Chlorophyll Content Retrieval of Rice Canopy with Multi-spectral Inversion Based on LS-SVR Algorithm
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摘要
To monitor growth and predict the yield of rice over a large area, the chlorophyll contents in the rice canopy were estimated using the unmanned aerial vehicle(UAV) remote sensing technology. In this work, multi-spectral image information of the rice crop was obtained using a 6-channel multi-spectral camera mounted on a fixed wing UAV, which was flown 600 m above the ground, between 11: 00-14: 00 on a sunny day in summer. The measured chlorophyll values were collected as sample sets. The s-REP index was screened out to estimate chlorophyll contents through the analysis of six kinds of spectral indexes of chlorophyll estimated capacity. An inversion model of the chlorophyll contents was then built using the least square support vector regression(LS-SVR)algorithm, with calibration and prediction R-square values of 0.89 and 0.83, respectively. Finally, remote sensing mapping for a UAV image of the Fangzheng County Dexter Rice Planting Park was accomplished using the inversion model. The inversion and measured values were then compared using regression fitting. R-square and root-mean-square error of the fitting model were 0.79 and 2.39,respectively. The results demonstrated that accurate estimation of rice-canopy chlorophyll contents was feasible using the LS-SVR inversion model developed using the s-REP vegetation index.
To monitor growth and predict the yield of rice over a large area, the chlorophyll contents in the rice canopy were estimated using the unmanned aerial vehicle(UAV) remote sensing technology. In this work, multi-spectral image information of the rice crop was obtained using a 6-channel multi-spectral camera mounted on a fixed wing UAV, which was flown 600 m above the ground, between 11: 00-14: 00 on a sunny day in summer. The measured chlorophyll values were collected as sample sets. The s-REP index was screened out to estimate chlorophyll contents through the analysis of six kinds of spectral indexes of chlorophyll estimated capacity. An inversion model of the chlorophyll contents was then built using the least square support vector regression(LS-SVR)algorithm, with calibration and prediction R-square values of 0.89 and 0.83, respectively. Finally, remote sensing mapping for a UAV image of the Fangzheng County Dexter Rice Planting Park was accomplished using the inversion model. The inversion and measured values were then compared using regression fitting. R-square and root-mean-square error of the fitting model were 0.79 and 2.39,respectively. The results demonstrated that accurate estimation of rice-canopy chlorophyll contents was feasible using the LS-SVR inversion model developed using the s-REP vegetation index.
To monitor growth and predict the yield of rice over a large area, the chlorophyll contents in the rice canopy were estimated using the unmanned aerial vehicle(UAV) remote sensing technology. In this work, multi-spectral image information of the rice crop was obtained using a 6-channel multi-spectral camera mounted on a fixed wing UAV, which was flown 600 m above the ground, between 11: 00-14: 00 on a sunny day in summer. The measured chlorophyll values were collected as sample sets. The s-REP index was screened out to estimate chlorophyll contents through the analysis of six kinds of spectral indexes of chlorophyll estimated capacity. An inversion model of the chlorophyll contents was then built using the least square support vector regression(LS-SVR)algorithm, with calibration and prediction R-square values of 0.89 and 0.83, respectively. Finally, remote sensing mapping for a UAV image of the Fangzheng County Dexter Rice Planting Park was accomplished using the inversion model. The inversion and measured values were then compared using regression fitting. R-square and root-mean-square error of the fitting model were 0.79 and 2.39,respectively. The results demonstrated that accurate estimation of rice-canopy chlorophyll contents was feasible using the LS-SVR inversion model developed using the s-REP vegetation index.
引文
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Chen X L,Chen C,Zhou L,et al.2010.Determination and correlativity analysis of chloropgyll content at different development stages in rice.Modern Agricultural Science and Technology,17:42-44.
Clevers J G P W.1994.Imaging spectrometry in agriculture-plant vitality and yield indicators:imaging spectrometry-a tool for environmental observations.Springer Netherlands.pp.193-219.
Curran P J,Dungan J L,Gholz,H L.1990.Exploring the relationship between reflectance red edge and chlorophyll content in slash pine.Tree Physiology,7(1/2/3/4):33-48.
Daughtry C S T,Wahhall C L,Kim M S,et al.2000.Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance.Remote Sensing of Environment,74(2):229-239.
Gitelson A A,Merzlyak M N.1996.Signature analysis of leaf reflectance spectra:algorithm development for remote sensing of chlorophyll.Journal of Plant Physiology,148(3/4):494-500.
Gu Y P,Zhao W J,Wu Z S.2010.Least squares support vector machine algorithm.J Tsinghua Univ(Sci&Tech),7:1063-1066.
