基于植被指数和气温因子的棉花估产研究
摘要
新疆是全国棉花的主产区,其播种面积和产量的变化直接影响着全国棉花的产量和库存情况;科学地监测新疆棉花长势、准确地预报其产量,可为各级政府提供及时、准确的棉花生产农情,对政府进行农业相关决策具有重大的意义。
本研究以克拉玛依市为研究区,以遥感资料为基础,通过提取棉花归一化植被指数( NDVI),结合气温因子建立棉花单产的多元线性回归估产模型,以期提高估产精度,为新疆棉花大面积产量估测提供科学依据。
经分析共有10个NDVI因子与棉花产量具有较好相关性,而且时段比较集中,主要在6月下旬至7月下旬以及9月上旬;其中,6月下旬至7月下旬的8个因子与棉花产量为正相关,9月上旬的2个因子均与棉花产量为负相关。共有5个气温因子与棉花单产的相关性较显著,且均为负相关。而且与NDVI因子同样,时段比较集中,主要在5月和8月中旬。
研究采用强迫引入法建立基于7月NDVI累加值占主要生育期NDVI累加值百分比和8月中旬旬均温的多元线性回归方程:Y=-1624.061+11855.483XN10-40.679XT1; F值为13.297,显著性水平为0.017。
将多元线性回归模型与单因子回归模型进行对比检验,经检验基于因子N10的单因子线性回归模型估产相对误差在-8.6﹪—13.3﹪之间,指数回归模型的相对误差在-7﹪—11.8﹪之间;基于因子T1的单因子线性回归模型估产相对误差在-13.4﹪—10.7﹪之间;基于因子N10和因子T1的多元线性回归模型估产的相对误差在-5.9﹪—9.9﹪之间。可以看出,多元线性回归模型的估产精度较高,且模型的稳定性较好,具有一定的可行性。
Xinjiang is the main production area of cotton in china. The change of cotton’s sown area and output influences cotton's output and stock situation of nation immediately. Monitors Xinjiang cotton growing trend scientifically and forecasts its output accurately, may provides prompt accurate cotton production state of agricultural production for all levels of the government, and it has the significant significance carries on the agricultural decision-making to the government.
The research take Kalamy as an example, take the remote sensing material as the foundation, through extraction cotton normalization vegetation index, union temperature factor, establish multi-dimensional linear regression estimation model of cotton per unit area yield ,by time enhance the precision of field estimation,and provide the scientific basis for big area output estimation of cotton in Xinjiang.
After analysis, altogether 10 NDVI factors has the good relevance with the cotton output, moreover the time interval is quite centralized ,mainly in late June to late July as well as early September. And there are 8 factors in late June to late July's 8 factors, is being related with the cotton output; 2 factors in early September's is inverse correlation with the cotton output. Altogether 5 temperature factors is remarkable relevance with the cotton per unit area yield's, and all is inverse correlation, moreover the same as NDVI factors, the time interval is quite centralized, concentrates in May and mid-August.
The study using force introduction law, establish Multi-dimensional linear regression equation based on July NDVI accumulation value accounts for the main period of duration NDVI accumulation value percentage and average temperature in mid-August ten-day period : Y=-1624.061+11855.483XN10-40.679XT1. The value of F is 13.297, and the significance level is 0.017.
Carrying Multi-dimensional linear regression model and the single factor regression model on the contrast examination. After examination, single factor linear regression model based on factor N10 , has estimation relative error between -8.6﹪and 13.3﹪, index regression model based on factor N10 has relative error between -7﹪and 11.8﹪. Single factor linear regression model based on factor T1 has estimation relative error between -13.4﹪and 10.7﹪. The multi-dimensional linear regression model based on factor N10 and factor T1 has estimation relative error between - 5.9﹪and 9.9﹪.We may see the multi-dimensional linear regression model's estimation precision is higher, and the model stability is better, it has certain feasibility.
