Deriving the Leaf Area Index of Silage Maize Using Digital Hemispherical Photography Method (Case Study: Qaleh-Now Farms, South of Tehran)

Document Type : Research Paper

Authors

1 PhD student of Remote sensing, Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran.

2 Associate professor, Department of Remote sensing and GIS, Faculty of Geography, University of Tehran, Tehran, IRAN.

3 Associate professor, Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran

4 Associate professor, Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran.

5 Assistant professor, Department of Agro-ecology, Environmental Sciences Research Institute, Shahid Beheshti University, G.C, Tehran, Iran

6 Institute of Methodologies for Environmental Analysis (CNR IMAA), C.da S.Loja snc, 85050 Tito (Potenza), Italy

Abstract

The present study aimed to evaluate the efficiency of digital hemispherical photography (DHP) in deriving LAI in silage maize farms in the south of Tehran. For this purpose, the DHP as well as destructive measurements for comparison were used to estimate LAI in silage maize farms in Qaleh-Now County in the south of Tehran in 2018 considering the nature of spatio-temporal variability in agricultural fields during a growing season. The results showed LAI obtained through DHP at different periods of plant growth has a strong linear correlation with the values measured by the destructive method (R2 = 0.92, RMSE = 0.45 and Bias = 0.31). However, the intermediate LAI range (LAI: 2 -

