基于SOC710VP高光谱成像仪的冬小麦土壤含水率反演模型研究
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摘要
【目的】实现小麦农田土壤含水率大面积快速监测。【方法】以冬小麦冠层高光谱数据为基础,计算得到8种植被指数,通过对关键生育时期(拔节期、抽穗期、灌浆期)不同水分处理下冬小麦不同土层(0～20、20～40、40～60 cm)土壤含水率与植被指数拟合状况进行分析和筛选,分别构建了基于植被指数的不同土层土壤含水率反演模型,并对模型进行检验。【结果】①各时期植被指数拟合效果有所差异,拔节期0～20 cm土层以植被指数VOG1拟合效果较好,相关系数为0.88,20～40 cm土层以植被指数mNDVI705拟合效果较好,相关系数为0.75,40～60 cm土层以植被指数VOG3拟合效果较好,相关系数为0.59;抽穗期0～20 cm土层以植被指数mNDVI705拟合效果较好,相关系数为0.70,20～40 cm土层以植被指数mNDVI705拟合效果较好,相关系数为0.72,40～60 cm土层以植被指数mSR705拟合效果较好,相关系数为0.57;灌浆期0～20 cm土层以植被指数mNDVI705拟合效果较好,相关系数为0.88,20～40 cm土层以植被指数SARVI拟合效果较好,相关系数为0.68,40～60 cm土层以植被指数SARVI拟合效果较好,相关系数为0.71;②各土层土壤含水率与植被指数拟合效果有所差异,其中利用VOG1和mNDVI705组合构建的模型反演0～20 cm土层,决定系数R2为0.743,利用mNDVI705和SARVI组合构建的模型反演20～40 cm土层,决定系数R2为0.707,利用VOG3、mSR705和SARVI组合构建的模型反演40～60 cm土层,决定系数R2为0.484;③通过建立植被指数对土壤含水率的反演模型,0～20 cm土层含水率反演效果好于20～40 cm和40～60 cm。【结论】高光谱植被指数反演模型中,以0～20 cm土层的估算模型最佳,植被指数组合为VOG1和mNDVI705。综上可知,该研究方法进行土壤含水率的反演是可行的。
【Objective】Soil water controls crop growth and many soil physical and biochemical processes, and the purpose of this paper is to present how hyperspectral imagery can be used to estimate soil moisture distribution rapidly at large scale.【Method】Based on hyperspectral data of the canopy of winter wheat, we calculated eight vegetation indices and then linked them to soil water content at different depths(0～20, 20～40, 40～60 cm) during key growth stages(jointing stage, heading stage, filling stage) of a winter wheat field.【Result】①The fitting between soil moisture and the vegetation indices varied with growth season. At jointing stage, the indices VOG1,mNDVI705 and VOG3 were superior, whereas at heading and filling stages, mNDVI705 and mSR705, and mNDVI705 and SARVI worked better, respectively. ② The fitting between soil water content and vegetation indices varied with soil depth as well. For the 0～20 cm soil, the model using VOG1 and mNDVI705 gave the best result with the coefficient of determination(R2) being 0.743, while for 20～40 cm soil, the model using mNDVI705 and SARVI was most accurate with R2 being 0.707. For the soil in 40～60 cm, the best vegetation indices for estimating the moisture was VOG3, mSR705 and SARVI, with R2 being 0.484. ③It was found that the fitting of the model for 0～20 cm soil was superior to that for 20～40 cm and 40～60 cm soil.【Conclusion】Using VOG1 and mNDVI705 indices calculated from the hyperspectral imagery can estimate the moisture in 0～20 cm soil reasonably well, and can thus help improve irrigation design and water resource management at regional and catchment scales.
