Geospatial Evaluation of Cropping Pattern and Cropping Intensity Using Multi Temporal Harmonized Product of Sentinel-2 Dataset on Google Earth Engine
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Remote Sensing in Agriculture · Smart Agriculture and AI · Land Use and Ecosystem Services
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Arts, School of Liberal Arts and Sciences, Mody University of Science and Technology, Lakshmangarh, Sikar 332311, Rajasthan, India
Department of Physics, Faculty of Sciences, University 20 Août 1955, Skikda 21000, Algeria
Department of Chemistry, College of Science, Taif University, Taif 21944, Saudi Arabia
Department of Chemistry, Faculty of Science, King Khalid University, Abha 61413, Saudi Arabia
Faculty of Science and Technology, Madhyanchal Professional University, Ratibad, Bhopal 462044, Madhya Pradesh, India
Department of Clinical Biochemistry, College of Medicine, King Khalid University, Abha 61413, Saudi Arabia
Geobiotec Research Centre, Department of Geoscience, University of Aveiro, 3810-193 Aveiro, Portugal
Funding
Deanship of Research, King Khalid University, Saudi Arabia, for the Small Research Group · Taif University Researchers Supporting Project · University Grants Commission, India (UGC)
References (44)
Show source-provided references
1. Tudi, M., Daniel Ruan, H., Wang, L., Lyu, J., Sadler, R., Connell, D., and Phung, D.T. (2021). Agriculture development, pesticide application and its impact on the environment. Int. J. Environ. Res. Public Health, 18.10.3390/ijerph18031112 ↗
2. Kozak, M., and Pudełko, R. (2021). Impact assessment of the long-term fallowed land on agricultural soils and the possibility of their return to agriculture. Agriculture, 11.10.3390/agriculture11020148 ↗
3. Security and privacy in smart farming: Challenges and opportunities10.1109/ACCESS.2020.2975142 ↗
4. Sanghera, G.S., Malhotra, P.K., Singh, H., and Bhatt, R. (2019). Climate change impact in sugarcane agriculture and mitigation strategies. Harnessing Plant Biotechnol. Physiol. Stimul. Agric. Growth, 99–115.
5. Environmental impact of conventional agriculture
6. Farooq, M., Rehman, A., and Pisante, M. (2019). Innovations in Sustainable Agriculture, Springer.10.1007/978-3-030-23169-9 ↗
7. Ensuring water security, food security, and clean water in the North China Plain–conflicting strategies10.1016/j.cosust.2019.09.008 ↗
8. Gustafson, J.P., Raven, P.H., and Ehrlich, P.R. (2020). Population, Agriculture, and Biodiversity: Problems and Prospects, University of Missouri Press.10.1079/9781789249095.0001 ↗
9. Modelling of land use land cover change using earth observation data-sets of Tons River Basin, Madhya Pradesh, India10.1080/10106049.2017.1343390 ↗
10. Modeling fragmentation probability of land-use and land-cover using the bagging, random forest and random subspace in the Teesta River Basin, Bangladesh10.1016/j.ecolind.2021.107612 ↗
11. Multiple cropping systems of the world and the potential for increasing cropping intensity10.1016/j.gloenvcha.2020.102131 ↗
12. Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine10.1016/j.rse.2019.111624 ↗
13. Global cropland intensification surpassed expansion between 2000 and 2010: A spatio-temporal analysis based on GlobeLand3010.1016/j.scitotenv.2020.141035 ↗
14. Crop rotation mapping using multidate IRS digital data10.1016/S0924-2716(97)83003-1 ↗
15. Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm10.1007/s11430-019-9606-4 ↗
16. Improved crop classification with rotation knowledge using sentinel-1 and-2 time series10.14358/PERS.86.7.431 ↗
17. Mapping of crop types in 1989, 1999, 2009 and 2019 to assess major land cover trends of the Udon Thani Province, Thailand10.1016/j.compag.2022.107083 ↗
18. Crop pattern mapping of Tumkur Taluk using NDVI technique: A remote sensing and GIS approach10.1016/j.aqpro.2015.02.181 ↗
19. Spatial dynamics for relative contribution of cropping pattern analysis on environment by integrating remote sensing and GIS
20. Guo, Y., Xia, H., Pan, L., Zhao, X., and Li, R. (2022). Mapping the northern limit of double cropping using a phenology-based algorithm and Google Earth Engine. Remote Sens., 14.10.3390/rs14041004 ↗
