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Volume 12
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Article

Geospatial Evaluation of Cropping Pattern and Cropping Intensity Using Multi Temporal Harmonized Product of Sentinel-2 Dataset on Google Earth Engine

by
Sonia
1,
Tathagata Ghosh
1,*,
Amel Gacem
2,
Taghreed Alsufyani
3,
M. Mujahid Alam
4,
Krishna Kumar Yadav
5,
Mohammed Amanullah
6 and
Marina M. S. Cabral-Pinto
7,*
1
Department of Arts, School of Liberal Arts and Sciences, Mody University of Science and Technology, Lakshmangarh, Sikar 332311, Rajasthan, India
2
Department of Physics, Faculty of Sciences, University 20 Août 1955, Skikda 21000, Algeria
3
Department of Chemistry, College of Science, Taif University, Taif 21944, Saudi Arabia
4
Department of Chemistry, Faculty of Science, King Khalid University, Abha 61413, Saudi Arabia
5
Faculty of Science and Technology, Madhyanchal Professional University, Ratibad, Bhopal 462044, Madhya Pradesh, India
6
Department of Clinical Biochemistry, College of Medicine, King Khalid University, Abha 61413, Saudi Arabia
7
Geobiotec Research Centre, Department of Geoscience, University of Aveiro, 3810-193 Aveiro, Portugal
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2022, 12(24), 12583; https://doi.org/10.3390/app122412583
Submission received: 20 November 2022 / Revised: 3 December 2022 / Accepted: 5 December 2022 / Published: 8 December 2022
(This article belongs to the Special Issue Recent Advances in Geospatial Big Data Mining)
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Abstract

Due to the declining land resources over the past few decades, the intensification of land uses has played a significant role in balancing the ever-increasing demand for food in developing nations such as India. To optimize agricultural land uses, one of the crucial indicators is cropping intensity, which measures the number of times a single parcel of land is farmed. Therefore, it is imperative to create a timely and accurate cropping intensity map so that landowners and agricultural planners can use it to determine the best course of action for the present and for the future. In the present study, we have developed an algorithm on Google Earth Engine (GEE) to depict cropping patterns and further fused it with a GIS environment to depict cropping intensity in the arid western plain zone of Rajasthan, India. A high-resolution multi-temporal harmonized product of the Sentinel-2 dataset was incorporated for depicting the growth cycle of crops for the year 2020–2021 using the greenest pixel composites. Kharif and Rabi accounted for 73.44% and 26.56% of the total cultivated area, respectively. Only 7.42% was under the double-cropped area to the total cultivated area. The overall accuracy of the classified image was 90%. For the Kharif crop, the accuracy was 95%, while for Rabi and the double-cropped region, the accuracy was 88%, with a kappa coefficient of 0.784. The present study was able to depict the seasonal plantation system in arid arable land with higher accuracy. The proposed work can be used to monitor cropping patterns and cost-effectively show cropping intensities.
Keywords: cropping pattern; cropping intensity; Google Earth Engine; arid western plain zone cropping pattern; cropping intensity; Google Earth Engine; arid western plain zone

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MDPI and ACS Style

Sonia; Ghosh, T.; Gacem, A.; Alsufyani, T.; Alam, M.M.; Yadav, K.K.; Amanullah, M.; Cabral-Pinto, M.M.S. Geospatial Evaluation of Cropping Pattern and Cropping Intensity Using Multi Temporal Harmonized Product of Sentinel-2 Dataset on Google Earth Engine. Appl. Sci. 2022, 12, 12583. https://doi.org/10.3390/app122412583

AMA Style

Sonia, Ghosh T, Gacem A, Alsufyani T, Alam MM, Yadav KK, Amanullah M, Cabral-Pinto MMS. Geospatial Evaluation of Cropping Pattern and Cropping Intensity Using Multi Temporal Harmonized Product of Sentinel-2 Dataset on Google Earth Engine. Applied Sciences. 2022; 12(24):12583. https://doi.org/10.3390/app122412583

Chicago/Turabian Style

Sonia, Tathagata Ghosh, Amel Gacem, Taghreed Alsufyani, M. Mujahid Alam, Krishna Kumar Yadav, Mohammed Amanullah, and Marina M. S. Cabral-Pinto. 2022. "Geospatial Evaluation of Cropping Pattern and Cropping Intensity Using Multi Temporal Harmonized Product of Sentinel-2 Dataset on Google Earth Engine" Applied Sciences 12, no. 24: 12583. https://doi.org/10.3390/app122412583

APA Style

Sonia, Ghosh, T., Gacem, A., Alsufyani, T., Alam, M. M., Yadav, K. K., Amanullah, M., & Cabral-Pinto, M. M. S. (2022). Geospatial Evaluation of Cropping Pattern and Cropping Intensity Using Multi Temporal Harmonized Product of Sentinel-2 Dataset on Google Earth Engine. Applied Sciences, 12(24), 12583. https://doi.org/10.3390/app122412583

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