本研究以克拉玛依市为研究区,以遥感资料为基础,通过提取棉花归一化植被指数( NDVI),结合气温因子建立棉花单产的多元线性回归估产模型,以期提高估产精度,为新疆棉花大面积产量估测提供科学依据。
经分析共有10个NDVI因子与棉花产量具有较好相关性,而且时段比较集中,主要在6月下旬至7月下旬以及9月上旬;其中,6月下旬至7月下旬的8个因子与棉花产量为正相关,9月上旬的2个因子均与棉花产量为负相关。共有5个气温因子与棉花单产的相关性较显著,且均为负相关。而且与NDVI因子同样,时段比较集中,主要在5月和8月中旬。
研究采用强迫引入法建立基于7月NDVI累加值占主要生育期NDVI累加值百分比和8月中旬旬均温的多元线性回归方程:Y=-1624.061+11855.483XN10-40.679XT1; F值为13.297,显著性水平为0.017。
将多元线性回归模型与单因子回归模型进行对比检验,经检验基于因子N10的单因子线性回归模型估产相对误差在-8.6﹪—13.3﹪之间,指数回归模型的相对误差在-7﹪—11.8﹪之间;基于因子T1的单因子线性回归模型估产相对误差在-13.4﹪—10.7﹪之间;基于因子N10和因子T1的多元线性回归模型估产的相对误差在-5.9﹪—9.9﹪之间。可以看出,多元线性回归模型的估产精度较高,且模型的稳定性较好,具有一定的可行性。
Xinjiang is the main production area of cotton in china. The change of cotton’s sown area and output influences cotton's output and stock situation of nation immediately. Monitors Xinjiang cotton growing trend scientifically and forecasts its output accurately, may provides prompt accurate cotton production state of agricultural production for all levels of the government, and it has the significant significance carries on the agricultural decision-making to the government.
The research take Kalamy as an example, take the remote sensing material as the foundation, through extraction cotton normalization vegetation index, union temperature factor, establish multi-dimensional linear regression estimation model of cotton per unit area yield ,by time enhance the precision of field estimation,and provide the scientific basis for big area output estimation of cotton in Xinjiang.
After analysis, altogether 10 NDVI factors has the good relevance with the cotton output, moreover the time interval is quite centralized ,mainly in late June to late July as well as early September. And there are 8 factors in late June to late July's 8 factors, is being related with the cotton output; 2 factors in early September's is inverse correlation with the cotton output. Altogether 5 temperature factors is remarkable relevance with the cotton per unit area yield's, and all is inverse correlation, moreover the same as NDVI factors, the time interval is quite centralized, concentrates in May and mid-August.
The study using force introduction law, establish Multi-dimensional linear regression equation based on July NDVI accumulation value accounts for the main period of duration NDVI accumulation value percentage and average temperature in mid-August ten-day period : Y=-1624.061+11855.483XN10-40.679XT1. The value of F is 13.297, and the significance level is 0.017.
Carrying Multi-dimensional linear regression model and the single factor regression model on the contrast examination. After examination, single factor linear regression model based on factor N10 , has estimation relative error between -8.6﹪and 13.3﹪, index regression model based on factor N10 has relative error between -7﹪and 11.8﹪. Single factor linear regression model based on factor T1 has estimation relative error between -13.4﹪and 10.7﹪. The multi-dimensional linear regression model based on factor N10 and factor T1 has estimation relative error between - 5.9﹪and 9.9﹪.We may see the multi-dimensional linear regression model's estimation precision is higher, and the model stability is better, it has certain feasibility.
引文
[1]原军.加快新疆棉花发展-实现国内外棉花共同繁荣[J].中国棉麻流通经济,专题报道. 2007:30-33.
[2]杨光华,庄伟五,吕芳.新疆棉花产业发展优势、问题与对策[J].区域经贸.2006,7:28-31
[3]郑维,林修碧.新疆棉花与气象[M].乌鲁木齐:新疆科技卫生出版社.1993.
[4]徐德源.新疆农业气候资源及区划[M].北京:科学出版社,1984.
[5]阎雨,陈圣波,田静等.卫星遥感估产技术的发展与展望[J].吉林农业大学学报,2004,26(2):187一191.
[6]朱晓红,谢昆青,徐希孺等.冬小麦产量构成分析与遥感估产[J].环境遥感,1989,4(2): 116-127.
[7]王人潮,黄敬峰.水稻遥感估产[M].中国农业出版社,2002年5月第一版:7-37.
