ABSTRACT

The knowledge of biophysical parameters is very crucial in making a strategy and managing precision agriculture practices. The leaf area index (LAI) is an essential biophysical parameter that helps in partitioning crop evapotranspiration into evaporation and transpiration and yield modeling and prediction. LAI varies with space and time, and optical remote sensing is an irreplaceable method in estimating spatio-temporal LAI. In addition, the various vegetation indices are helpful in assessing crop health and density, photosynthetic activities, leaf structure, and crop senescence. The present study has utilized the Sentinel-2 data to estimate the LAI through the SNAP software for the farmers’ field (for wheat crop) in the Delhi-NCR region of India. The LAI estimated through the remote sensing technique is compared with the LAI measured using SunScan, a device manufactured by Delta-T, and these two matched accurately. Further, the estimated LAI through SNAP was correlated with various vegetation indices like normalized difference vegetation index (NDVI), normalized difference red-edge index (NDRE1, NDRE2, and NDRE3), re-normalized vegetation index (RDVI), and chlorophyll red-edge index (CIRed-Edge and CIRed-Edge-1). These vegetation indices were also estimated through remote sensing techniques. The coefficient of determination (R2) between LAI and NDVI was 0.86. The R2 between LAI and NDRE1 was 0.92, whereas the R2 between LAI and NDRE2 was 0.70, and that between LAI and NDRE3 was 0.31. Further, the R2 between LAI and RDVI was 0.88. The R2 between LAI and CIRed-Edge was 0.88, whereas the R2 between LAI and CIRed-Edge-1 was 0.74.