Keywords

Main Subjects

References

Ariza-Carricondo, C., Di Mauro, F., de Beeck, M.O., Roland, M., Gielen, B., Vitale, D., Ceulemans, R. and Papale, D., (2019). A comparison of different methods for assessing leaf area index in four canopy types. Central European Forestry Journal, 65(2), 67-80.
Ashrafi A, Amiraslani F, Darvishi Boloorani A, Mousivand A J. (2019). Leaf Area Index (LAI) Responses of tree species to industrial dust (case study: the Caspian hyrcanian mixed forests). Geographical space. 18 (64). 267-286. (In Farsi).
Badiehneshin, A., Noori, H., Vazifehdoost, M. (2014). Calibration of leaf area index estimating equations in maize and sugar beet based on MODIS sensor satellite data (Qazvin irrigation network). Iranian Journal of Soil and Water Research, 45(2), 155-165. doi: 10.22059/ijswr.2014.51617. (In Farsi).
Baret, F., Weiss, M., Allard, D., Garrigues, S., Leroy, M., Jeanjean, H., Fernandes, R., Myneni, R., Privette, J., Morisette, J. and Bohbot, H., (2005). VALERI: a network of sites and a methodology for the validation of medium spatial resolution land satellite products. Remote Sensing of Environment, 76(3), 36-39.
Battude, M., Al Bitar, A., Morin, D., Cros, J., Huc, M., Sicre, C.M., Le Dantec, V. and Demarez, V. (2016). Estimating maize biomass and yield over large areas using high spatial and temporal resolution Sentinel-2 like remote sensing data. Remote Sensing of Environment, 184, 668-681.
Campos-Taberner, M., García-Haro, F. J., Moreno, A., Gilabert, M. A., Sanchez-Ruiz, S., Martinez, B., and Camps-Valls, G. (2015). Mapping leaf area index with a smartphone and Gaussian processes. IEEE Geoscience and Remote Sensing Letters, 12(12), 2501-2505.
Cavero, J., Farre, I., Debaeke, P., and Faci, J. M. (2000). Simulation of maize yield under water stress with the EPICphase and CROPWAT models. Agronomy journal. 679-690. doi:10.2134/agronj2000.924679x.
Claverie, M., Demarez, V., Duchemin, B., Hagolle, O., Ducrot, D., Marais-Sicre, C., Dejoux, J.F., Huc, M., Keravec, P., Béziat, P. and Fieuzal, R. (2012). Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data. Remote Sensing of Environment, 124, 844-857.
Darvishsefet, A., Miri, N., Shakeri, Z., Zargham, N. (2017). Estimation of leaf area index in Zagros forests using Landsat 8 data. Iranian Journal of Forest, 9(1), 29-42. (In Farsi).
Darvishzadeh, R.; Skidmore, A.; Schlerf, M.; Atzberger, C. (2008) Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland. Remote Sensing of Environment. 112, 2592–2604.
Deljouei, A., Sadeghi, S.M.M, Abdi, E., (2016). Comparing leaf area index at different distances from constrcted forest roads edge in hyrcanian forest (case study: a hornbeam-beech forest in kheyrud, mazandaran). Forest research and development, 2(2). 169-180. (In Farsi).
Demarez, V., Duthoit, S., Baret, F., Weiss, M., Dedieu, G., (2008). Estimation of leaf area and clumping indexes of crops with hemispherical photographs. Agriculture and Forest Meteorology. 148, 644–655.
ESA, (2005). SPARC 2004, Contract No. 18307/04/NL/FF, SPARC Data Acquisition Report.
Fang, H., Baret, F., Plummer, S., and Schaepman‐Strub, G. (2019). An overview of global leaf area index (LAI): Methods, products, validation, and applications. Reviews of Geophysics. Wiley Online Library. 1-61.
Faridhosseini, A.R., Astaraei, S.H., Sanaeinejad, P., Mirhoseini Moosavi, P. (2013). Estimation of leaf area index using IRS satellite images. Iranian journal of field crops research. 10(3). 577-582. (In Farsi).
Gao, Y., Duan, A., Qiu, X., Sun, J., Zhang, J., Liu, H., & Wang, H. (2010). Distribution and use efficiency of photosynthetically active radiation in strip intercropping of maize and soybean. Agronomy journal, 102(4), 1149-1157.
IRIMO [WWW Document], (2018). URL www.irimo.ir (accessed 9.30.18).
Jin, X., Yang, G., Xu, X., Yang, H., Feng, H., Li, Z., Shen, J., Zhao, C. and Lan Y., (2015). Combined multi-temporal optical and radar parameters for estimating LAI and biomass in winter wheat using HJ and RADARSAR-2 data. Remote Sensing, 7(10), 13251-13272. doi:10.3390/rs71013251.
Jonckheere, I., Fleck, S., Nackaerts, K., Muys, B., Coppin, P., Weiss, M., and Baret, F. (2004). Review of methods for in situ leaf area index determination: Part I. Theories, sensors and hemispherical photography. Agricultural and Forest Meteorology, 121(1-2), 19-35.
Kross, A., McNairn, H., Lapen, D., Sunohara, M., and Champagne, C. (2015). Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. International Journal of Applied Earth Observation and Geoinformation, 34, 235-248.
Leblanc, S.G., Chen, J.M., Fernandes, R., Deering, D.W., Conley, A., (2005). Methodology comparison for canopy structure parameters extraction from digital hemispherical photography in boreal forests. Agricultural and Forest Meteorology. 129, 187–207.
Liu, J., Pattey, E., and Admiral, S. (2013). Assessment of in situ crop LAI measurement using unidirectional view digital photography. Agricultural and Forest Meteorology, 169, 25-34.
Macfarlane, C., Hoffman, M., Eamus, D., Kerp, N., Higginson, S., McMurtrie, R., Adams, M., (2007). Estimation of leaf area index in eucalypt forest using digital photography. Agricultural and Forest Meteorology. 143, 176–188.
Mousivand, A., Menenti, M., Gorte, B., & Verhoef, W. (2015). Multi–temporal, multi–sensor retrieval of terrestrial vegetation properties from spectral–directional radiometric data. Remote Sensing of Environment, 158, 311-330.
Munz, S., Feike, T., Chen, Q., Claupein, W., and Graeff-Hönninger, S. (2014). Understanding interactions between cropping pattern, maize cultivar and the local environment in strip-intercropping systems. Agricultural and Forest Meteorology, 195, 152-164.
Verrelst, J., Muñoz, J., Alonso, L., Delegido, J., Rivera, J. P., Camps-Valls, G., and Moreno, J. (2012). Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and-3. Remote Sensing of Environment, 118, 127-139.
Xia, T., Miao, Y., Wu, D., Shao, H., Khosla, R., and Mi, G. (2016). Active optical sensing of spring maize for in-season diagnosis of nitrogen status based on nitrogen nutrition index. Remote sensing, 8(7), 605.
Yan, G., Hu, R., Luo, J., Weiss, M., Jiang, H., Mu, X., Xie, D. and Zhang, W., (2019). Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives. Agricultural and Forest Meteorology, 265, 390-411.
Iranian Journal of Soil and Water Research
Volume 51, Issue 6
September 2020
Pages 1331-1340

Files

Share

How to cite

Statistics