[8]李剑萍,郑有飞等作物遥感估产研究进展及未来趋势分析[J].气象教育与科技,1999,(2): 19-23.
[9]邢素丽,张广录.我国农业遥感的应用现状与发展[J].农业工程学报,2003,19(6): 174-178.
[10]Rassmussen M S.Developing simple,operational,consistent NDVI-vegetation.models by applying environment and climatic information.Part2:Cropyield assessment[J].international Journal of Remote Sensing,1998,19(l):119-139.
[11]李付琴,隋洪智.农作物遥感估产研究进展[J].地球科学进展,1992,7(3):30-36.
[12]Lozano-Carcia Fabian D,Norberto Fernandez R,Cbris J Johannnsen. Assessment of regional biomass-soil relationships using vegetation indexes[J].IEEE Transactions on Geoscience and Remote Sensing,1991,29(2):331-339.
[13]杨卫星,王长耀,牛铮等.全球稻谷主产国遥感估产可行性研究[J].1998,9(2): 251-256.
[14]Hatison,B A,Jupp,D L B.Ibraham A A,and Angus, JF.The use of data for monitoring growth of irrigated crop proceedings of the third Austratasion remote sensing conference.1984:36-43
[15]Felix N .Kogan.Operational Space Technology for Global Vegetation Assessment [J] . Bulletin of the American Meteorological Soiety,2001,82(9):1949-1964
[16]Van Dijk. A Crop Condition and Crop Yield Estimation Method Based on NOAA/AVHRR Satellite Data Remote Sensing,Africa[D].University of Missouri-Columbia,1986.
[17]Martin R.D. and Heilman,J.I.1986,Spectral Reflectance Paterns of Flooded Rice, PERS, 52(12),December 1986,PP:1885-1890.
[18]李郁竹.农作物气象卫星遥感监测和估产研究进展及前景探讨[J],气象科技,1977,(3): 29-34.
[19]陈沈斌.种植业可持续发展的支持系统一农作物卫星遥感估产[P].地理科学进展,1998, 17(2):71-77.
[20]杨邦杰,裴志远,周清波等.我国农情遥感监测关键技术研究进展[J].农业工程学报, 2002,18(3):191-194.
[21]肖乾广,周嗣松,陈维英等.用气象卫星数据对冬小麦进行估产的试验[J].环境遥感, 1986,1(4):260-269.
[22]徐希儒等.混合像元的因子分析方法及其在大面积冬小麦种植面积估算中的应用[J].科学通报,1990,35(4),317-320.
[23]卢志光,曹大微,张宏名等.遥感估产试区(通县)小麦单产预测[J].应用数学学报,1990, 13(2),149-155.
[24]徐希孺,朱晓红.冬小麦遥感估产模型,环境监测与作物估产的遥感研究论文集[M].北京大学出版社,1991.
[25]李付琴,田国良.小麦单产的遥感一一气象综合模式研究[J].环境遥感,1992, 8 (3):202一209.
[26]王乃斌等.大面积小麦遥感估产模型的构建与调试方法研究.小麦、玉米和水稻遥感估产技术试验研究文集[M].中国科学技术出版社,1993.
[27]杨卫星,薛正平,陆贤等.水稻遥感动力估产模拟初探[J].环境遥感.1994,9(4): 280-286.
[28]高峰.微机大面积水稻遥感信息提取研究[J].环境遥感,1994,9(2):92-99.
[29]吴炳方,刘海燕.水稻种植面积估计的运行化遥感方法[J].遥感学报,1997,1(1):58-64.
[30]千怀遂.中国小麦遥感估产区划研究[J].自然资源学报,1997,12(2):97-104.
[31]杨邦杰,陆登槐,裴志远等.国家级农情监测系统结构设计[J].农业工程学报,1997, 13(1):16-19.
[32]杨邦杰,裴志远,焦险峰等.基于CBERS-1卫星图像的新疆棉花遥感监测技术体系[J].农业工程学报,2003,19(6):146-149.
[33]张建华.作物估产的遥感-数值模拟方法[J].干早区资源与环境,2000,14(2):119-139.
