Sensing, 2001, 22(12): 2401-2411.
[19]   夏天, 吴文斌, 周清波, 周勇, 于雷. 基于高光谱的冬小麦叶面积指数估算方法. 中国农业科学, 2012, 45(10): 2085-2092.
Xia T, Wu W B, Zhou Q B, Zhou Y, Yu L. An estimation method of winter wheat leaf area index based on hyperspectral data. Scientia Agricultura Sinica, 2012, 45(10): 2085-2092. (in Chinese)
[20]   刘轲, 周清波, 吴文斌, 陈仲新, 唐华俊. 基于多光谱与高光谱遥感数据的冬小麦叶面积指数反演比较. 农业工程学报, 2016, 32(3): 155-162.
Liu K, Zhou Q B, Wu W B, Chen Z X, Tang H J. Comparison between multispectral and hyperspectral remote sensing for LAI estimation. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(3): 155-162. (in Chinese)
[21]   杨飞, 张柏, 宋开山, 王宗明, 刘殿伟, 徐京萍. 玉米光合有效辐射分量高光谱估算的初步研究. 中国农业科学, 2008, 41(7): 1947-1954.
Yang F, Zhang B, Song K S, Wang Z M, Liu D W, Xu J P. Hyperspectral estimation of corn fraction of photosynthetically active radiation. Scientia Agricultura Sinica, 2008, 41(7): 1947-1954. (in Chinese)
[22]   李宗南. 基于光能利用率模型和定量遥感的玉米生长监测方法研究[D]. 北京: 中国农业科学院, 2014.
Li Z N. Research on method of maize growth monitoring using light use efficiency model and quantitative remote sensing[D]. Beijing: Chinese Academy of Agricultural Sciences, 2014. (in Chinese)
[23]   夏天. 基于高光谱遥感的区域冬小麦生物量模拟及粮食安全评价[D]. 武汉: 华中师范大学, 2010.
Xia T. Regional wheat biomass simulation and food safety evaluation that based on hyperspectral remote sensing[D]. Wuhan: Central China Normal University, 2010. (in Chinese)
[24]   王备战, 冯晓, 温暖, 郑涛, 杨武德. 基于SPOT-5影像的冬小麦拔节期生物量及氮积累量监测. 中国农业科学, 2012, 45(15): 3049-3057.
Wang B Z, Feng X, Wen N, Zheng T, Yang W D. Monitoring biomass and N accumulation at jointing stage in winter wheat based on SPOT-5 images. Scientia Agricultura Sinica, 2012, 45(15): 3049-3057. (in Chinese)
[25]   田永超, 曹卫星, 姜东, 朱艳. 不同水氮条件下水稻冠层反射光谱与叶片水势关系的研究. 水土保持学报, 2003, 17(3): 178-180.
Tian Y C, Cao W X, Jiang D, Zhu Y. Relationship between canopy reflectance and leaf water potential in rice under different soil water and nitrogen conditions. Journal of Soil and Water Conservation, 2003, 17(3): 178-180. (in Chinese)
[26]   陈海波, 李就好, 余长洪, 刘德灿. 基于冠层光谱反射特征的甘蔗叶水势模型. 灌溉排水学报, 2014, 33(3): 92-96.
Chen H B, Li J H, Yu Z H, Liu D C. Sugarcane leaf water potential model based on canopy spectral signature. Journal of Irrigation and Drainage, 2014, 33(3): 92-96. (in Chinese)
[27]   Chen H B, Wang P, Li J H, Zhang J D, Zhong L X. Canopy spectral reflectance feature and leaf water potential of sugarcane inversion. Physics Procedia, 2012, 25: 595-600.
[28]   VILA H, HUGALDE I, DI FILIPPO M. Estimation of leaf water potentialby by thermographic and spectral measurements in grapevine. Ria Revista De Investigaciones Agropecuarias, 2011, 37(1): 46-52.
