Proceedings Rice Monitoring Using Sentinel-1 data in Google Earth Engine Platform+
C. Dineshkumar 1,*, J. Satish Kumar 1 and S. Nitheshnirmal 2
1 Department of Civil Engineering, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India; dineshchandrasekar.dk@gmail.com
2 Department of Geography, School of Earth Science, Bharathidasan University, Tiruchirappalli, Tamil Nadu, India; nirmalgsarath@gmail.com
* Correspondence:
dineshchandrasekar.dk@gmail.com; Tel.:
+91-9787690660 + Presented at the 2nd International Electronic Conference on Geosciences (IECG 2019), 8-15 June 2019.
Rice is the most essential and nutritional staple food crop worldwide. There is a need for accurate and timely rice mapping and monitoring which is a pre-requisite for crop management and food security. Recent studies utilize Sentinel-1 data for mapping and monitoring rice grown area. The present study was carried out in the Google Earth Engine (GEE), where the Sentinel-1data were used for monitoring the rice grown area over Kulithalai taluk of Karur district, located along the Cauvery delta region. Normally, the production of rice in the study area starts in the late Samba Season where the long duration variety Cr1009 (130 days) is extensively grown. The results exhibit a low backscattering values during the transplanting stage of VV and VH Polarization (-15.19 db and -24 db), whereas maximum backscattering is experienced at peak vegetation stage of VV and VH Polarization (-7.42 and -16.9 db) and there is a decrease in the backscattering values after attaining the maturity stage. Amongst VH and VV Polarization, VH Polarization provides a consistently increasing trend in backscatter coefficients from the panicle initiation phase to the early milking phase after which the crop attains its maturity phase, whereas in the VV Polarization early peak of backscatter coefficients are seen at much earlier during the flowering phase itself. Thus, in this study, VV Polarization gives better interpretation than VH Polarization in the selected rice crop fields. The obtained results were cross-validated by collecting the ground truth values during the satellite data acquisition time, throughout the crop growing period from the selected rice fields.
Keywords: Rice Monitoring, Sentinel-1, Google Earth Engine, Growing Stages, Backscatter
5. Conclusion
The present study has been carried out in order to calculate the backscattering of VV and VH polarized images during different phenological stages of rice in the study area using the GEE cloud platform. The results from this clearly shows that VV Polarization has high backscattering coefficients using which the length of growing period and the rice grown crop fields can be mapped and monitored. It became evident from this study, that the usage of GEE platform helped in quick pre-processing and reduced the space for the data acquisition. Using the parallel power of GEE platform, rice cropland present in every Delta Zone of the whole country can be mapped easily. Author Contributions: “Conceptualization, C.D., J.S.K., S. N.; Methodology, C.D. and S.N.; Software, C.D. and S.N.; Validation, C.D. and S.N. and J.S.K; Writing-Original Draft Preparation, S.N.; Writing-Review & Editing, S.N. and C.D.; Supervision, J.S.K.”. Funding: This research received no external funding Conflicts of Interest: The authors declare no conflict of interest
Figure 2. Methodology
Figure 3. Deriving backscatter coefficients using Google Earth Engine
Figure 4. Backscattering trend through different phenological stages of rice
Figure 5. (a) VV Polarization backscatter images and (b) VH Polarization backscatter images
Figure 6. Optical rice field photographs
Figure 7. VV and VH Polarization backscattering coefficients during different phenological stages of rice
References
1. Muthayya, S.; Sugimoto, J.D.; Montgomery, S.; Maberly, G.F. An overview of global rice production, supply, trade, and consumption. Annals of the New York Academy of Sciences 2014, 1324, 7-14, DOI: 10.1111/nyas.12540.
2. Akinbile, C.O.; Abd El-Latif, K.M.; Abdullah, R.; Yusoff, M. S. Rice Production and Water use Efficiency for Self-Sufficiency in Malaysia: A Review. Trends in Applied Sciences Research 2011, 6, 1127-1140, DOI:
10.3923/tasr.2011.1127.1140.
3. Masutomi, Y.; Takahashi, K.; Harasawa, H.; Matsuoka, Y. Impact Assessment of Climate Change on Rice Production in Asia in Comprehensive Consideration of Process/Parameter Uncertainty in General Circulation Models. Agriculture, Ecosystems & Environment 2009, 131(3-4), 281-291, DOI: 10.1016/j.agee.2009.02.004.
4. Nguyen, D.B.; Gruber, A.; Wagner, W. Mapping rice extent and cropping scheme in the Mekong Delta using Sentinel-1A data. Remote Sensing Letters 2016, 7(12), 1209-1218, DOI:
10.1080/2150704X.2016.1225172.
5. Toan, T.L.; Ribbes, F.; Wang, L.F.; Floury, N.; Ding, K.H.; Kong, J.A.; Fujita, M.; Kurosu, T. Rice Crop Mapping and Monitoring Using ERS-1 Data Based on Experiment and Modeling Results. IEEE Transactions on Geoscience and Remote Sensing 1997, 35(1), 41–56, DOI:
10.1109/36.551933.
6. Onojeghuo, A.O.; Blackburn, G.A.; Wang, Q.; Atkinson, P.M.; Kindred, D.; Miao, Y. Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data, International Journal of Remote Sensing 2018, 39(4), 1042-1067, DOI: 10.1080/01431161.2017.1395969.
7. Kuenzer, C.; Knauer, K. Remote Sensing of Rice Crop Areas. International Journal of Remote Sensing 2013, 34(6), 2101–2139, DOI: 10.1080/01431161.2012.738946.
8. Mosleh, M.; Hassan, Q.; Chowdhury, E. Application of Remote Sensors in Mapping Rice Area and Forecasting Its Production: A Review. Sensors 2015, 15(1), 769–791, DOI: 10.3390/s150100769.
9. Bazzi, H.; Baghdadi, N.; Hajj, M.E.; Zribi, M.; Minh, D.H.T.; Ndikumana, E.; Courault, D.; Belhouchette, H. Mapping Paddy Rice Using Sentinel-1 SAR Time Series in Camargue, France. Remote Sensing 2019, 11, 887, DOI:
10.3390/rs11070887. 10. Ndikumana, E.; Ho Tong Minh, D.; Baghdadi, N.; Courault, D.; Hossard, L. Deep Recurrent Neural Network for Agricultural Classification using multitemporal SAR Sentinel-1 for Camargue, France. Remote Sensing 2018, 10(8), 1217, DOI: 10.3390/rs10081217.
11. Gorelick, N.; Hancher, M.; Dixon, M.; Ilyushchenko, S.; Thau, D.; Moore, R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 2017, 202(1), 18-27, DOI: 10.1016/j.rse.2017.06.031.
12. Sidhu, N.; Pebesma, E.; Camara, G. Using Google Earth Engine to detect land cover change: Singapore as a use case. European Journal of Remote Sensing 2018, 51(1), 486-500, DOI:
10.1080/22797254.2018.1451782.
13. Shelestov, A.; Lavreniuk, M.; Kussul, N.; Novikov, A.; Skakun, S. Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping. Front. Earth Sci. 2017, 5, 17, DOI: 10.3389/feart.2017.00017.
14. Singha, M.; Dong, J.; Zhang, G.; Xiao, X. High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data. Scientific Data 2019, 6(26), DOI: 10.1038/s41597-019-0036-3.
Full Text
download Click here
Read and download Click here
ليست هناك تعليقات:
إرسال تعليق