Haboudane D,Miller J R,Tremblay N,et al.2002.Integrated narrowband vegetation indices for prediction of crop chlorophyll content for application to precision agriculture.Remote Sensing of Environment,81(2/3):416-426.
Filella I,Penuelas J.2007.The red edge position and shape as indicators of plant chlorophyll content,biomass and hydric status.International Journal of Remote Sensing,15(7):1459-1470.
Kong W P,Bi Y L,Li S P,et al.2014.Hyperspectral estimation of leaf chlorophyll content in mycorrhizal inoculated soybean under drought stress.Transactions of the Chinese Society of Agricultural Engineering,30(12):123-131.
Liang L,Yang M,Zhang L.2012.Chlorophyll content inversion with hyperspectral technology for wheat canopy based on support vector regression algorithm.Transactions of the Chinese Society of Agricultural Engineering,10:162-171.
Luan Q Z,Liu H P,Xiao Z Q.2007.Comparison between algorithms of ortho-rectification for remote sensing images.Remote Sensing Technology and Application,22(6):743-747.
Pearson R L,Miller L D.1972.Remote mapping of standing crop biomass for estimation of the productivity of the short grass prairie.Proceeding of the 8th International Symposium on Remote Sensing of the Environment,45(2):7-12.
Peng Y,Gitelson A A.2012.Remote estimation of gross primary productivity in soybean and maize based on total crop chlorophyll content.Remote Sensing of Environment,117(2):440-448.
Penuelas J,Gamon J A,Fredeen A L,et al.1994.Reflectance indices associated with physiological changes in nitrogen-and water-limited sunflower leaves.Remote Sensing of Environment,48(2):135-146.
Rouse J W,Haas R W,Schell J A,et al.1974.Monitoring the vernal advancement and retrogradation(Greenwave effect)of natural vegetation.NASA/GSFCT Type III final report.Nasa.
Qin Z F,Chang Q R,Xie B,et al.2016.Rice leaf nitrogen content estimation based on hysperspectral imagery of UAV in Yellow River diversion irrigation district.Transactions of the Chinese Society of Agricultural Engineering,32(23):77-85.
Schleicher T D,Bausch W C,Delgado J A,et al.2001.Evaluation and refinement of the nitrogen reflectance index(NRI)for site-specific fertilizer managemen.2001 ASAE Annual International Meeting.St-Joseph,MI,USA.ASAE Paper.pp.111-151.
Sims D A,Gamon J A.2002.Relationships between leaf pigment content and spectral reflectance across a wide range of species,leaf structures and developmental stages.Remote Sensing of Environment,81(2):337-354.
Song K S,Zhang B,Fang L I,et al.2005.A hyperspectral model for estimating chlorophyll content in maize leaves.Acta Agronomica Sinica,31(8):1095-1097.
Vi?a A,Gitelson A A,Nguy-Robertson A L,et al.2011.Comparison of different vegetation indices for the remote assessment of green leaf area index of crops.Remote Sensing of Environment,115:3468-3478.
Wang R H,Song X Y,Li Z H,et al.2014.Estimation of winter wheat nitrogen nutrition index using hyperspectral remote sensing.Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),30(19):191-187.
Wang Y H,Chen Y L.2009.Automatic matching and panorama generation of low altitude aerial images.Journal of Geomatics,34(2):47-49.
Wu J P,Sun D S.2006.Modern data analysis.Machinery Industry Press,Beijing.pp.92-96.
Xie X J,Shen S H,Li Y X,et al.2010.Red edge characteristics and monitoring SPAD and LAI for rice with high temperature stress.Transactions of the Chinese Society of Agricultural Engineering,26(3):183-190.
Yuan J G,Zheng N.2007.Nitrogen and chlorophyll mapping based on Hyperion hyperspectral image.Transactions of the Chinese Society of Agricultural Engineering,23(4):172-178.
Zaman-Allah M,Vergara O,Araus J L,et al.2015.Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize.Plant Methods,11(1):35.
Zhang Y Q,Chen J M,Miller J R,et al.2008.Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery.Remote Sensing of Environment,112(5):3234-3247.
Chen X L,Chen C,Zhou L,et al.2010.Determination and correlativity analysis of chloropgyll content at different development stages in rice.Modern Agricultural Science and Technology,17:42-44.
Clevers J G P W.1994.Imaging spectrometry in agriculture-plant vitality and yield indicators:imaging spectrometry-a tool for environmental observations.Springer Netherlands.pp.193-219.
Curran P J,Dungan J L,Gholz,H L.1990.Exploring the relationship between reflectance red edge and chlorophyll content in slash pine.Tree Physiology,7(1/2/3/4):33-48.