21. Application of Sentinel-1 Data for Classifying Croplands Using Google Earth Engine
22. Automated near-real-time mapping and monitoring of rice extent, cropping patterns, and growth stages in Southeast Asia using Sentinel-1 time series on a Google Earth Engine platform10.3390/rs11141666 ↗
23. Magidi, J., Nhamo, L., Mpandeli, S., and Mabhaudhi, T. (2021). Application of the random forest classifier to map irrigated areas using google earth engine. Remote Sens., 13.10.3390/rs13050876 ↗
24. Sentinel SAR-optical fusion for crop type mapping using deep learning and Google Earth Engine10.1016/j.isprsjprs.2021.02.018 ↗
25. Amani, M., Kakooei, M., Moghimi, A., Ghorbanian, A., Ranjgar, B., Mahdavi, S., and Mohammadzadeh, A. (2020). Application of google earth engine cloud computing platform, sentinel imagery, and neural networks for crop mapping in Canada. Remote Sens., 12.10.3390/rs12213561 ↗
26. Automated cropland mapping of continental Africa using Google Earth Engine cloud computing10.1016/j.isprsjprs.2017.01.019 ↗
27. Temporal dynamics of spatial heterogeneity over cropland quantified by time-series NDVI, near infrared and red reflectance of Landsat 8 OLI imagery
28. Artificial neural network for crop classification using C-band RISAT-1 satellite datasets10.3103/S1068367416030137 ↗
29. Sitokonstantinou, V., Papoutsis, I., Kontoes, C., Lafarga Arnal, A., Armesto Andrés, A.P., and Garraza Zurbano, J.A. (2018). Scalable parcel-based crop identification scheme using Sentinel-2 data time-series for the monitoring of the common agricultural policy. Remote Sens., 10.10.3390/rs10060911 ↗
30. Orusa, T., and Borgogno Mondino, E. (2021). Exploring Short-term climate change effects on rangelands and broad-leaved forests by free satellite data in Aosta Valley (Northwest Italy). Climate, 9.10.3390/cli9030047 ↗
31. Landsat 8 thermal data to support urban management and planning in the climate change era: A case study in Torino area, NW Italy
32. Carella, E., Orusa, T., Viani, A., Meloni, D., Borgogno-Mondino, E., and Orusa, R. (2022). An Integrated, Tentative Remote-Sensing Approach Based on NDVI Entropy to Model Canine Distemper Virus in Wildlife and to Prompt Science-Based Management Policies. Animals, 12.10.3390/ani12081049 ↗
33. De Marinis, P., De Petris, S., Sarvia, F., Manfron, G., Momo, E.J., Orusa, T., and Borgogno, E.M. (2021). Supporting Pro-Poor Reforms of Agricultural Systems in Eastern DRC (Africa) with Remotely Sensed Data: A Possible Contribution of Spatial Entropy to Interpret Land Management Practices. Land, 10.10.3390/land10121368 ↗
34. District Census Handbook (2016). Village and Town Directory.
35. ICAR-Central Arid Zone Research Institute, Jodhpur: Erosion Processes and Desertification in the Thar Desert of India10.16943/ptinsa/2016/48507 ↗
36. (2022, October 12). Survey of India: State Map of Rajasthan, Available online: https://surveyofindia.gov.in/files/Raj_State_Map.pdf.
37. (2022, October 15). Earth Engine Data Catalog: Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-2A. Available online: https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED#bands.
38. Wang, R., Cherkauer, K., and Bowling, L. (2016). Corn response to climate stress detected with satellite-based NDVI time series. Remote Sens., 8.10.3390/rs8040269 ↗
39. Detection of rice phenology through time series analysis of ground-based spectral index data10.1016/j.fcr.2016.08.027 ↗
40. Smoothing and differentiation of data by simplified least squares procedures10.1021/ac60214a047 ↗
41. A generalized approach based on convolutional neural networks for large area cropland mapping at very high resolution10.1016/j.rse.2020.111912 ↗
42. Capability of integrated MODIS imagery and ALOS for oil palm, rubber and forest areas mapping in tropical forest regions10.3390/s140508259 ↗
43. Kwak, G.H., Park, C.W., Lee, K.D., Na, S.I., Ahn, H.Y., and Park, N.W. (2021). Potential of hybrid CNN-RF model for early crop mapping with limited input data. Remote Sens., 13.10.3390/rs13091629 ↗
44. Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine