Mapping Paddy Cropland in Guntur District using Machine Learning and Google Earth Engine utilizing Images from Sentinel-1 and Sentinel-2

Authors

  • Pureti Siva Nagendram Department of ECE, KLEF, India
  • Penke Satyanarayana Department of IOT, KLEF, India
  • Panduranga Ravi Teja Department of IOT, KLEF, India
Volume: 13 | Issue: 6 | Pages: 12427-12432 | December 2023 | https://doi.org/10.48084/etasr.6460

Abstract

Ensuring global food security necessitates vigilant monitoring of crop quantity and quality. Therefore, the reliable classification of croplands and diverse Land Covers (LC) becomes pivotal in fostering sustainable agricultural progress and safeguarding national food security. The Seasonal Crop Inventory (SCI) emerges as a strong asset. In this study, Sentinel-1 (S1) and Sentinel-2 (S2) image data were used to show varied land uses and paddy crops in Guntur district, Andhra Pradesh, India, during the 2021 growing season. Employing a technologically advanced space-based remote sensing approach, this study exploited the Google Earth Engine (GEE) and a range of classification techniques, including Random Forest (RF) and Classification Regression Trees (CART), to generate pixel-based SCI tailored to the area under investigation. The results underscored the reliability of GEE-based cropland mapping in the region, demonstrating a satisfactory level of classification accuracy, surpassing 97% across distinct time intervals in overall accuracy values, Kappa coefficients, and F1-Score.

Keywords:

paddy, crop land mapping, machine learning, GEE, Guntur, Sentinel-1 and Sentinel-2

Downloads

Download data is not yet available.

References

L. Xu et al., "Paddy Rice Mapping in Thailand Using Time-Series Sentinel-1 Data and Deep Learning Model," Remote Sensing, vol. 13, no. 19, 2021.

Dasar Agromakanan Negara 2011-2020. Kementerian Pertanian dan Industri Asas Tani Malaysia, 2011, 2021.

X. Xiao et al., "Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images," Remote Sensing of Environment, vol. 100, no. 1, pp. 95–113, Jan. 2006.

X. Xiao et al., "Mapping paddy rice agriculture in southern China using multi-temporal MODIS images," Remote Sensing of Environment, vol. 95, no. 4, pp. 480–492, Apr. 2005.

A. M. Shew and A. Ghosh, "Identifying Dry-Season Rice-Planting Patterns in Bangladesh Using the Landsat Archive," Remote Sensing, vol. 11, no. 10, 2019.

J. Dong et al., "Northward expansion of paddy rice in northeastern Asia during 2000–2014," Geophysical Research Letters, vol. 43, no. 8, pp. 3754–3761, 2016.

Z. Liu, Q. Hu, J. Tan, and J. Zou, "Regional scale mapping of fractional rice cropping change using a phenology-based temporal mixture analysis," International Journal of Remote Sensing, vol. 40, no. 7, pp. 2703–2716, Apr. 2019.

J. Dong et al., "Tracking the dynamics of paddy rice planting area in 1986–2010 through time series Landsat images and phenology-based algorithms," Remote Sensing of Environment, vol. 160, pp. 99–113, Apr. 2015.

K. Lasko, K. P. Vadrevu, V. T. Tran, and C. Justice, "Mapping Double and Single Crop Paddy Rice With Sentinel-1A at Varying Spatial Scales and Polarizations in Hanoi, Vietnam," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 2, pp. 498–512, Oct. 2018.

N. T. Son, C. F. Chen, C. R. Chen, and V. Q. Minh, "Assessment of Sentinel-1A data for rice crop classification using random forests and support vector machines," Geocarto International, vol. 33, no. 6, pp. 587–601, Jun. 2018.

H. Yang, B. Pan, W. Wu, and J. Tai, "Field-based rice classification in Wuhua county through integration of multi-temporal Sentinel-1A and Landsat-8 OLI data," International Journal of Applied Earth Observation and Geoinformation, vol. 69, pp. 226–236, Jul. 2018.

H. Tian, M. Wu, L. Wang, and Z. Niu, "Mapping Early, Middle and Late Rice Extent Using Sentinel-1A and Landsat-8 Data in the Poyang Lake Plain, China," Sensors, vol. 18, no. 1, 2018.

L. R. Mansaray, D. Zhang, Z. Zhou, and J. Huang, "Evaluating the potential of temporal Sentinel-1A data for paddy rice discrimination at local scales," Remote Sensing Letters, vol. 8, no. 10, pp. 967–976, Oct. 2017.

L. R. Mansaray, W. Huang, D. Zhang, J. Huang, and J. Li, "Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets," Remote Sensing, vol. 9, no. 3, Mar. 2017, Art. no. 257.

K. Clauss, M. Ottinger, and C. Kuenzer, "Mapping rice areas with Sentinel-1 time series and superpixel segmentation," International Journal of Remote Sensing, vol. 39, no. 5, pp. 1399–1420, Mar. 2018.

J. D. Mohite et al., "Operational Near Real Time Rice Area Mapping Using Multi-Temporal Sentinel-1 SAR Observations," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII–4, pp. 433–438, Sep. 2018.

D. Mandal, V. Kumar, A. Bhattacharya, Y. S. Rao, P. Siqueira, and S. Bera, "Sen4Rice: A Processing Chain for Differentiating Early and Late Transplanted Rice Using Time-Series Sentinel-1 SAR Data With Google Earth Engine," IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 12, pp. 1947–1951, Sep. 2018.

D. B. Nguyen and W. Wagner, "European Rice Cropland Mapping with Sentinel-1 Data: The Mediterranean Region Case Study," Water, vol. 9, no. 6, 2017.

M. Saadat, M. Hasanlou, and S. Homayouni, "Rice Crop Mapping Using Sentinel-1 Time Series Images (Case Study: Mazandaran, Iran)," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-4-W18, pp. 897–904, Oct. 2019.

M. Singha, J. Dong, G. Zhang, and X. Xiao, "High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data," Scientific Data, vol. 6, no. 1, Apr. 2019, Art. no. 26.

S. Park, J. Im, S. Park, C. Yoo, H. Han, and J. Rhee, "Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data," Remote Sensing, vol. 10, no. 3, 2018.

Rudiyanto, B. Minasny, R. M. Shah, N. Che Soh, C. Arif, and B. Indra Setiawan, "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 Platform," Remote Sensing, vol. 11, no. 14, 2019.

V. N. Fikriyah, R. Darvishzadeh, A. Laborte, N. I. Khan, and A. Nelson, "Discriminating transplanted and direct seeded rice using Sentinel-1 intensity data," International Journal of Applied Earth Observation and Geoinformation, vol. 76, pp. 143–153, Apr. 2019.

T. D. Setiyono et al., "Rice yield estimation using synthetic aperture radar (SAR) and the ORYZA crop growth model: development and application of the system in South and South-east Asian countries," International Journal of Remote Sensing, vol. 40, no. 21, pp. 8093–8124, Nov. 2019.

N. Torbick, D. Chowdhury, W. Salas, and J. Qi, "Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2," Remote Sensing, vol. 9, no. 2, 2017.

A. M. Ali et al., "Integrated method for rice cultivation monitoring using Sentinel-2 data and Leaf Area Index," The Egyptian Journal of Remote Sensing and Space Science, vol. 24, no. 3, Part 1, pp. 431–441, Dec. 2021.

N. You et al., "The 10-m crop type maps in Northeast China during 2017–2019," Scientific Data, vol. 8, no. 1, Feb. 2021, Art. no. 41.

L. Liu et al., "Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine," Remote Sensing of Environment, vol. 239, Mar. 2020, Art. no. 111624.

R. Ni et al., "An enhanced pixel-based phenological feature for accurate paddy rice mapping with Sentinel-2 imagery in Google Earth Engine," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 178, pp. 282–296, Aug. 2021.

S. Inoue, A. Ito, and C. Yonezawa, "Mapping Paddy Fields in Japan by Using a Sentinel-1 SAR Time Series Supplemented by Sentinel-2 Images on Google Earth Engine," Remote Sensing, vol. 12, no. 10, Jan. 2020, Art. no. 1622.

M. Gatcha, F. Messelmi, and S. Saadi, "An Anisotropic Diffusion Adaptive Filter for Image Denoising and Restoration Applied on Satellite Remote Sensing Images: A Case Study," Engineering, Technology & Applied Science Research, vol. 12, no. 6, pp. 9715–9719, Dec. 2022.

Y. Slimani and R. Hedjam, "A Hybrid Metaheuristic and Deep Learning Approach for Change Detection in Remote Sensing Data," Engineering, Technology & Applied Science Research, vol. 12, no. 5, pp. 9351–9356, Oct. 2022.

M. K. Villareal and A. F. Tongco, "Multi-sensor Fusion Workflow for Accurate Classification and Mapping of Sugarcane Crops," Engineering, Technology & Applied Science Research, vol. 9, no. 3, pp. 4085–4091, Jun. 2019.

Downloads

How to Cite

[1]
P. S. Nagendram, P. Satyanarayana, and P. Ravi Teja, “Mapping Paddy Cropland in Guntur District using Machine Learning and Google Earth Engine utilizing Images from Sentinel-1 and Sentinel-2”, Eng. Technol. Appl. Sci. Res., vol. 13, no. 6, pp. 12427–12432, Dec. 2023.

Metrics

Abstract Views: 343
PDF Downloads: 280

Metrics Information