SEPARABILITY OF TRANSPLANTED AND DIRECT SEEDED RICE USING MULTI-TEMPORAL SENTINEL-1A DATA

• Main Authors: L. Villano, J. Raviz, N. M. Paguirigan, M. A. Gutierrez, M. R. Mabalay, A. Laborte
• Format: Article
• Language: English
• Published: Copernicus Publications 2019-12-01
• Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W19/471/2019/isprs-archives-XLII-4-W19-471-2019.pdf

Occurrence of pests and diseases are influenced by several factors including weather, landscape and field-level factors such as crop management practices including crop establishment method. In this paper, we adopted and applied a method using Sentinel-1A (S-1A) Synthetic Aperture Radar (SAR) intensity to discriminate between rice fields that are transplanted and direct seeded to come up with a robust method for automated classification of crop establishment method. Multi-temporal S-1A C-band dual polarization images at 20m resolution covering the wet cropping season over four provinces in the Philippines were acquired from March to November 2018. Field measurements, observations and interviews were conducted on 186 sample fields and mean backscatter values for each of the sampled fields were generated from S-1A data acquired during the season. The reported dates of land preparation and estimated dates of crop growth stages were matched with the corresponding SAR acquisition dates. We used the Mann-Whitney U test to identify growth stages for which there are significant differences in backscatter values between transplanted and direct seeded rice. The results are generally consistent with the findings of a previous study conducted in one province in the Philippines in the dry season of 2017. We found, however, some inconsistencies in terms of the polarization where the significant differences were observed. These findings demonstrate the possibility of discriminating transplanted from direct seeded rice using SAR temporal data but suggests further fine tuning in the methodology is needed for different locations and seasons.