农田灌溉遥感监测技术的发展与前景
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摘要
农田灌溉遥感监测旨在通过遥感技术监测灌区作物的灌溉面积和种植结构,来实施高效的灌溉用水管理,达到建设节水型农业和对灌区现代化管理的目的。科学客观地总结农田灌溉面积遥感提取相关的研究进展,能为开展该方面的研究提供一定的参考。通过系统全面的查阅国内外相关文献资料,总结当前研究所采用的原理与方法,具体操作过程,存在问题等,并对未来发展趋势进行了展望。值得肯定的是,目前已具有初步的理论和探索,对局部和区域农田灌溉面积的遥感提取具有了一定成效。不足之处在于,基于灌溉前后土壤水分和温度变化的原理与方法受降水影响大;地表实测数据收集困难;数据源单一,高"时空"分辨率数据少。在未来研究中,农田灌溉面积的多时相动态提取是趋势所在。还应注重以下几个方面:①提取原理和方法需进一步完善和补充;②注重多源数据和多种因素整合分析;③注重灌区基础资料库的建立和更新,注重大数据技术的应用。
Remote sensing monitor of farmland irrigation aims to monitor the irrigated area and planting structure of irrigation areas through remote sensing technology to implement efficient irrigation water management, and achieve the purpose of building water-saving agriculture and modern management of irrigation areas. A scientific and objective summary of the research progress of remote sensing extraction of farmland irrigation area can provide some reference for the research in this field. Through a systematic and comprehensive review of relevant literature at home and abroad, this paper summarizes the current research principles and methods, specific operation process, existing problems, etc., and looks forward to the future development trend. It is worth affirming that there have been preliminary theories and explorations, which have achieved certain results in remote sensing extraction of local and regional farmland irrigation areas. The disadvantage is that the principle and method of soil moisture and temperature change before and after irrigation are greatly affected by precipitation; the collection of surface measured data is difficult; data sources are single, and high "time and space" resolution data are less. In future studies, the multi-temporal dynamic extraction of irrigated farmland area is the trend. The following aspects should also be emphasized: ①The principles and methods of extraction need to be further improved and supplemented; ②Pay attention to multi-source data and multiple factors integration analysis; ③pay attention to the establishment and update of basic database of irrigation area, pay attention to the application of big data technology.
Remote sensing monitor of farmland irrigation aims to monitor the irrigated area and planting structure of irrigation areas through remote sensing technology to implement efficient irrigation water management, and achieve the purpose of building water-saving agriculture and modern management of irrigation areas. A scientific and objective summary of the research progress of remote sensing extraction of farmland irrigation area can provide some reference for the research in this field. Through a systematic and comprehensive review of relevant literature at home and abroad, this paper summarizes the current research principles and methods, specific operation process, existing problems, etc., and looks forward to the future development trend. It is worth affirming that there have been preliminary theories and explorations, which have achieved certain results in remote sensing extraction of local and regional farmland irrigation areas. The disadvantage is that the principle and method of soil moisture and temperature change before and after irrigation are greatly affected by precipitation; the collection of surface measured data is difficult; data sources are single, and high "time and space" resolution data are less. In future studies, the multi-temporal dynamic extraction of irrigated farmland area is the trend. The following aspects should also be emphasized: ①The principles and methods of extraction need to be further improved and supplemented; ②Pay attention to multi-source data and multiple factors integration analysis; ③pay attention to the establishment and update of basic database of irrigation area, pay attention to the application of big data technology.
引文
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[4] 沈静. 遥感技术在灌溉面积监测上的应用研究[D]. 大连:大连理工大学, 2012.
[5] 易珍言, 赵红莉, 蒋云钟, 等. 遥感技术在河套灌区灌溉管理中的应用研究[J]. 南水北调与水利科技, 2014(5):166-169.
[6] 王啸天, 路京选. 基于垂直干旱指数(PDI)的灌区实际灌溉面积遥感监测方法[J]. 南水北调与水利科技, 2016,14(3):169-174.