[29]   Rodríguez-Pérez J R, Riaño D, Carlisle E, Ustin S, Smart D R. Evaluation of hyperspectral reflectance indexes to detect grapevine water status in vineyards. American Journal of Enology and Viticulture, 2007, 58(3): 302-317.
[30]   张杰, 张强, 赵宏, 张平兰. 定量遥感反演作物水势的原理及其应用. 生态学杂志, 2008, 27(6): 916-923.
Zhang J, Zhang Q, Zhao H, Zhang P L. Theory and application of leaf water potential retrieved from remote sensing. Chinese Journal of Ecology, 2008, 27(6): 916-923. (in Chinese)
[31]   Rondeaux G, Steven M, Baret F. Optimization of soil adjusted vegetation indices. Remote Sensing of Environment, 1996, 55(2): 95-107.
[32]   Jiang Z, Huete A R, Didan K, Miura T. Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 2008, 112(10): 3833-3845.
[33]   Rouse J W, Haas R H, Schell J A, Harlan J C. Monitoring the vernal advancement and retrogradation (greenwave effect) of natural vegetation[R], 1974.
[34]   高林, 李长春, 王宝山, 杨贵军, 王磊, 付奎. 基于多源遥感数据的大豆叶面积指数估测精度对比. 应用生态学报, 2016, 27(1): 191-200.
GAO L, LI C C, WANG B S, YANG G J, WANG L, FU K. Comparison of precision in retrieving soybean leaf area index based on multi-source remote sensing data. Chinese Journal of Applied Ecology, 2016, 27(1): 191-200. (in Chinese)
[35]   张喜英. 叶水势反映冬小麦和夏玉米水分亏缺程度的试验(简报). 植物生理学通讯, 1997, 33(4): 249-253.
Zhang X Y. A report on diagnosis of soil water deficiency of wheat and maize using leaf water potential. Plant Physiology Communications, 1997, 33(4): 249-253. (in Chinese)
[36]   康绍忠, 刘晓明, 王振镒. 冬小麦叶片水势、气孔阻力、蒸腾速率与环境因素的关系. 灌溉排水, 1991, 10(3): 1-6.
Kang S Z, Liu X M, Wang Z Y. Relations between leaf water potential, stomatal resistance and transpiration rate of winter wheat, and environmental factors. Guangai Paishui, 1991, 10(3): 1-6. (in Chinese)
[37]   李合生. 现代植物生理学. 2版. 北京: 高等教育出版社, 2006: 353-354.
Li H S. Modern Plant Physiology. 2nd Ed. Beijing: Higher Education Press, 2006: 353-354. (in Chinese)
[38]   Rock B N, Vogelmann J E, Williams D L, Vogelmann A F, Hoshizaki T. Remote detection of forest damage. BioScience, 1986, 36(7): 439-445.
[39]   谷艳芳. 不同水分处理下冬小麦光合产物分配格局、过程及调控机制的研究[D]. 开封: 河南大学, 2008.
Gu Y F. Partition pattern, process and regulation mechanisms of photosynthate of winter wheat under different water treatments[D]. Kaifeng: Henan University, 2008. (in Chinese)
[40]   Smith K L, Steven M D, Colls J J. Use of hyperspectral derivative ratios in the red-edge region to indentify plant stress responses to gas leaks. Remote Sensing of Environment, 2004, 92(2): 207-217.
[41]   姚付启. 冬小麦高光谱特征及其生理生态参数估算模型研究[D]. 杨凌: 西北农林科技大学, 2012.
Yao F Q. Hyperspectral characteristics and estimating models about physiological ecological parameters of winter wheat[D]. Yangling: Northwest A & F University, 2012. (in Chinese)
[42]   Phillips S B, Keahey D A, Warren J G, Mulins G L. Estimating winter wheat tiller density using spectral reflectance sensors for early-spring, variable-rate nitrogen applications. Agronomy Journal, 2004, 96(3): 591-600.