【Objective】Soil water controls crop growth and many soil physical and biochemical processes, and the purpose of this paper is to present how hyperspectral imagery can be used to estimate soil moisture distribution rapidly at large scale.【Method】Based on hyperspectral data of the canopy of winter wheat, we calculated eight vegetation indices and then linked them to soil water content at different depths(0～20, 20～40, 40～60 cm) during key growth stages(jointing stage, heading stage, filling stage) of a winter wheat field.【Result】①The fitting between soil moisture and the vegetation indices varied with growth season. At jointing stage, the indices VOG1,mNDVI705 and VOG3 were superior, whereas at heading and filling stages, mNDVI705 and mSR705, and mNDVI705 and SARVI worked better, respectively. ② The fitting between soil water content and vegetation indices varied with soil depth as well. For the 0～20 cm soil, the model using VOG1 and mNDVI705 gave the best result with the coefficient of determination(R2) being 0.743, while for 20～40 cm soil, the model using mNDVI705 and SARVI was most accurate with R2 being 0.707. For the soil in 40～60 cm, the best vegetation indices for estimating the moisture was VOG3, mSR705 and SARVI, with R2 being 0.484. ③It was found that the fitting of the model for 0～20 cm soil was superior to that for 20～40 cm and 40～60 cm soil.【Conclusion】Using VOG1 and mNDVI705 indices calculated from the hyperspectral imagery can estimate the moisture in 0～20 cm soil reasonably well, and can thus help improve irrigation design and water resource management at regional and catchment scales.
引文
[1]王宏博,李丽光,王笑影,等.基于MODIS数据的土壤含水率监测方法研究综述[J].土壤通报, 2011, 42(1):243-249.
[2]薛利红,罗卫红,曹卫星,等.作物水分和氮素光谱诊断研究进展[J].遥感学报, 2003(1):73-80.
[3] BEN-DOU E, PATIN K, BANIN A, et al. Mapping of several soil properties using DAIS-7915 hyperspectral scanner data a case study over clayey soils in Israel[J]. International Journal of Remote Sensing, 2002, 23(6):1 043-1 062
[4] LU N, ZHANG Z, GAO Y. Recognition and mapping of soil salinization in arid environment with hyperspectral data[J]. Geoscience and Remote Sensing Symposium, 2005, 6:4 520-4 523.
[5]范梅凤,蔡焕杰,李志军.基于高光谱遥感水分指数的叶片与土壤含水率监测研究[J].灌溉排水学报, 2014, 33(Z1):213-217.
[6]白燕英,魏占民,刘全明,等.基于ETM+遥感影像的农田土壤含水率反演研究[J].灌溉排水学报, 2013, 32(4):76-78.
[7] BACH H, VERHOEF W. Sensitivity studies on the effect of surface soil moisture on canopy reflectance using the radiative transfer model GeoSAIL[J].IEEE International Geoscience&Remote Sensing Symposium 2004, 3:1 679-1 681.
[8]姜雪芹,叶勤,林怡,等.基于谐波分析和高光谱遥感的土壤含水量反演研究[J].光学学报, 2017,37(10):300-310.
[9] SANDHOLT I, RASMUSSEN K, ANDERSEN J. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status[J]. Remote Sensing of Environment, 2002, 79(2/3):213-224.
[10]莫伟华,王振会,孙涵,等.基于植被供水指数的农田干旱遥感监测研究[J].南京气象学院学报, 2006(3):396-401.
[11]陈维英,肖乾广,盛永伟.距平植被指数在1992年特大干旱监测中的应用[J].环境遥感, 1994, 9(2):106-112.
[12] KOGAN F N. Remote sensing of weather impacts on vegetation in non-homogeneous areas[J]. International Journal of Remote Sensing, 1990, 11(8):1 405-1 419.
[13]王鹏新,龚健雅,李小文.条件植被温度指数及其在干旱监测中的应用[J].武汉大学学报(信息科学版), 2001(5):412-418.
[14] JACKSON R D, JDSO S B, REGINATO R J, et al. Canopy temperature as a crop water stress indictor[J]. Water Resources Research, 1981, 17(4):1 133-1 138.
[15]李相,丁建丽.基于实测高光谱指数与HSI影像指数的土壤含水率监测[J].农业工程学报, 2015, 31(19):68-75.