[7] Jakubauskas M E, Legates D R, Kastens J H. Crop identification using harmonic analysis of time-series AVHRR NDVI data[J]. Computers and Electronics in Agriculture, 2002,37(1/2/3):127-139.
[8] Thenkabail P S, Biradar C M, Noojipady P, et al. Global irrigated area map (GIAM), derived from remote sensing for the end of the last millennium[J]. Remote Sensing. 2009,30(14):3 679-3 733.
[9] Thenkabail P S, Dheeravath V, Biradar C M, et al. Irrigated Area Maps and Statistics of India Using Remote Sensing and National Statistics[J]. Remote Sensing, 2009,1(2):50-67.
[10] Inge Sandholt, Kjeld Rasmussen, Jens Andersen. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status[J]. Remote Sensing of Environment, 2002,(79):213-224.
[11] N M Velpuri, P S Thenkabail, M K Gumma, et al. Influence of Resolution in Irrigated Area Mapping and Area Estimation[J]. Photogrammetric Engineering Remote Sensing, 2009,75(12):1 383-1 395.
[12] Biggs T W, Thenkabail P S, Gumma M K, et al. Irrigated area mapping in heterogeneous landscapes with MODIS time series, ground truth and census data, Krishna Basin, India[J]. International Journal of Remote Sensing, 2006,27(19):4 245-4 266.
[13] Sim?es S J C, Júnior N S P. Spatial evolution of irrigated areas using remote sensing-the Medium Paraíba do Sul Valley, Southeast of Brazil[J]. Ambiente e água: An Interdisciplinary Journal of Applied Science, 2007,1(1).
[14] 陈子丹, 李纪人, 夏夫川. 有效灌溉面积遥感调查方法研究与应用[J]. 遥感信息, 1997,(2):19-24.
[15] 王薇, 朱长明. 基于多源遥感数据的大型灌区面积监测方法研究[J]. 中国水利, 2009,(16):47-48.
[16] Xiufang Zhu, Wenquan Zhu, Jinshui Zhang, et al. Mapping irrigated areas in China from remote sensing and statistical data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014,7(11):4 490-4 504.
[17] 张洁, 武建军, 周磊, 等. 基于MODIS数据的农业干旱监测方法对比分析[J]. 遥感信息, 2012,27(5):48-54.
[18] 邸兰杰. 基于TVDI和ATI模型河北省土壤湿度遥感反演[D]. 石家庄:河北师范大学, 2014.
[19] 蒋磊, 杨雨亭, 尚松浩. 基于遥感蒸发模型的干旱区灌区溉效率评价[J]. 农业工程学报, 2013,29(20):95-101.
[20] 何娇娇, 刘海新, 张安兵, 等. 温度反演和植被供水指数的农田灌溉面积提取[J]. 测绘科学, 2017,42(5):50-55.
[21] 刘海启, 金敏毓, 龚维鹏. 美国农业遥感技术应用状况概述[J]. 中国农业资源与区划, 1999,(2):58-62.
[22] M J Pringle, R J Denham, R. Identification of cropping activity in central and southern Queensland, Australia, with the aid of MODIS MOD13Q1 imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2012,(19): 276-285.
[23] Potgieter A B, Lawson K, Huete A R. Determining crop acreage estimates for specific winter crops using shape attributes from sequential MODIS imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2013,23(8):254-263.
[24] Thenkabail P S, Wu Z. An automated cropland classification algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data[J]. Remote Sensing, 2012,4(10):2 890-2 918.
[25] Lloyd D. A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery[J]. International Journal of Remote Sensing, 1990,11(12):2 269-2 279.
[26] Atzberger C, Rembold F. Mapping the spatial distribution of winter crops at sub-pixel level using AVHRR NDVI Time series and neural nets[J]. Remote Sensing, 2013, 5(3):1 335-1 354.
[27] Monfreda C, Ramankutty N, Foley J A. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000[J]. Global Biogeochemical Cycles, 2008,22(1):10.
[28] 夏德深, 李华. 国外灾害遥感应用研究现状[J]. 国土资源遥感, 1996,29(3):1-8.
[29] 熊利亚. 中国农作物遥感动态监测与估产集成系统区[M]. 北京: 中国科学技术出版社, 1996.