[43]   姜海玲, 杨杭, 陈小平, 王树东, 李雪轲, 刘凯, 岑奕. 利用光谱指数反演植被叶绿素含量的精度及稳定性研究. 光谱与光谱学分析, 2015, 35(4): 975-981.
Jiang H L, Yang H, Chen X P, Wang S D, Li X K, Liu K, Cen Y. Research on accuracy and stability of inversing vegetation chlorophyll content by spectral index method. Spectroscopy and Spectral Analysis, 2015, 35(4): 975-981. (in Chinese)
[44]   张俊华, 张佳宝. 不同生育期冬小麦光谱特征对叶绿素和氮素的响应研究. 土壤通报, 2008, 39(3): 586-592.
Zhang J H, Zhang J B. Response of winter wheat spectral reflectance to leaf chlorophyll, total nitrogen of above ground. Chinese Journal of Soil Science, 2008, 39(3): 586-592. (in Chinese)
[45]   昝亚玲. 氮磷对旱地冬小麦产量、养分利用及籽粒矿质营养品质的影响[D]. 杨凌: 西北农林科技大学, 2012.
Zan Y L. Effect of nitrogen and phosphorus fertilizer rate on    yield, nutrient utilization and grain mineral nutrient quality of wheat in dryland[D]. Yangling: Northwest A&F University, 2012. (in Chinese)
[46]   Hutte 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 Sensing of Environment, 2002, 83: 195-213.
[1] ZHU Qi, JIA ZhenPeng, Tahir SHAH, XU ChenSheng, LI ZhiQi, LÜ HuiShuai, ZHU PengChao, WEI XiaoMin, HUANG DongLin, SUN YanNi, CAO WeiDong, GAO YaJun, WANG ZhaoHui, ZHANG DaBin. Green Manure Crops Combined with Enhanced-Efficiency Products Reduced Greenhouse Gas Emissions and Carbon Footprints in Dryland Wheat Fields [J]. Scientia Agricultura Sinica, 2026, 59(7): 1507-1522.
[2] QIAN Jin, LI YingXue, WU Fang, ZOU XiaoChen. Improved Leaf Phosphorus Content Estimation of Winter Wheat Using Ensemble Hyperspectral Dimensionality Reduction Method [J]. Scientia Agricultura Sinica, 2026, 59(4): 781-792.
[3] KONG Yuan, CUI ShaSha, LI Mei, LI Jian, YANG SiYu, FANG Feng, LIU ShuaiShuai, LIU MingPing, ZENG Yan, GAO XingXiang, BAI LianYang. Spatiotemporal Distribution Dynamics of Five Grass Weed Species Including Lolium multiflorum in Winter Wheat Fields of the Huang- Huai-Hai Region [J]. Scientia Agricultura Sinica, 2026, 59(4): 807-823.
[4] XIAN QingLin, XIAO JianKe, GAO AQing, GAO LiChuang, LIU Yang. Effects of Planting Patterns Combined with Soil Moisture Measurement and Supplementary Irrigation on the Yield and Water Use Efficiency of Winter Wheat [J]. Scientia Agricultura Sinica, 2026, 59(3): 589-601.
[5] LÜ XuDong, SUN ShiYuan, LI YaNan, LIU YuLong, WANG YanQun, FU Xin, ZHANG JiaYing, NING Peng, PENG ZhengPing. Effects of Intelligent Mechanized Layered Fertilization on Root-Soil Nutrient Distribution and Yield in Wheat Fields [J]. Scientia Agricultura Sinica, 2026, 59(1): 129-146.
[6] PU LiXia, ZHANG JiaRui, YE JianPing, HUANG XiuLan, FAN GaoQiong, YANG HongKun. The Combined Effects of 16, 17-Dihydro Gibberellin A5 and Straw Mulching on Tillering and Grain Yield of Dryland Wheat [J]. Scientia Agricultura Sinica, 2025, 58(9): 1735-1748.
