High-Resolution Mapping of Paddy Rice Extent and Growth Stages across Peninsular Malaysia Using a Fusion of Sentinel-1 and 2 Time Series Data in Google Earth Engine

Abstract

Rice is the staple crop for more than half the world’s population, but there is a lack of high-resolution maps outlining rice areas and their growth stages. Most remote sensing studies map the rice extent; however, in tropical regions, rice is grown throughout the year with variable planting dates and cropping frequency. Thus, mapping rice growth stages is more useful than mapping only the extent. This study addressed this challenge by developing a phenology-based method. The hypothesis was that the unsupervised classification (k-means clustering) of Sentinel-1 and 2 time-series data could identify rice fields and growth stages, because (1) the presence of flooding during transplanting can be identified by Sentinel-1 VH backscatter; and (2) changes in the canopy of rice fields during growth stages (vegetative, generative, and ripening phases) up to the point of harvesting can be identified by Normalized Difference Vegetation Index (NDVI) time series. Using the proposed method, this study mapped rice field extent and cropping calendars across Peninsular Malaysia (131,598 km2) on the Google Earth Engine (GEE) platform. The Sentinel-1 and 2 monthly time series data from January 2019 to December 2020 were classified using k-means clustering to identify areas with similar phenological patterns. This approach resulted in 10-meter resolution maps of rice field extent, intensity, and cropping calendars. Validation using very high-resolution street view images from Google Earth showed that the predicted map had an overall accuracy of 95.95%, with a kappa coefficient of 0.92. In addition, the predicted crop calendars agreed well with the local government’s granary data. The results show that the proposed phenology-based method is cost-effective and can accurately map rice fields and growth stages over large areas. The information will be helpful in measuring the achievement of self-sufficiency in rice production and estimates of methane emissions from rice cultivation.

Mapping rice paddy fields in semiarid northeastern Thailand using Sentinel-1 and -2 time series data and phenological clustering

“…For instance, a study conducted by Fatchurrachman, Rudiyanto et. al., (2022) 25 exemplifies the utility of combining Sentinel-1 and Sentinel-2 data for rice paddy mapping in the Mekong Delta. They employed SAR backscatter data from Sentinel-1 to capture rice field inundation dynamics, while Sentinel-2's multispectral imagery was used to discern crop growth stages and land cover variations.…”

Using Time Series Sentinel Images for Object-Oriented Crop Extraction of Planting Structure in the Google Earth Engine

“…Sentinel-1 data used in this study were invoked on the GEE platform by the 'COPER-NICUS/S1_GRD' dataset (synthetic aperture radar GRD product), also with a planting year as the time range, totaling 33 images. The dual-polarized VH band in interference Wide width (IW) mode was chosen because it is more sensitive to rice backscattering than VV [32,40]. The detailed data used by the research are shown in Table 1, and the distribution of the number of effective Sentinel data observations in the rotation cycle of the study area is shown in Figure 3.…”

“…This approach yielded improvements of 5.82% and 2.39% in accuracy compared to the use of Sentinel-1 or Sentinel-2 time series data alone, respectively [29]. Fatchurrachman et al [32] Synthesized the time series dataset of Sentinel-1 VH polarization and Sentinel-2 NDVI month by month, and conducted unsupervised classification of rice by k-means clustering. The overall accuracy and Kappa coefficient of rice extraction were 95.95% and 0.92.…”

The Assessment of the Spatiotemporal Characteristics of the Eco-Environmental Quality in the Chishui River Basin from 2000 to 2020

“…Google Earth Engine (GEE), a cloud-based geospatial analysis platform, can effectively perform large-scale and long-time processing of satellite imagery to detect environmental change patterns [21]. Therefore, GEE has been widely applied to analyze land use changes [22,23], identify crop types and boundaries [24,25], evaluate ecosystem services responses to biotic and abiotic disturbances [26,27] and monitor environmental quality and integrity [28].…”