[30] 陈沈斌, 孙九林. 建立我国主要农作物卫星遥感估产运行系统的主要技术环节及解决途径[J]. 自然资源学报, 1997,12(4):363-369.
[31] 郑长春, 王秀珍, 黄敬峰. 基于特征波段的Spot-5卫星影像水稻面积信息自动提取的方法研究[J]. 遥感技术与应用, 2008,(3):294-299.
[32] 张霞, 焦全军, 张兵, 等. 利用MODIS_EVI图像时间序列提取作物种植模式初探[J]. 农业工程学报, 2008,(5):161-165.
[33] 乌云. 基于不同时相TM数据的森林植被类型提取研究[D]. 呼和浩特: 内蒙古农业大学, 2014.
[34] 李鑫川, 徐新刚, 王纪华, 等. 基于时间序列环境卫星影像的作物分类识别[J].农业工程学报, 2013,29(2):169-176.
[35] 谢登峰, 张锦水, 潘耀忠, 等. Landsat8和MODIS融合构建高时空分辨率数据识别秋粮作物[J]. 遥感学报, 2015,19(5):791-805.
[36] Zhang Mingwei, Zhou Qingbo, Chen Zhongxin, et al. Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data[J]. International Journal of Applied Earth Observation and Geoinformation, 2008,10:476-485.
[37] Brian D Wardlow, Stephen L Egbert. Large-area crop mapping using time-series MODIS 250m NDVI data: An assessment for the U. S. Central Great Plains[J]. Remote Sensing of Environment, 2008,112:1 096-1 116.
[38] Doraiswamy P C, Sinclair T R, Hollinger S, et al. Application of MODIS derived parameters for regional crop yield assessment[J]. Remote Sensing of Environment, 2005, 97: 192-202.
[39] E A Zaghloul, S M Hassan, A M Bahy, et al. Detection of Ancient Irrigation Canals of Deir El-Hagar Playa, Dakhla Oasis, Egypt, Using Egyptsat-1 Data[J]. The Egyptian Journal of Remote Sensing and Space Sciences, 2013,16:153-161.
[40] 徐美, 黄诗峰, 姚永慧, 等. 干旱半干旱地区灌溉农业中的遥感应用[J]. 干旱区研究, 2006,23(4):592-597.
[41] 柯丽娟. 遥感反演土壤含水量在灌溉用水管理中的应用研究[D]. 兰州: 兰州交通大学, 2015.
[42] 焦旭. 石津灌区种植结构与灌溉面积信息提取[D]. 邯郸: 河北工程大学, 2016.
[43] 李喆, 谭德宝, 崔远来, 等. 基于PDI的湖北漳河灌区土壤含水量遥感监测[J]. 人民长江, 2010,41(1):92-95.
[44] Shi Y, Ji S P, Shao X W, Tang H J, Wu W B, Yang P, Zhang Y J and Shihasaki R. Framework of SAGI agriculture remote sensing and its perspectives in supporting national food security[J]. Journal of Integrative Agriculture, 2014,13(7):1 443-1 450.
[45] 肖广金. 遥感技术在农业节水灌溉工作中的应用[J]. 水利科技与经济, 2016,22(4):90-91.
[46] 孙家柄. 遥感原理与应用[M]. 武汉:武汉大学出版社, 2010:7-2.
[47] 赵文生. Landsat-5 TM遥感影像辐射定标系统的设计实现[J]. 科研, 2016,(1):219-222.
[48] 郑伟, 曾志远. 遥感图像大气校正方法综述[J]. 遥感信息, 2004,(4):66-70.
[2] 程晓冰, 石玉波, 蒋云钟, 等. 推进水资源信息化建设落实最严格的水资源管理制度[J]. 水利信息化, 2010(2):1-4.
[3] 万玉文, 苏超, 方崇. 我国大中型灌区有效灌溉面积的灰色预测[J]. 人民长江, 2011(15):96-98.
[4] 沈静. 遥感技术在灌溉面积监测上的应用研究[D]. 大连:大连理工大学, 2012.