Daughtry C S T,Wahhall C L,Kim M S,et al.2000.Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance.Remote Sensing of Environment,74(2):229-239.
Gitelson A A,Merzlyak M N.1996.Signature analysis of leaf reflectance spectra:algorithm development for remote sensing of chlorophyll.Journal of Plant Physiology,148(3/4):494-500.
Gu Y P,Zhao W J,Wu Z S.2010.Least squares support vector machine algorithm.J Tsinghua Univ(Sci&Tech),7:1063-1066.
Haboudane D,Miller J R,Tremblay N,et al.2002.Integrated narrowband vegetation indices for prediction of crop chlorophyll content for application to precision agriculture.Remote Sensing of Environment,81(2/3):416-426.
Filella I,Penuelas J.2007.The red edge position and shape as indicators of plant chlorophyll content,biomass and hydric status.International Journal of Remote Sensing,15(7):1459-1470.
Kong W P,Bi Y L,Li S P,et al.2014.Hyperspectral estimation of leaf chlorophyll content in mycorrhizal inoculated soybean under drought stress.Transactions of the Chinese Society of Agricultural Engineering,30(12):123-131.
Liang L,Yang M,Zhang L.2012.Chlorophyll content inversion with hyperspectral technology for wheat canopy based on support vector regression algorithm.Transactions of the Chinese Society of Agricultural Engineering,10:162-171.
Luan Q Z,Liu H P,Xiao Z Q.2007.Comparison between algorithms of ortho-rectification for remote sensing images.Remote Sensing Technology and Application,22(6):743-747.
Pearson R L,Miller L D.1972.Remote mapping of standing crop biomass for estimation of the productivity of the short grass prairie.Proceeding of the 8th International Symposium on Remote Sensing of the Environment,45(2):7-12.
Peng Y,Gitelson A A.2012.Remote estimation of gross primary productivity in soybean and maize based on total crop chlorophyll content.Remote Sensing of Environment,117(2):440-448.
Penuelas J,Gamon J A,Fredeen A L,et al.1994.Reflectance indices associated with physiological changes in nitrogen-and water-limited sunflower leaves.Remote Sensing of Environment,48(2):135-146.
Rouse J W,Haas R W,Schell J A,et al.1974.Monitoring the vernal advancement and retrogradation(Greenwave effect)of natural vegetation.NASA/GSFCT Type III final report.Nasa.
Qin Z F,Chang Q R,Xie B,et al.2016.Rice leaf nitrogen content estimation based on hysperspectral imagery of UAV in Yellow River diversion irrigation district.Transactions of the Chinese Society of Agricultural Engineering,32(23):77-85.
Schleicher T D,Bausch W C,Delgado J A,et al.2001.Evaluation and refinement of the nitrogen reflectance index(NRI)for site-specific fertilizer managemen.2001 ASAE Annual International Meeting.St-Joseph,MI,USA.ASAE Paper.pp.111-151.
Sims D A,Gamon J A.2002.Relationships between leaf pigment content and spectral reflectance across a wide range of species,leaf structures and developmental stages.Remote Sensing of Environment,81(2):337-354.
Song K S,Zhang B,Fang L I,et al.2005.A hyperspectral model for estimating chlorophyll content in maize leaves.Acta Agronomica Sinica,31(8):1095-1097.
Vi?a A,Gitelson A A,Nguy-Robertson A L,et al.2011.Comparison of different vegetation indices for the remote assessment of green leaf area index of crops.Remote Sensing of Environment,115:3468-3478.
Wang R H,Song X Y,Li Z H,et al.2014.Estimation of winter wheat nitrogen nutrition index using hyperspectral remote sensing.Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),30(19):191-187.
Wang Y H,Chen Y L.2009.Automatic matching and panorama generation of low altitude aerial images.Journal of Geomatics,34(2):47-49.
Wu J P,Sun D S.2006.Modern data analysis.Machinery Industry Press,Beijing.pp.92-96.
Xie X J,Shen S H,Li Y X,et al.2010.Red edge characteristics and monitoring SPAD and LAI for rice with high temperature stress.Transactions of the Chinese Society of Agricultural Engineering,26(3):183-190.
Yuan J G,Zheng N.2007.Nitrogen and chlorophyll mapping based on Hyperion hyperspectral image.Transactions of the Chinese Society of Agricultural Engineering,23(4):172-178.
Zaman-Allah M,Vergara O,Araus J L,et al.2015.Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize.Plant Methods,11(1):35.
Zhang Y Q,Chen J M,Miller J R,et al.2008.Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery.Remote Sensing of Environment,112(5):3234-3247.