Iranian Journal of Remote Sensing & GIS

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Publication Ethics

Indexing and Abstracting

Related Links

FAQ

Peer Review Process

News

Evaluating Capability of Reflectance Spectrometry for Estimating the Wheat Chlorophyll and Nitrogen

Document Type : Original Article

Authors

1 Assistant prof., Dep. of Geography and Urban Planning, Faculty of Geographical Sciences and Planning, University of Isfahan

2 Ph.D. of Remote Sensing, Dep. of Remote Sensing and GIS, Faculty of Geography, University of Tehran

3 Dep. of Soil and Water Research, East Azarbaijan Agricultural and Natural Resources Research and Training Center, Tabriz

4 Assistant Prof., Dep. of Civil Engineering, Sanandaj Branch, Islamic Azad University

5 Prof. of Dep. of Soil Science, Faculty of Agriculture, Tarbiat Modares University

Abstract

Leaf chlorophyll and nitrogen, due to their important role in photosynthesis are among the major biological parameters of plant physiological status. The ability to quantify chlorophyll and nitrogen can provide important information for precision agricultural activities, plant and agricultural resource management planning, and modeling ecosystem services and production capabilities. This study aimed to assess the capability of indices for estimating the amount of chlorophyll and nitrogen in wheat using spectral data at the canopy level and also determine the most suitable spectral regions and absorption features for this purpose. This research was carried out in a greenhouse environment and the spectroscopic measurements were performed using ASD Fieldspec-3 full-range spectral spectroradiometer. Four plant band indices were classified into two groups of ratio- (NDVI, RVI, and DVI) and soil-based indices (SAVI2) for the raw spectrum and the first derivative of the spectrum for the total samples, and the results were compared. The parameters of position, depth, area, asymmetry and width were calculated for seven absorption features extracted from continuum-removed spectra, and the correlation of these indices with chlorophyll and nitrogen content of wheat was examined. The results showed that SAVI2 had a stronger correlation (RMSE = 0.12, R2 = 0.85) with the chlorophyll content NDVI (RMSE=0.30, R2=0.69) had a higher correlation with the nitrogen content, while using the first derivative with NDVI provided better results. Moreover, area and depth parameters of 430-760 nm absorption spectrum were the best indicators for estimating the amount of chlorophyll and nitrogen in wheat, respectively.

Keywords

References
Abbasi, M., Darvishsefat, A.A., Schaepman, M.E., Marvie Mohadjer, M.R. & Sobhany, H., 2009, Investigation on Leaf Spectral Reflectance of Most Important Species of Caspian Forests Using Field Spectroradiometry, Iranian Journal of Forest and Poplar Research, 17(4), PP. 568-580.
Abdolzadeh, A. & Safari, N., 2002, Comparison of Salt Tolerance in Eleven Varieties of Wheat with Emphasize on Ions Accumulation, Journal of Agricultural Sciences and Natural Resources, 9(2), PP. 95-103.
Asner, G.P. & Martin, R.E., 2008, Spectral and Chemical Analysis of Tropical Forests: Scaling from Leaf to Canopy Levels, Remote Sensing of Environment, 112, PP. 3958-3970.
Bahrami, H.A., Mirzaei, S., Darvishi Boloorani, A., Darvishzadeh, R. & Alavipanah, S.K., 2016, Analysis of Dust Storm Effects on Reflectance Spectra of Wheat Canopy, Iranian Journal of Remote Sensing and GIS, 7(4), PP. 13-26.
Bonneville, M.C. & Fyles, J.W., 2006, Assessing Variations in SPAD-502 Chlorophyll Meter Measurements and Their Relationships with Nutrient Content of Trembling Aspen Foliage, Communications in Soil Science and Plant Analysis, 37, PP. 525-539.
Broge, N.H. & Mortensen, J.V., 2002, Deriving Green Crop Area Index and Canopy Chlorophyll Density of Winter Wheat from Spectral Reflectance Data, Remote Sensing of Environment, 81, PP. 45-57.
Carter, G.A., 1994, Ratios of Leaf Reflectances in Narrow Wavebands as Indicators of Plant Stress, International Journal of Remote Sensing, 15(3), PP. 697-703.
Clark, R.N., 1999, Chapter 1 Spectroscopy of Rocks and Minerals, and Principles of Spectroscopy, in Manual of Remote Sensing, Vol. 3, Remote Sensing for the Earth Sciences, (A.N. Rencz, Ed.) John Wiley and Sons, New York, PP. 3-58.
 