[34]江东,王乃斌,杨小唤等.NDVI曲线与农作物长势的时序互动规律[J].生态学报,2002, 22(2):247-252.
[35]姜城,金继运,张维理.TM遥感与地块内冬小麦产量变异[J].遥感技术与应用,2001, 16(1):23-27.
[36]王长耀,林文鹏.基于MODIS-EVI的冬小麦产量遥感预测研究[J].农业工程学报,2005,21 (10):90-94.
[37]任建强,陈仲新,唐华俊.基于MODIS-NDVI的区域冬小麦遥感估产—以山东省济宁市为例[J].应用生态学报,2006,17(12):2372-2375.
[38]Ray,S.S.Pokharna and Ajai.Cotton yield estimation using agrometeorological model and satellite-deriveds pectral porfile[J].Intenrational Jounral of Remote Sensing,1999,20(14): 2693-2702.
[39]R.E.Plant,D.S.Munk,B.R.Roberts.Relationships between remotely sensed reflectanced data and coton growth and yield[J].Transactions of the ASAE,2000,43(3): 535-546.
[40]Stephan J.Maas.Linear Mixture Modeling Approach for Estimating Cotton Canopy Gorund Cover using Satellite Multi-spectral Imagery[J] .Remote Sensing Enviroment,2000,72: 304-308.
[41]N.R.Daleziosl.Cotton Yield Estimation Based on NOAA/AVHRR Produced NDVI[J]. Phys.Chem.Eurth(B),2001,26(3):247-251.
[42]黎泽文,姜栋,陆登槐登.棉花种植面积遥感估测方法的研究.农业遥感论文集[M].北京:1991.
[43]李明霞,千怀遂.中国棉花遥感估产最佳时相的选择[J].河南大学学报,1997,27(2): 73-78.
[44]张仁华.遥感模型及地面基础[M].北京:科学出版社,1996.
[45]王登伟,曹连莆,李少昆等.北强棉花群体光合特性分析[J].石河子大学学报(自然科学版),1999,3(增刊):31-39.
[46]焦险峰,杨邦杰,裴志远.全国棉花种植面积遥感监测抽样方法设计[J].农业工程学报, 2002,18(4):159-162.
[47]蒋桂英,李鲁华,刁明.高光谱分辨率遥感在新疆棉花上的应用前景[J].中国棉花,2003, 30(2):2-4.
[48]杨邦杰,裴志远,焦险峰等.基于CBERS-1卫星图像的新疆棉花遥感监测技术体系[J].农业工程学报,2003,19(6):146-149.
[49]柏军华,李少昆等.棉花产量遥感预测的L—Y模型构建[J].作物学报,2006,32(6): 840-844.
[50]樊科研,田丽萍,薛琳等.遥感在农作物估产中的应用与发展[J].安徽农学通报,2006, 12(11):145-147.
[51]克拉玛依市人民政府公众信息网.http://www.klmy.gov.cn/.
[52]张春环.克拉玛依大农业开发全面启动.人民网.http://www.people.com.cn/.2001,3.
[53]克拉玛依市统计年鉴.克拉玛依市统计局.
[54]中国棉花科技网.http://www.cricaas.com.cn/.
[55]黄敬峰,王秀珍.新疆棉花物候与气象条件研究[J].干旱区资源与环境.1999,13(2):90-95.
[56]曲辉,陈圣波.中分辨率成像光谱仪数据在地学中的应用前景[J].世界地质,2002, 621(2):176-180.
[57]丁静,唐军武.MODIS水色遥感数据的获取与产品处理综述[J].遥感技术与应用,2003,8, 18(4):263-268.
[58]张旭. EOS/MODIS影像处理及其在塔里木河下游植被变化中的应用[D].新疆大学,2004:12-23.
[59]刘闯,葛成辉.美国对地观测系统(EOS)中分辨率成像光谱仪(MODIS)遥感数据的特点与应用[J].遥感信息.2000,3,44-48.
[60]黄春林,李新.HDF-EOS数据格式在处理空间数据中的应用[J].遥感技术与应用.2001, 16(4):252-259.