[16]杨淑婷,张学俭.宁南山区土壤含水量遥感监测研究[J].宁夏农林科技,2015,56(10):26-28.
[17]汪沛,李就好,周志艳.不同土壤水分状况下甘蔗冠层光谱特征研究[J].水资源与水工程学报, 2010, 21(1):34-37.
[18]王宏博,冯锐,纪瑞鹏,等.干旱胁迫下春玉米拔节-吐丝期高光谱特征[J].光谱学与光谱分析, 2012, 32(12):3 358-3 362.
[19]林毅,李倩,王宏博,等.干旱条件下春玉米高光谱特征及土壤含水率反演[J].生态学杂志, 2016, 35(5):1 323-1 329.
[20]王纯枝,毛留喜,何延波,等.温度植被干旱指数法(TVDI)在黄淮海平原土壤湿度反演中的应用研究[J].土壤通报, 2009, 40(5):998-1 005.
[21]孙丽,王飞,吴全.干旱遥感监测模型在中国冬小麦区的应用[J].农业工程学报, 2010, 26(1):243-249.
[22]李艳,王鹏新,刘峻明,等.基于条件植被温度指数的冬小麦主要生育时期干旱监测效果评价:Ⅱ.改进的层次分析法和变异系数法组合赋权[J].干旱地区农业研究, 2014, 32(01):236-239.
[23] LIU H Q, HUETE A R. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise[J]. IEEE Transactions on Geoscience&Remote Sensing, 1995, 33(2):457-465.
[24] MAJOR D J, BARET F, GUYOT G. A ratio vegetation index adjusted for soil brightness[J]. International Journal of Remote Sensing, 1990, 11:727-740.
[25] KAUFMAN Y J, TANRE D. Atmospherically resistant vegetation index(ARVI)for EOS-MODIS[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30(2):261-270.
[26] SIMS D A, GAMON J A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages[J]. Remote Sensing of Environment, 2002, 81(2):337-354.
[27] VOGELMANN J E, ROCK B N, MOSS D M. Red edge spectral measurements from sugar maple leaves[J]. International Journal of Remote Sensing,1993, 14(8):1 563-1 575.
[28]王春梅,王鹏新,朱向明,等.区域蒸散和表层土壤含水量遥感模拟及影响因子[J].农业工程学报, 2008, 24(10):127-133.
[29] MATSUI T, OMASA K, HORIE T. The difference in sterility due to high temperatures during the flowering period among japonica rice varieties[J]. Plant Production Science-Tokyo, 2001, 4(2):90-93.
[30]李淑文,于淼,杜建云,等.不同灌水处理下土壤水分动态及玉米水分利用效率研究[J].河北农业大学学报, 2010, 33(4):17-21.
[31]王晓迪,王春堂,侯贺贺,等.管道均匀移动精准灌溉对夏玉米土壤水分变化及水分利用效率的影响研究[J].节水灌溉, 2014(9):22-26.
[32]李洪建,王孟本,柴宝峰.黄土高原土壤水分变化的时空特征分析[J].应用生态学报,2003, 14(4):515-519.
[33]冯晓钰,周广胜.夏玉米叶片水分变化与光合作用和土壤水分的关系[J].生态学报, 2018(1):1-9.
[34]陆红飞,郭相平,王振昌,等.分蘖期旱涝交替胁迫对水稻叶片性状的影响[J].灌溉排水学报, 2017, 36(1):47-51.
[35]郭相平,王甫,王振昌,等.不同灌溉模式对水稻抽穗后叶绿素荧光特征及产量的影响[J].灌溉排水学报, 2017, 36(3):1-6.
[36]陈歆,刘贝贝,彭黎旭.土壤水分对槟榔幼苗净光合速率和蒸腾速率的影响[J].热带作物学报, 2015, 36(11):2034-2038.
[37]袁宏伟,蒋尚明,汤广民,等.淮北平原冬小麦蒸发蒸腾量与不同土层土壤含水率关系初探[J].灌溉排水学报, 2016, 35(2):86-89.
