Satellite-Based Spatiotemporal Variability and Mapping the Irrigation Water Requirements of Wheat Crop in Ludhiana District, Punjab
DOI:
https://doi.org/10.53550/Keywords:
Evapotranspiration, Irrigation water requirement, Mapping, WheatAbstract
This study evaluated the spatial and temporal patterns of evapotranspiration (ET) and irrigation water requirement (IWR) for wheat in the Ludhiana district, Punjab, using satellite data and statistical modeling. MODIS-derived ET and WorldClim rainfall data were processed in QGIS using the Inverse Distance Weighting (IDW) method for spatial interpolation. Sentinel-2A multispectral imagery was used to generate wheat crop masks. Pearson correlation analysis revealed strong positive relationships between ET, temperature, and wind speed, and a significant negative correlation with relative humidity, while rainfall showed no significant association. A two-way ANOVA indicated significant effects of both month and block (spatial) on ET, with the highest values observed in February. The interaction between block and month emphasized the role of localized climatic and soil conditions. IWR was calculated using rainfall and ET data, with peak irrigation demand during February and March, coinciding with critical growth stages of wheat. The developed ET and IWR maps serve as valuable tools for location-specific irrigation scheduling. Findings support groundwater conservation efforts by promoting efficient water use and provide a framework to work with the realtime weather data and auxiliary spatial layers to enhance precision irrigation.
References
Casa, R., Matteo, R., Giusepp S and Antonio T. (2009). Assessing crop water demand by remote sensing and GIS for the Pontina Plain, Central Italy. Water Resour. Ranage. 23: 1685-1712. https://doi.org/10.1007/s11269-008-9347-4
Jalota, S. K., Jain, A. K. and Vashisht, B. B. (2018). Minimize water deficit in wheat crop to ameliorate groundwater decline in rice-wheat cropping system. Agric. Water Manage. 208: 261-267. https://doi.org/10.1016/j.agwat.2018.06.020
Jalota, S. K., Kaur, P., Kaur, J., Kingra, P. K. and Vashisht, B. B. (2020). Critical analysis of temporal climate change and ensuing frequency of crop yield constraining temperatures. J. Agrometeorol. 22: 339-52.
Jalota, S. K., Vashisht, B. B., Kaur, H., Kaur, S. and Kaur, P. (2014). Location specific climate change scenario and its impact on rice and wheat in Central Indian Punjab. Agric. Sys. 131: 77-86. https://doi.org/10.1016/j.agsy.2014.07.009
Jang, K., Kang, S., Kim, J., Lee, C. B., Kim, T., Kim, J. and Saigusa, N. (2010). Mapping evapotranspiration using MODIS and MM5 four-dimensional data assimilation. Remote Sens. Environ. 114: 657-673. https://doi.org/10.1016/j.rse.2009.11.010
Johnson, A., Ashika, N. P., Parvathy Nayana, N. and Joseph, A. (2019). Crop water requirement and irrigation scheduling of selected crops using cropwat: A case study of Pattambi region. PhD dissertation, Kelappaji College of Agricultural Engineering and technology, India.
Kamali, M. I. and Nazari, R. (2018). Determination of maize water requirement using remote sensing data and SEBAL algorithm. Agric. Water Manage. 209: 197-205. https://doi.org/10.1016/j.agwat.2018.07.035
Li, H., Zheng, L., Lei, Y., Li, C., Liu, Z. and Zhang, S. (2008). Estimation of water consumption and crop water productivity of winter wheat in North China Plain using remote sensing technology. Agric. Water Manage. 95: 1271-1278. https://doi.org/10.1016/j.agwat.2008.05.003
Liu, X., Shao, L., Sun, H., Chen, S. and Zhang, X. (2013). Responses of yield and water use efficiency to irrigation amount decided by pan evaporation for winter wheat. Agric. Water Manage. 129: 173-180. https://doi.org/10.1016/j.agwat.2013.08.002
Mahmoud, S. H. and Gan, T. Y. (2019). Irrigation water management in arid regions of Middle East: Assessing spatio-temporal variation of actual evapotranspiration through remote sensing techniques and meteorological data. Agric. Water Manage. 212: 35-47. https://doi.org/10.1016/j.agwat.2018.08.040
Moyer, J. D. and Hedden, S. (2020). Are we on the right path to achieve the sustainable development goals? World Development, 127, 104749.
Mu, Q., Heinsch, F. A., Zhao, M. and Running, S. W. (2007). Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens. Environ. 111: 519-536. https://doi.org/10.1016/j.rse.2007.04.015
Nelson, S. A. and Khorram, S. (2018). Introduction to image data processing. In Image Processing and Data Analysis with ERDAS IMAGINE. Pp: 49-68. CRC Press, London, UK.
Pakhale, G., Gupta, P. and Nale, J. (2010). Crop and irrigation water requirement estimation by remote sensing and GIS: A case study of Karnal district, Haryana, India. Int. J. Eng. Technol. 2(4): 207-211.
Pramod, V. P., Rao, B. B., Ramakrishna, S. S. V. S., Sandeep, V. M., Patel, N. R., Chandran, M. S. and Kumar, P. V. (2018). Trends in water requirements of wheat crop under projected climates in India. J. Agrometeorol. 20(2): 110-116. https://doi.org/10.54386/jam.v20i2.520
Sidhu, B. S., Sharda, R. and Singh, S. (2021). Spatio-temporal assessment of groundwater depletion in Punjab, India. Groundwater Sustain. Dev. 12: 100498. https://doi.org/10.1016/j.gsd.2020.100498
Trivedi, A., Pyasi, S. K. and Galkate, R. V. (2018). Estimation of Evapotranspiration using CROPWAT 8.0 Model for Shipra River Basin in Madhya Pradesh, India. Int. J. Curr. Microbiol. Appl. Sci. 7(5): 1248-1259. https://doi.org/10.20546/ijcmas.2018.705.151
Trivedi, A., Rao, K. V. R., Rajwade, Y., Yadav, D. and Verma, N. S. (2022). Remote Sensing and Geographic Information System Applications for Precision Farming and Natural Resource Management. Ind. J. Ecol. 49(5). https://doi.org/10.55362/IJE/2022/3707
Woznicki S A, Nejadhashemi A P and Parsinejad M. 2015. Climate change and irrigation demand: Uncertainty and adaptation. J. Hydrol. Reg. Stud. 3: 247-264. https://doi.org/10.1016/j.ejrh.2014.12.003
Yousaf, W., Awan, W. K., Kamran, M., Ahmad, S. R., Bodla, H. U., Riaz, M. and Chohan, K. (2021). A paradigm of GIS and remote sensing for crop water deficit assessment in near real time to improve irrigation distribution plan. Agric. Water Manage. 243: 106443. https://doi.org/10.1016/j.agwat.2020.106443