[61]张文建.地球观测系统(EOS)中分辨率成像光谱仪(MODIS)—科学意义,仪器介绍和在我国环境遥感中的应用展望[R].EOS/MODIS资料接收应用培训教材,2001.
[62]MODIS Level LA Earth Location: A Lgorithm Theoretical Basis Document [Z].
[63]刘放,程方正.一种新的卫星红外遥感信息数据源EOS/MODIS数据[J].国际地震动态,2003,8(296):1-7.
[64]崔晓军.积温单位浅析[J].科技与出版.2006.4:57.
[65]边疆远.农业气象常用温度术语简介[J].农事气象.2002.12:27.
[66]董永平,吴新宏等.草原遥感监测技术[M].北京:化学工业出版社.2005.
[67]郭铌.植被指数及其研究进展[J].干旱气象.2003,21(4):71-75.
[68]刘蕾.2001-2005年新疆植被覆盖动态变化及原因分析[J].干旱环境监测.2007,21(3): 146-148.
[69]黄敬峰,王秀珍.新疆棉花物候与气候条件研究[J].干旱区资源环境.1999,13(2): 90-95.
[70]吕昭智,李莉等.新疆北部20年棉花物候计算和分析——以炮台镇为例[J].干旱区地理.2003,26(4):340-344.
[71]徐建华.现代地理学中的数学方法[M].高等教育出版社.2002,10第二版.
[72]杨华,王巧莲,勾洪波.昌吉州棉区棉花生育期气候条件分析[J].沙漠与绿洲气象. 2007.6:49-52.
[73]刘双俊,汤建国.气象条件对棉花蕾铃脱落影响的探讨[J].江西棉花.2007.29(5):26-27.
[74]章文波,陈红艳.使用数据统计分析及SPSS12.0应用[M].人民邮电出版社.2006,2.
[2]杨光华,庄伟五,吕芳.新疆棉花产业发展优势、问题与对策[J].区域经贸.2006,7:28-31
[3]郑维,林修碧.新疆棉花与气象[M].乌鲁木齐:新疆科技卫生出版社.1993.
[4]徐德源.新疆农业气候资源及区划[M].北京:科学出版社,1984.
[5]阎雨,陈圣波,田静等.卫星遥感估产技术的发展与展望[J].吉林农业大学学报,2004,26(2):187一191.
[6]朱晓红,谢昆青,徐希孺等.冬小麦产量构成分析与遥感估产[J].环境遥感,1989,4(2): 116-127.
[7]王人潮,黄敬峰.水稻遥感估产[M].中国农业出版社,2002年5月第一版:7-37.
[8]李剑萍,郑有飞等作物遥感估产研究进展及未来趋势分析[J].气象教育与科技,1999,(2): 19-23.
[9]邢素丽,张广录.我国农业遥感的应用现状与发展[J].农业工程学报,2003,19(6): 174-178.
[10]Rassmussen M S.Developing simple,operational,consistent NDVI-vegetation.models by applying environment and climatic information.Part2:Cropyield assessment[J].international Journal of Remote Sensing,1998,19(l):119-139.
[11]李付琴,隋洪智.农作物遥感估产研究进展[J].地球科学进展,1992,7(3):30-36.
[12]Lozano-Carcia Fabian D,Norberto Fernandez R,Cbris J Johannnsen. Assessment of regional biomass-soil relationships using vegetation indexes[J].IEEE Transactions on Geoscience and Remote Sensing,1991,29(2):331-339.
[13]杨卫星,王长耀,牛铮等.全球稻谷主产国遥感估产可行性研究[J].1998,9(2): 251-256.
[14]Hatison,B A,Jupp,D L B.Ibraham A A,and Angus, JF.The use of data for monitoring growth of irrigated crop proceedings of the third Austratasion remote sensing conference.1984:36-43
[15]Felix N .Kogan.Operational Space Technology for Global Vegetation Assessment [J] . Bulletin of the American Meteorological Soiety,2001,82(9):1949-1964
[16]Van Dijk. A Crop Condition and Crop Yield Estimation Method Based on NOAA/AVHRR Satellite Data Remote Sensing,Africa[D].University of Missouri-Columbia,1986.
[17]Martin R.D. and Heilman,J.I.1986,Spectral Reflectance Paterns of Flooded Rice, PERS, 52(12),December 1986,PP:1885-1890.