[7] SHI Fan, LI WenGuang, YI ShuSheng, YANG Na, CHEN YuMeng, ZHENG Wei, ZHANG XueChen, LI ZiYan, ZHAI BingNian. The Variation Characteristics of Soil Organic Carbon Fractions Under the Combined Application of Organic and Inorganic Fertilizers [J]. Scientia Agricultura Sinica, 2025, 58(4): 719-732.
[8] FANG KangRui, DING ShiJie, CHEN YuShan, YANG BingGeng, GUO TengFei, XU XinPeng, ZHAO ShiCheng, WANG XiuBin, HUANG ShaoMin, QIU ShaoJun, HE Ping, ZHOU Wei. In-Season Release Rate of Nitrogen and Phosphorus in Manure Fertilizers During the Wheat Season in Typical Fluvo-Aquic Soil Under the Combined Application of Chemical and Manure Fertilizers [J]. Scientia Agricultura Sinica, 2025, 58(24): 5234-5246.
[9] SHE YingJun, ZHOU ZiZhe, WU Ming, GUO Wei, SHI ChangJian, HU Chao, LI Ping. Effects of Groundwater Depth and Nitrogen Application on the Distribution of Soil Water and Salt and the Nutrient Absorption and Utilization of Winter Wheat [J]. Scientia Agricultura Sinica, 2025, 58(20): 4285-4304.
[10] WANG RongRong, XU NingLu, HUANG XiuLi, ZHAO KaiNan, HUANG Ming, WANG HeZheng, FU GuoZhan, WU JinZhi, LI YouJun. Effects of One-Off Irrigation and Nitrogen Fertilizer Management on Grain Yield and Quality in Dryland Wheat [J]. Scientia Agricultura Sinica, 2025, 58(1): 43-57.
[11] GAO XingXiang, KONG Yuan, ZHANG YaoZhong, LI Mei, LI Jian, JIN Yan, ZHANG GuoFu, LIU ShuaiShuai, LIU MingPing, ZENG Yan, BAI LianYang. Analysis on Distribution and Change of Weed Community in Winter Wheat Field in Henan Province [J]. Scientia Agricultura Sinica, 2025, 58(1): 91-100.
[12] ZANG ShaoLong, LIU LinRu, GAO YueZhi, WU Ke, HE Li, DUAN JianZhao, SONG Xiao, FENG Wei. Classification and Identification of Nitrogen Efficiency of Wheat Varieties Based on UAV Multi-Temporal Images [J]. Scientia Agricultura Sinica, 2024, 57(9): 1687-1708.
[13] GAO ChenKai, LIU ShuiMiao, LI YuMing, ZHAO ZhiHeng, SHAO Jing, YU HaoLin, WU PengNian, WANG YanLi, GUAN XiaoKang, WANG TongChao, WEN PengFei. The Related Driving Factors of Water Use Efficiency and Its Prediction Model Construction in Winter Wheat [J]. Scientia Agricultura Sinica, 2024, 57(7): 1281-1294.
[14] GAO ShangJie, LIU XingRen, LI YingChun, LIU XiaoWan. Effects of Biochar and Straw Return on Greenhouse Gas Emissions and Global Warming Potential in the Farmland [J]. Scientia Agricultura Sinica, 2024, 57(5): 935-949.
[15] ZHU RuiMing, ZHAO RongQin, JIAO ShiXing, LI XiaoJian, XIAO LianGang, XIE ZhiXiang, YANG QingLin, WANG Shuai, ZHANG HuiFang. Spatial Distribution and Driving Factors of Winter Wheat Irrigation Carbon Emission Intensity at Township Level in Henan Province [J]. Scientia Agricultura Sinica, 2024, 57(5): 950-964.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备09089781号-30
Copyright 2018 © Editorial office of Scientia Agricultura Sinica
Tel: 86-010-82106279    E-mail: zgnykx@mail.caas.net.cn
Supported by Beijing Magtech Co.Ltd