[38] HUETE A R. A soil adjusted vegetation index(SAVI)[J]. Remote Sensing Environment, 1988, 25:295-309.
[39] COLLINS W, CHANG S H, RAINES G, et al. Airborne biogeophysical mapping of hidden mineral deposits[J]. Economic Geology, 1983, 78:737-749.
[40] CHANG S H, COLLINS W. Confirmation of the airborne biogeophysical mineral exploration technique using laboratory methods[J]. Economic Geology,1983, 78:723-736.
[41]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.
[42]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.
[43]向红英,牛建龙,彭杰,等.棉田土壤水分的高光谱定量遥感模型[J].土壤通报, 2016, 47(2):272-277.
[2]薛利红,罗卫红,曹卫星,等.作物水分和氮素光谱诊断研究进展[J].遥感学报, 2003(1):73-80.
[3] BEN-DOU E, PATIN K, BANIN A, et al. Mapping of several soil properties using DAIS-7915 hyperspectral scanner data a case study over clayey soils in Israel[J]. International Journal of Remote Sensing, 2002, 23(6):1 043-1 062
[4] LU N, ZHANG Z, GAO Y. Recognition and mapping of soil salinization in arid environment with hyperspectral data[J]. Geoscience and Remote Sensing Symposium, 2005, 6:4 520-4 523.
[5]范梅凤,蔡焕杰,李志军.基于高光谱遥感水分指数的叶片与土壤含水率监测研究[J].灌溉排水学报, 2014, 33(Z1):213-217.
[6]白燕英,魏占民,刘全明,等.基于ETM+遥感影像的农田土壤含水率反演研究[J].灌溉排水学报, 2013, 32(4):76-78.
[7] BACH H, VERHOEF W. Sensitivity studies on the effect of surface soil moisture on canopy reflectance using the radiative transfer model GeoSAIL[J].IEEE International Geoscience&Remote Sensing Symposium 2004, 3:1 679-1 681.
[8]姜雪芹,叶勤,林怡,等.基于谐波分析和高光谱遥感的土壤含水量反演研究[J].光学学报, 2017,37(10):300-310.
[9] SANDHOLT I, RASMUSSEN K, ANDERSEN J. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status[J]. Remote Sensing of Environment, 2002, 79(2/3):213-224.
[10]莫伟华,王振会,孙涵,等.基于植被供水指数的农田干旱遥感监测研究[J].南京气象学院学报, 2006(3):396-401.
[11]陈维英,肖乾广,盛永伟.距平植被指数在1992年特大干旱监测中的应用[J].环境遥感, 1994, 9(2):106-112.
[12] KOGAN F N. Remote sensing of weather impacts on vegetation in non-homogeneous areas[J]. International Journal of Remote Sensing, 1990, 11(8):1 405-1 419.
[13]王鹏新,龚健雅,李小文.条件植被温度指数及其在干旱监测中的应用[J].武汉大学学报(信息科学版), 2001(5):412-418.
[14] JACKSON R D, JDSO S B, REGINATO R J, et al. Canopy temperature as a crop water stress indictor[J]. Water Resources Research, 1981, 17(4):1 133-1 138.
[15]李相,丁建丽.基于实测高光谱指数与HSI影像指数的土壤含水率监测[J].农业工程学报, 2015, 31(19):68-75.
[16]杨淑婷,张学俭.宁南山区土壤含水量遥感监测研究[J].宁夏农林科技,2015,56(10):26-28.
[17]汪沛,李就好,周志艳.不同土壤水分状况下甘蔗冠层光谱特征研究[J].水资源与水工程学报, 2010, 21(1):34-37.
[18]王宏博,冯锐,纪瑞鹏,等.干旱胁迫下春玉米拔节-吐丝期高光谱特征[J].光谱学与光谱分析, 2012, 32(12):3 358-3 362.
[19]林毅,李倩,王宏博,等.干旱条件下春玉米高光谱特征及土壤含水率反演[J].生态学杂志, 2016, 35(5):1 323-1 329.
[20]王纯枝,毛留喜,何延波,等.温度植被干旱指数法(TVDI)在黄淮海平原土壤湿度反演中的应用研究[J].土壤通报, 2009, 40(5):998-1 005.
[21]孙丽,王飞,吴全.干旱遥感监测模型在中国冬小麦区的应用[J].农业工程学报, 2010, 26(1):243-249.
[22]李艳,王鹏新,刘峻明,等.基于条件植被温度指数的冬小麦主要生育时期干旱监测效果评价:Ⅱ.改进的层次分析法和变异系数法组合赋权[J].干旱地区农业研究, 2014, 32(01):236-239.
[23] LIU H Q, HUETE A R. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise[J]. IEEE Transactions on Geoscience&Remote Sensing, 1995, 33(2):457-465.
[24] MAJOR D J, BARET F, GUYOT G. A ratio vegetation index adjusted for soil brightness[J]. International Journal of Remote Sensing, 1990, 11:727-740.
[25] KAUFMAN Y J, TANRE D. Atmospherically resistant vegetation index(ARVI)for EOS-MODIS[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30(2):261-270.
[26] SIMS D A, GAMON J A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages[J]. Remote Sensing of Environment, 2002, 81(2):337-354.
[27] VOGELMANN J E, ROCK B N, MOSS D M. Red edge spectral measurements from sugar maple leaves[J]. International Journal of Remote Sensing,1993, 14(8):1 563-1 575.
[28]王春梅,王鹏新,朱向明,等.区域蒸散和表层土壤含水量遥感模拟及影响因子[J].农业工程学报, 2008, 24(10):127-133.
[29] MATSUI T, OMASA K, HORIE T. The difference in sterility due to high temperatures during the flowering period among japonica rice varieties[J]. Plant Production Science-Tokyo, 2001, 4(2):90-93.
[30]李淑文,于淼,杜建云,等.不同灌水处理下土壤水分动态及玉米水分利用效率研究[J].河北农业大学学报, 2010, 33(4):17-21.
[31]王晓迪,王春堂,侯贺贺,等.管道均匀移动精准灌溉对夏玉米土壤水分变化及水分利用效率的影响研究[J].节水灌溉, 2014(9):22-26.
[32]李洪建,王孟本,柴宝峰.黄土高原土壤水分变化的时空特征分析[J].应用生态学报,2003, 14(4):515-519.
[33]冯晓钰,周广胜.夏玉米叶片水分变化与光合作用和土壤水分的关系[J].生态学报, 2018(1):1-9.
[34]陆红飞,郭相平,王振昌,等.分蘖期旱涝交替胁迫对水稻叶片性状的影响[J].灌溉排水学报, 2017, 36(1):47-51.
[35]郭相平,王甫,王振昌,等.不同灌溉模式对水稻抽穗后叶绿素荧光特征及产量的影响[J].灌溉排水学报, 2017, 36(3):1-6.
[36]陈歆,刘贝贝,彭黎旭.土壤水分对槟榔幼苗净光合速率和蒸腾速率的影响[J].热带作物学报, 2015, 36(11):2034-2038.
[37]袁宏伟,蒋尚明,汤广民,等.淮北平原冬小麦蒸发蒸腾量与不同土层土壤含水率关系初探[J].灌溉排水学报, 2016, 35(2):86-89.
[38] HUETE A R. A soil adjusted vegetation index(SAVI)[J]. Remote Sensing Environment, 1988, 25:295-309.
[39] COLLINS W, CHANG S H, RAINES G, et al. Airborne biogeophysical mapping of hidden mineral deposits[J]. Economic Geology, 1983, 78:737-749.
[40] CHANG S H, COLLINS W. Confirmation of the airborne biogeophysical mineral exploration technique using laboratory methods[J]. Economic Geology,1983, 78:723-736.
[41]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.
[42]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.
[43]向红英,牛建龙,彭杰,等.棉田土壤水分的高光谱定量遥感模型[J].土壤通报, 2016, 47(2):272-277.