[18]李郁竹.农作物气象卫星遥感监测和估产研究进展及前景探讨[J],气象科技,1977,(3): 29-34.
[19]陈沈斌.种植业可持续发展的支持系统一农作物卫星遥感估产[P].地理科学进展,1998, 17(2):71-77.
[20]杨邦杰,裴志远,周清波等.我国农情遥感监测关键技术研究进展[J].农业工程学报, 2002,18(3):191-194.
[21]肖乾广,周嗣松,陈维英等.用气象卫星数据对冬小麦进行估产的试验[J].环境遥感, 1986,1(4):260-269.
[22]徐希儒等.混合像元的因子分析方法及其在大面积冬小麦种植面积估算中的应用[J].科学通报,1990,35(4),317-320.
[23]卢志光,曹大微,张宏名等.遥感估产试区(通县)小麦单产预测[J].应用数学学报,1990, 13(2),149-155.
[24]徐希孺,朱晓红.冬小麦遥感估产模型,环境监测与作物估产的遥感研究论文集[M].北京大学出版社,1991.
[25]李付琴,田国良.小麦单产的遥感一一气象综合模式研究[J].环境遥感,1992, 8 (3):202一209.
[26]王乃斌等.大面积小麦遥感估产模型的构建与调试方法研究.小麦、玉米和水稻遥感估产技术试验研究文集[M].中国科学技术出版社,1993.
[27]杨卫星,薛正平,陆贤等.水稻遥感动力估产模拟初探[J].环境遥感.1994,9(4): 280-286.
[28]高峰.微机大面积水稻遥感信息提取研究[J].环境遥感,1994,9(2):92-99.
[29]吴炳方,刘海燕.水稻种植面积估计的运行化遥感方法[J].遥感学报,1997,1(1):58-64.
[30]千怀遂.中国小麦遥感估产区划研究[J].自然资源学报,1997,12(2):97-104.
[31]杨邦杰,陆登槐,裴志远等.国家级农情监测系统结构设计[J].农业工程学报,1997, 13(1):16-19.
[32]杨邦杰,裴志远,焦险峰等.基于CBERS-1卫星图像的新疆棉花遥感监测技术体系[J].农业工程学报,2003,19(6):146-149.
[33]张建华.作物估产的遥感-数值模拟方法[J].干早区资源与环境,2000,14(2):119-139.
[34]江东,王乃斌,杨小唤等.NDVI曲线与农作物长势的时序互动规律[J].生态学报,2002, 22(2):247-252.
[35]姜城,金继运,张维理.TM遥感与地块内冬小麦产量变异[J].遥感技术与应用,2001, 16(1):23-27.
[36]王长耀,林文鹏.基于MODIS-EVI的冬小麦产量遥感预测研究[J].农业工程学报,2005,21 (10):90-94.
[37]任建强,陈仲新,唐华俊.基于MODIS-NDVI的区域冬小麦遥感估产—以山东省济宁市为例[J].应用生态学报,2006,17(12):2372-2375.
[38]Ray,S.S.Pokharna and Ajai.Cotton yield estimation using agrometeorological model and satellite-deriveds pectral porfile[J].Intenrational Jounral of Remote Sensing,1999,20(14): 2693-2702.
[39]R.E.Plant,D.S.Munk,B.R.Roberts.Relationships between remotely sensed reflectanced data and coton growth and yield[J].Transactions of the ASAE,2000,43(3): 535-546.
[40]Stephan J.Maas.Linear Mixture Modeling Approach for Estimating Cotton Canopy Gorund Cover using Satellite Multi-spectral Imagery[J] .Remote Sensing Enviroment,2000,72: 304-308.
[41]N.R.Daleziosl.Cotton Yield Estimation Based on NOAA/AVHRR Produced NDVI[J]. Phys.Chem.Eurth(B),2001,26(3):247-251.
[42]黎泽文,姜栋,陆登槐登.棉花种植面积遥感估测方法的研究.农业遥感论文集[M].北京:1991.
[43]李明霞,千怀遂.中国棉花遥感估产最佳时相的选择[J].河南大学学报,1997,27(2): 73-78.
[44]张仁华.遥感模型及地面基础[M].北京:科学出版社,1996.
[45]王登伟,曹连莆,李少昆等.北强棉花群体光合特性分析[J].石河子大学学报(自然科学版),1999,3(增刊):31-39.
[46]焦险峰,杨邦杰,裴志远.全国棉花种植面积遥感监测抽样方法设计[J].农业工程学报, 2002,18(4):159-162.
[47]蒋桂英,李鲁华,刁明.高光谱分辨率遥感在新疆棉花上的应用前景[J].中国棉花,2003, 30(2):2-4.
[48]杨邦杰,裴志远,焦险峰等.基于CBERS-1卫星图像的新疆棉花遥感监测技术体系[J].农业工程学报,2003,19(6):146-149.
[49]柏军华,李少昆等.棉花产量遥感预测的L—Y模型构建[J].作物学报,2006,32(6): 840-844.
[50]樊科研,田丽萍,薛琳等.遥感在农作物估产中的应用与发展[J].安徽农学通报,2006, 12(11):145-147.
[51]克拉玛依市人民政府公众信息网.http://www.klmy.gov.cn/.
[52]张春环.克拉玛依大农业开发全面启动.人民网.http://www.people.com.cn/.2001,3.
[53]克拉玛依市统计年鉴.克拉玛依市统计局.
[54]中国棉花科技网.http://www.cricaas.com.cn/.
[55]黄敬峰,王秀珍.新疆棉花物候与气象条件研究[J].干旱区资源与环境.1999,13(2):90-95.
[56]曲辉,陈圣波.中分辨率成像光谱仪数据在地学中的应用前景[J].世界地质,2002, 621(2):176-180.
[57]丁静,唐军武.MODIS水色遥感数据的获取与产品处理综述[J].遥感技术与应用,2003,8, 18(4):263-268.
[58]张旭. EOS/MODIS影像处理及其在塔里木河下游植被变化中的应用[D].新疆大学,2004:12-23.
[59]刘闯,葛成辉.美国对地观测系统(EOS)中分辨率成像光谱仪(MODIS)遥感数据的特点与应用[J].遥感信息.2000,3,44-48.
[60]黄春林,李新.HDF-EOS数据格式在处理空间数据中的应用[J].遥感技术与应用.2001, 16(4):252-259.
[61]张文建.地球观测系统(EOS)中分辨率成像光谱仪(MODIS)—科学意义,仪器介绍和在我国环境遥感中的应用展望[R].EOS/MODIS资料接收应用培训教材,2001.
[62]MODIS Level LA Earth Location: A Lgorithm Theoretical Basis Document [Z].
[63]刘放,程方正.一种新的卫星红外遥感信息数据源EOS/MODIS数据[J].国际地震动态,2003,8(296):1-7.
[64]崔晓军.积温单位浅析[J].科技与出版.2006.4:57.
[65]边疆远.农业气象常用温度术语简介[J].农事气象.2002.12:27.
[66]董永平,吴新宏等.草原遥感监测技术[M].北京:化学工业出版社.2005.
[67]郭铌.植被指数及其研究进展[J].干旱气象.2003,21(4):71-75.
[68]刘蕾.2001-2005年新疆植被覆盖动态变化及原因分析[J].干旱环境监测.2007,21(3): 146-148.
[69]黄敬峰,王秀珍.新疆棉花物候与气候条件研究[J].干旱区资源环境.1999,13(2): 90-95.
[70]吕昭智,李莉等.新疆北部20年棉花物候计算和分析——以炮台镇为例[J].干旱区地理.2003,26(4):340-344.
[71]徐建华.现代地理学中的数学方法[M].高等教育出版社.2002,10第二版.
[72]杨华,王巧莲,勾洪波.昌吉州棉区棉花生育期气候条件分析[J].沙漠与绿洲气象. 2007.6:49-52.
[73]刘双俊,汤建国.气象条件对棉花蕾铃脱落影响的探讨[J].江西棉花.2007.29(5):26-27.
[74]章文波,陈红艳.使用数据统计分析及SPSS12.0应用[M].人民邮电出版社.2006,2.