[5] 易珍言, 赵红莉, 蒋云钟, 等. 遥感技术在河套灌区灌溉管理中的应用研究[J]. 南水北调与水利科技, 2014(5):166-169.
[6] 王啸天, 路京选. 基于垂直干旱指数(PDI)的灌区实际灌溉面积遥感监测方法[J]. 南水北调与水利科技, 2016,14(3):169-174.
[7] Jakubauskas M E, Legates D R, Kastens J H. Crop identification using harmonic analysis of time-series AVHRR NDVI data[J]. Computers and Electronics in Agriculture, 2002,37(1/2/3):127-139.
[8] Thenkabail P S, Biradar C M, Noojipady P, et al. Global irrigated area map (GIAM), derived from remote sensing for the end of the last millennium[J]. Remote Sensing. 2009,30(14):3 679-3 733.
[9] Thenkabail P S, Dheeravath V, Biradar C M, et al. Irrigated Area Maps and Statistics of India Using Remote Sensing and National Statistics[J]. Remote Sensing, 2009,1(2):50-67.
[10] Inge Sandholt, Kjeld Rasmussen, Jens Andersen. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status[J]. Remote Sensing of Environment, 2002,(79):213-224.
[11] N M Velpuri, P S Thenkabail, M K Gumma, et al. Influence of Resolution in Irrigated Area Mapping and Area Estimation[J]. Photogrammetric Engineering Remote Sensing, 2009,75(12):1 383-1 395.
[12] Biggs T W, Thenkabail P S, Gumma M K, et al. Irrigated area mapping in heterogeneous landscapes with MODIS time series, ground truth and census data, Krishna Basin, India[J]. International Journal of Remote Sensing, 2006,27(19):4 245-4 266.
[13] Sim?es S J C, Júnior N S P. Spatial evolution of irrigated areas using remote sensing-the Medium Paraíba do Sul Valley, Southeast of Brazil[J]. Ambiente e água: An Interdisciplinary Journal of Applied Science, 2007,1(1).
[14] 陈子丹, 李纪人, 夏夫川. 有效灌溉面积遥感调查方法研究与应用[J]. 遥感信息, 1997,(2):19-24.
[15] 王薇, 朱长明. 基于多源遥感数据的大型灌区面积监测方法研究[J]. 中国水利, 2009,(16):47-48.
[16] Xiufang Zhu, Wenquan Zhu, Jinshui Zhang, et al. Mapping irrigated areas in China from remote sensing and statistical data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014,7(11):4 490-4 504.
[17] 张洁, 武建军, 周磊, 等. 基于MODIS数据的农业干旱监测方法对比分析[J]. 遥感信息, 2012,27(5):48-54.
[18] 邸兰杰. 基于TVDI和ATI模型河北省土壤湿度遥感反演[D]. 石家庄:河北师范大学, 2014.
[19] 蒋磊, 杨雨亭, 尚松浩. 基于遥感蒸发模型的干旱区灌区溉效率评价[J]. 农业工程学报, 2013,29(20):95-101.
[20] 何娇娇, 刘海新, 张安兵, 等. 温度反演和植被供水指数的农田灌溉面积提取[J]. 测绘科学, 2017,42(5):50-55.
[21] 刘海启, 金敏毓, 龚维鹏. 美国农业遥感技术应用状况概述[J]. 中国农业资源与区划, 1999,(2):58-62.
[22] M J Pringle, R J Denham, R. Identification of cropping activity in central and southern Queensland, Australia, with the aid of MODIS MOD13Q1 imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2012,(19): 276-285.
[23] Potgieter A B, Lawson K, Huete A R. Determining crop acreage estimates for specific winter crops using shape attributes from sequential MODIS imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2013,23(8):254-263.
[24] Thenkabail P S, Wu Z. An automated cropland classification algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data[J]. Remote Sensing, 2012,4(10):2 890-2 918.
[25] Lloyd D. A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery[J]. International Journal of Remote Sensing, 1990,11(12):2 269-2 279.