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REFERENCES

Afrasiabian, Y., Noory, H., Mokhtari, A., Nikoo, M. R., Pourshakouri, F., and Haghighatmehr, P. (2021). Effects of spatial, temporal, and spectral resolutions on the estimation of wheat and barley leaf area index using multi-and hyper-spectral data (case study: Karaj, Iran). Precision Agriculture, 22, 660–688.
Boiarskii, B., and Hasegawa, H. (2019). Comparison of NDVI and NDRE indices to detect differences in vegetation and chlorophyll content. J. Mech. Contin. Math. Sci, 4, 20–29.
Carpenter, S. R., Caraco, N. F., Correll, D. L., Howarth, R. W., Sharpley, A. N., and Smith, V. H. (1998). Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecological applications, 8(3), 559–568.
Clevers, J. G., and Gitelson, A. A. (2013). Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and-3. International Journal of Applied Earth Observation and Geoinformation, 23, 344–351.
Crema, A., Boschetti, M., Nutini, F., Cillis, D., and Casa, R. (2020). Influence of soil properties on maize and wheat nitrogen status assessment from Sentinel-2 data. Remote Sensing, 12(14), 2175.
Defourny, P., et al. (2019). Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world. Remote sensing of environment, 221, 551–568.
Delegido, J., Verrelst, J., Alonso, L., and Moreno, J. (2011). Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors, 11(7), 7063–7081.
Dopico, E., Linde, A. R., and Garcia-Vazquez, E. (2009). Traditional and modern practices of soil fertilization: effects on cadmium pollution of river ecosystems in Spain. Human Ecology, 37, 235–240.
Drusch, M., Gascon, F., and Berger, M. (2010). Sentinel-2 mission requirements document. http://esamultimedia.esa.int/docs/GMES/Sentinel-2_MRD.pdf.
FAO and IWMI. (2018). More people, more food, worse water? A global review of water pollution from agriculture.
Fei, Y., Jiulin, S., Hongliang, F., Zuofang, Y., Jiahua, Z., Yunqiang, Z., Kaishan, S., Zongming, W., and Maogui, H. (2012). Comparison of different methods for corn LAI estimation over northeastern China. International Journal of Applied Earth Observation and Geoinformation, 18, 462–471.
Foley, J. A., et al. (2011). Solutions for a cultivated planet. Nature, 478(7369), 337–342.
Gitelson, A. A. (2004). Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of plant physiology, 161(2), 165–173.
Gowda, P., Oommen, T., Misra, D., Schwartz, R., Howell, T., and Wagle, P. (2015). Retrieving leaf area index from remotely sensed data using advanced statistical approaches. J. Remote Sens. GIS, 5, 156.
Gu, Y., Wylie, B. K., Howard, D. M., Phuyal, K. P., and Ji, L. (2013). NDVI saturation adjustment: A new approach for improving cropland performance estimates in the Greater Platte River Basin, USA. Ecological Indicators, 30, 1–6.
Houborg, R., and McCabe, M. F. (2018). A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning. ISPRS Journal of Photogrammetry and Remote Sensing, 135, 173–188.
Huang, L., Song, F., Huang, W., Zhao, J., Ye, H., Yang, X., and Liang, D. (2018). New Triangle Vegetation Indices for Estimating Leaf Area Index on Maize. Journal of the Indian Society of Remote Sensing, 46, 1907–1914.
Huang, S., Miao, Y., Yuan, F., Gnyp, M. L., Yao, Y., Cao, Q., Wang, H., Lenz-Wiedemann, V. I. S., and Bareth, G. (2017). Potential of RapidEye and WorldView-2 satellite data for improving rice nitrogen status monitoring at different growth stages. Remote Sensing, 9(3), 227.
Kamal, M., Phinn, S., and Johansen, K. (2016). Assessment of multi-resolution image data for mangrove leaf area index mapping. Remote Sensing of Environment, 176, 242–254.
Kulkarni, R., and Honda, K. (2020). Estimating LAI of rice using NDVI derived from MODIS surface reflectance. Adv. Sci. Technol. Eng. Syst. J, 5, 1047–1053.
Lee, B., Kwon, H., Miyata, A., Lindner, S., and Tenhunen, J. (2016). Evaluation of a phenology-dependent response method for estimating leaf area index of rice across climate gradients. Remote Sensing, 9(1), 20.
Liu, F., Qin, Q., and Zhan, Z. (2012). A novel dynamic stretching solution to eliminate saturation effect in NDVI and its application in drought monitoring. Chinese Geographical Science, 22, 683–694.
Muerth, M., Migdall, S., Hodrius, M., Niggemann, F., Holzapfel, M., Bach, H., and Volden, E. (2020, June). Food Security TEP-Supporting sustainable intensification of food production from space. In IOP Conference Series: Earth and Environmental Science (Vol. 509, No. 1, p. 012038). IOP Publishing.
Naguib, N. S., and Daliman, S. (2022, November). Analysis of NDVI and NDRE Indices Using Satellite Images for Crop Identification at Kelantan. In IOP Conference Series: Earth and Environmental Science (Vol. 1102, No. 1, p. 012054). IOP Publishing.
Nguy-Robertson, A., Gitelson, A., Peng, Y., Viña, A., Arkebauer, T., and Rundquist, D. (2012). Green leaf area index estimation in maize and soybean: Combining vegetation indices to achieve maximal sensitivity. Agronomy journal, 104(5), 1336–1347.
Qiao, K., Zhu, W., Xie, Z., and Li, P. (2019). Estimating the seasonal dynamics of the leaf area index using piecewise LAI-VI relationships based on phenophases. Remote Sensing, 11(6), 689.
Roujean, J. L., and Breon, F. M. (1995). Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote sensing of Environment, 51(3), 375–384.
Serbin, G., Hunt, E. R., Jr., Daughtry, C. S., and McCarty, G. W. (2013). Assessment of spectral indices for cover estimation of senescent vegetation. Remote Sensing Letters, 4(6), 552–560.
Sishodia, R. P., Ray, R. L., and Singh, S. K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sensing, 12(19), 3136.
Sun, Y., Ren, H., Zhang, T., Zhang, C., and Qin, Q. (2018). Crop leaf area index retrieval based on inverted difference vegetation index and NDVI. IEEE Geoscience and Remote Sensing Letters, 15(11), 1662–1666.
Tillack, A., Clasen, A., Kleinschmit, B., and Förster, M. (2014). Estimation of the seasonal leaf area index in an alluvial forest using high-resolution satellite-based vegetation indices. Remote Sensing of Environment, 141, 52–63.
Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R., and Polasky, S. (2002). Agricultural sustainability and intensive production practices. Nature, 418(6898), 671–677.
Tong, A., and He, Y. (2013). Comparative analysis of SPOT, Landsat, MODIS, and AVHRR normalized difference vegetation index data on the estimation of leaf area index in a mixed grassland ecosystem. Journal of Applied Remote Sensing, 7(1), 073599–073599.
Vijayasekaran, D. (2019). SEN2-AGRI–Crop type mapping pilot study using sentinel-2 satellite imagery in India. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 175–180.
Wang, Z., Yao, F., Li, W., and Wu, J. (2017). Saturation correction for nighttime lights data based on the relative NDVI. Remote Sensing, 9(7), 759.
Zhao, Y., Potgieter, A. B., Zhang, M., Wu, B., and Hammer, G. L. (2020). Predicting wheat yield at the field scale by combining high-resolution Sentinel-2 satellite imagery and crop modelling. Remote Sensing, 12(6), 1024.

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Pages: 948 - 959

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Published online: May 16, 2024

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Manoj Yadav [email protected]
1Ph.D. Research Scholar, Dept. of Civil Engineering, Shiv Nadar Institution of Eminence, Greater Noida, India. Email: [email protected]
Manikyala Sriram Theerdh [email protected]
2Undergraduate Research Scholar, Dept. of Civil Engineering, Shiv Nadar Institution of Eminence, Greater Noida, India. Email: [email protected]
Ghanshyam Giri [email protected]
3Ph.D. Research Scholar, Dept. of Civil Engineering, Shiv Nadar Institution of Eminence, Greater Noida, India. Email: [email protected]
Hitesh Upreti [email protected]
4Assistant Professor, Dept. of Civil Engineering, Shiv Nadar Institution of Eminence, Greater Noida, India. Email: [email protected]
Gopal Das Singhal [email protected]
5Associate Professor, Dept. of Civil Engineering, Shiv Nadar Institution of Eminence, Greater Noida, India. Email: [email protected]
Likith Muni Narakala [email protected]
6Undergraduate Research Scholar, Dept. of Civil Engineering, Shiv Nadar Institution of Eminence, Greater Noida, India. Email: [email protected]

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