Colombo, R., Bellingeri, D., Fasolini, D. & Marino, C.M., 2003, Retrieval of Leaf Area Index in Different Vegetation Types Using High Resolution Satellite Data, Remote Sensing of Environment, 86(1), PP. 120-131.
Darvishi Boloorani, A., Ranjbar, S., Mirzaei, S., Bahrami, H.A., Mirzapour, F. & Abbaszadeh Tehrani, N., 2019, Analysis of Persian Oak (Quercus Brantii Lindl)’s Spectral-Temporal Behavior under the Stresses of Water Deficiency and Dust Particles, Dushanbe, Tajikistan, 8-12 April, E3S Web of Conferences, 99, 04011.
Darvishi Boloorani, A., Ranjbar, S., Mirzaei, S., Bahrami, H.A., Mirzapour, F. & Abbaszadeh Tehrani, N, 2020, Spectral Behavior of Persian Oak under Compound Stress of Water Deficit and Dust Storm, International Journal of Applied Earth Observation, 88, P. 102082.
Darvishzadeh, R., Skidmore, A., Schlerf, M., Atzberger, C., Corsia, F. & Choa, M., 2008, LAI and Chlorophyll Estimation for a Heterogeneous Grassland Using Hyperspectral Measurements, ISPRS Journal of Photogrammetry & Remote Sensing, 63, PP. 409-426.
Dorigo, W.A., Zurita-Milla, R., de Wit, A.J.W., Brazile, J., Singh, R. & Schaepman, M.E., 2007, A Review on Reflective Remote Sensing and Data Assimilation Techniques for Enhanced Agroecosystem Modeling, International Journal of Applied Earth Observation, 9, PP. 165-193.
Greenway, H. & Munns, R., 1980, Mechanisms of salt tolerance in no halophytes, Annual Review of Plant Physiology, 31, PP. 149-190.
Hansen, P.M. & Schjoerring, J.K., 2003, Reflectance Measurement of Canopy Biomass and Nitrogen Status in Wheat Crops Using Normalized Difference Vegetation Indices and Partial Least Squares Regression, Remote Sensing of Environment, 86, PP. 542-553.
Ju, X.T., Xing, G.X., Chen, X.P., Zhang, S.L., Zhang, L.J., Liu, X.J., Cui, Z.L., Yin, B., Christie, P., Zhu, Z.L. & Zhang, F.S., 2009, Reducing Environmental Risk by Improving N Management in Intensive Chinese Agricultural Systems, Proceedings of the National Academy of Sciences, U.S.A., 106, PP. 3041-3046.
Kokaly, R.F., 2011, PRISM: Processing Routines in IDL for Spectroscopic Measurements (Installation Manual and User’s Guide, Version 1.0), U.S. Geological Survey Open-File Report 2011-1155.
Kokaly, R.F. & Clark, R.N., 1999, Spectroscopic Determination of Leaf Biochemistry Using Band-Depth Analysis of Absorption Features and Stepwise Multiple Linear Regression, Remote Sensing of Environment, 67, PP. 267-287.
Lichtenthaler, H.K., 1998, The Stress Concept in Plants: An Introduction, Annals of the New York Academy of Science, 851, PP. 187-198.
Liu, M., Liu, X., Li, M., Fang, M. & Chi, W, 2010, Neural-Network Model for Estimating Leaf Chlorophyll Concentration in Rice under Stress from Heavy Metals Using Four Spectral Indices, Biosystems Engineering, 106, PP. 223-233.
Main, R., Azong Cho, M., Mathieu, R., O’Kennedy, M.M., Ramoelo, A. & Koch, S., 2011, An inVestigation into Robust Spectral Indices for Leaf Chlorophyll Estimation, ISPRS Journal of Photogrammetry and Remote Sensing, 66(6), PP. 751-761.
Markwell, J., Osterman, J.C. & Mitchell, J.L., 1995, Calibration of the Minolta SPAD-502 Leaf Chlorophyll Meter, Photosynthesis Research, 46(3), PP. 467-72.
Meroni, M., Colombo, R. & Panigada, C., 2004, Inversion of a Radiative Transfer Model with Hyperspectral Observations for LAI Mapping in Poplar Plantations, Remote Sensing of Environment, 92(2), PP. 195-206.
Mirzaei, M., Marofi, S., Solgi, E., Abasi, M. & Karimi, R., 2021, Nondestructive Estimation of Leaf Nitrogen and Chlorophyll Contents in Grapes Using Field Hyper Spectral Data and Support Vector Machines Approach, Journal of Plant Research (Iranian Journal of Biology), 34(1), PP. 152-167.
Mutanga, O., Skidmore, A.K. & Van Wieren, S., 2003, Discriminating Tropical Grass (Cenchrus Ciliaris) Canopies Grown under Different Nitrogen Treatments Using Spectroradiometry, ISPRS Journal of Photogrammetry & Remote Sensing, 57(3), PP. 263-272.
Niinemets, U. & Tenhunen, J.D., 1997, A Model Separating Leaf Structural and Physiological Effects on Carbon Gain along Light Gradients for the Shade-Tolerant Species Acer Saccharum, Plant, Cell and Environment, 20, PP. 845- 866.
Pérez-Patricio, M., Camas-Anzueto, J.L., Sanchez-Alegría, A., Aguilar-González, A., Gutiérrez-Miceli, F., Escobar-Gómez, E., Voisin, Y., Rios-Rojas, C. & Grajales-Coutiño, R., 2018, Optical Method for Estimating the Chlorophyll Contents in Plant Leaves, Sensors, 18(650), PP. 1-12.
Raun, W.R., Solie, J.B., Johnson, G.V., Stone, M.L., Mullen, R.W., Freeman, K.W., Thomason, W.E. & Lukina, E.V., 2002, Improving Nitrogen Use Efficiency in Cereal Grain Production with Optical Sensing and Variable Rate Application, Agronomy, 94, PP. 815-820.
Savitzky, A. & Golay, M.J.E., 1964, Smoothing and Differentiation of Data by Simplified Least Square Procedure, Analytical Chemistry, 36(8), PP. 1627-1638.
Schaepman-Strub, G., Limpens, J., Menken, M., Bartholomeus, H.M. & Schaepman, M.E., 2008, Towards Spatial Assessment of Carbon Sequestration in Peatlands: Spectroscopy Based Estimation of Fractional Cover of Three Plant Functional Types, Biogeosciences Discussions, 5, PP. 1293-1317.
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, PP. 337-354.
Thenkabail, P., Smith, R. & De Pauw, E., 2000, Hyperspectral Vegetation and Their Relationships with Agricultural Crop Characteristics, Remote Sensing of Environment, 71(2), PP. 158-182.
Wang, W., Yao, X., Yao, X., Tian, Y.C., Liu, X.J., Ni, J., Cao, W.X. & Zhu, Y., 2012, Estimating Leaf Nitrogen Concentration with Three-Band Vegetation Indices in Rice and Wheat, Field Crops Research, 129, PP. 90-98.
Yao, X., Zhu, Y., Tian, Y.C., Feng, W. & Cao, W.X., 2010, Exploring Hyperspectral Bands and Estimation Indices for Leaf Nitrogen Accumulation in Wheat, International Journal of Applied Earth Observation, 12, PP. 89-100.
Yoder, B.J. & Pettigrew-Crosby, R.E., 1995, Predicting Nitrogen and Chlorophyll Content and Concentrations from Reflectance Spectra (400– 2500 nm) at Leaf and Canopy Scales, Remote Sensing of Environment, 53(3), PP. 199- 211.
Zahedifar, M., Karimian, N., Ronaghi, A., Yasrebi, J. & Emam, Y., 2011, Phosphorus and Zinc Distribution in Different Parts and Various Growth Stages of Wheat under Field Conditions, Journal of Water and Soil, 25(3), PP. 436-445.
Zarco-Tejada, P.J., González-Dugo, V., Williams, L.E., Suárez, L., Berni, J.A.J., Goldhamer, D. & Fereres, E., 2013, A PRI-Based Water Stress Index Combining Structural and Chlorophyll Effects: Assessment Using Diurnal Narrow-Band Airborne Imagery and the CWSI Thermal Index, Remote Sensing of Environment, 138, PP. 38-50.
Zhu, Y., Yao, X., Tian, Y.C., Liu, X.J. & Cao, W.X., 2008, Analysis of Common Canopy Vegetation Indices for Indicating Leaf Nitrogen Accumulations in Wheat and Rice, International Journal of Applied Earth Observation, 10, PP. 1-10.
Iranian Journal of Remote Sensing & GIS
Volume 14, Issue 4 - Serial Number 56
January 2023
Pages 119-134
Files
Share
How to cite
Statistics