[26] Atzberger C, Rembold F. Mapping the spatial distribution of winter crops at sub-pixel level using AVHRR NDVI Time series and neural nets[J]. Remote Sensing, 2013, 5(3):1 335-1 354.
[27] Monfreda C, Ramankutty N, Foley J A. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000[J]. Global Biogeochemical Cycles, 2008,22(1):10.
[28] 夏德深, 李华. 国外灾害遥感应用研究现状[J]. 国土资源遥感, 1996,29(3):1-8.
[29] 熊利亚. 中国农作物遥感动态监测与估产集成系统区[M]. 北京: 中国科学技术出版社, 1996.
[30] 陈沈斌, 孙九林. 建立我国主要农作物卫星遥感估产运行系统的主要技术环节及解决途径[J]. 自然资源学报, 1997,12(4):363-369.
[31] 郑长春, 王秀珍, 黄敬峰. 基于特征波段的Spot-5卫星影像水稻面积信息自动提取的方法研究[J]. 遥感技术与应用, 2008,(3):294-299.
[32] 张霞, 焦全军, 张兵, 等. 利用MODIS_EVI图像时间序列提取作物种植模式初探[J]. 农业工程学报, 2008,(5):161-165.
[33] 乌云. 基于不同时相TM数据的森林植被类型提取研究[D]. 呼和浩特: 内蒙古农业大学, 2014.
[34] 李鑫川, 徐新刚, 王纪华, 等. 基于时间序列环境卫星影像的作物分类识别[J].农业工程学报, 2013,29(2):169-176.
[35] 谢登峰, 张锦水, 潘耀忠, 等. Landsat8和MODIS融合构建高时空分辨率数据识别秋粮作物[J]. 遥感学报, 2015,19(5):791-805.
[36] Zhang Mingwei, Zhou Qingbo, Chen Zhongxin, et al. Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data[J]. International Journal of Applied Earth Observation and Geoinformation, 2008,10:476-485.
[37] Brian D Wardlow, Stephen L Egbert. Large-area crop mapping using time-series MODIS 250m NDVI data: An assessment for the U. S. Central Great Plains[J]. Remote Sensing of Environment, 2008,112:1 096-1 116.
[38] Doraiswamy P C, Sinclair T R, Hollinger S, et al. Application of MODIS derived parameters for regional crop yield assessment[J]. Remote Sensing of Environment, 2005, 97: 192-202.
[39] E A Zaghloul, S M Hassan, A M Bahy, et al. Detection of Ancient Irrigation Canals of Deir El-Hagar Playa, Dakhla Oasis, Egypt, Using Egyptsat-1 Data[J]. The Egyptian Journal of Remote Sensing and Space Sciences, 2013,16:153-161.
[40] 徐美, 黄诗峰, 姚永慧, 等. 干旱半干旱地区灌溉农业中的遥感应用[J]. 干旱区研究, 2006,23(4):592-597.
[41] 柯丽娟. 遥感反演土壤含水量在灌溉用水管理中的应用研究[D]. 兰州: 兰州交通大学, 2015.
[42] 焦旭. 石津灌区种植结构与灌溉面积信息提取[D]. 邯郸: 河北工程大学, 2016.
[43] 李喆, 谭德宝, 崔远来, 等. 基于PDI的湖北漳河灌区土壤含水量遥感监测[J]. 人民长江, 2010,41(1):92-95.
[44] Shi Y, Ji S P, Shao X W, Tang H J, Wu W B, Yang P, Zhang Y J and Shihasaki R. Framework of SAGI agriculture remote sensing and its perspectives in supporting national food security[J]. Journal of Integrative Agriculture, 2014,13(7):1 443-1 450.
[45] 肖广金. 遥感技术在农业节水灌溉工作中的应用[J]. 水利科技与经济, 2016,22(4):90-91.
[46] 孙家柄. 遥感原理与应用[M]. 武汉:武汉大学出版社, 2010:7-2.
[47] 赵文生. Landsat-5 TM遥感影像辐射定标系统的设计实现[J]. 科研, 2016,(1):219-222.
[48] 郑伟, 曾志远. 遥感图像大气校正方法综述[J]. 遥感信息, 2004,(4):66-70.