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

Abstract

More than 50% of the world’s population consumes rice. Accurate and up-to-date information on rice field extent is important to help manage food and water security. Currently, field surveys or MODIS satellite data are used to estimate rice growing areas. This study presents a cost-effective methodology for near-real-time mapping and monitoring of rice growth extent and cropping patterns over a large area. This novel method produces high-resolution monthly maps (10 m resolution) of rice growing areas, as well as rice growth stages. The method integrates temporal Sentinel-1 data and rice phenological parameters with the Google Earth Engine (GEE) cloud-based platform. It uses monthly median time series of Sentinel-1 at VH polarization from September 2016 to October 2018. The two study areas are the northern region of West Java, Indonesia (0.75 million ha), and the Kedah and Perlis states in Malaysia (over 1 million ha). K-means clustering, hierarchical cluster analysis (HCA), and a visual interpretation of VH polarization time series profiles are used to generate rice extent, cropping patterns, and spatiotemporal distribution of growth stages. To automate the process, four supervised classification methods (support vector machine (SVM), artificial neural networks (ANN), random forests, and C5.0 classification models) were independently trialled to identify cluster labels. The results from each classification method were compared. The method can also forecast rice extent for up to two months. The VH polarization data can identify four growth stages of rice—T&P: tillage and planting (30 days); V: vegetative-1 and 2 (60 days); R: reproductive (30 days); M: maturity (30 days). Compared to field survey data, this method measures overall rice extent with an accuracy of 96.5% and a kappa coefficient of 0.92. SVM and ANN show better performance than random forest and C5.0 models. This simple and robust method could be rolled out across Southeast Asia, and could be used as an alternative to time-consuming, expensive field surveys.

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

“…GEE hosted extensive publicly available RS datasets that can be effectively utilized for productivity, quality, profitability, and sustainability studies of agriculture production. Researchers have applied GEE to plantation mapping and monitoring [75], [76], phenology-based classification [77], [78], cropland mapping [79], [80], crop condition monitoring [81], [82], crop yield estimation [83], [84], irrigation mapping [85], [86], and other agricultural studies [87], [88]. For example, seasonal median composites of Sentinel-1 and Sentinel-2 were calculated in GEE to predict the Maize yield in Kenya and Tanzania [83].…”

Towards Cultivating Black Pepper Using Geospatial Technology for Growth Monitoring and Mapping: A Review for Small Scale Practice in Malaysia

“…Geospatial technology is one of the categorizations in precision agriculture where the integration of technologies such as geographical information system (GIS), global navigation satellite system (GNSS) and remote sensing, resulting in decision making through accurate and timely manner data collection and analysis (Martinez, 2017;Prim, 2017). Data sources of geospatial can be obtained as spatial product or spectral resolution (Rudiyanto et al, 2019). Analysis of geospatial application for agriculture sector may encompass biophysical characteristics, growth monitoring, yield forecasting and market prediction (Nurmiaty et al, 2019).…”

“…The component helps in producing accurate crop maps throughout their growing season (Li & Chen, 2011;Song et al, 2017;Sousa & Small, 2019) hence reassure for precise decision making. Recently, increase application of remotely sensed data for monitoring altered by the availability of open data, advanced machine learning methods, and access to cloud computing platforms to handle big data storage and processing (Kussul et al, 2018;Rudiyanto et al, 2019). Apart from that, these methods also provide a cost-effective and site-specific assessment for monitoring the crop growth condition in real-time (Carolita et al, 2018).…”

“…Satellite data sources for mapping can be obtained through optical product (Landsat, Sentinel-2, MODIS, RapidEye, SPOT and PROB-V) and synthetic aperture radar (SAR) or microwave product (Sentinel-1, RADARSAT) (Rudiyanto et al, 2019). Optical systems measure reflected sunlight and provide spectral properties of their targets.…”

Google Earth Engine for the Detection of Soiling on Photovoltaic Solar Panels in Arid Environments

“…The GEE is an open-source platform with great potential in expanding the research frontiers. The GEE has also been used in various applications such as monitoring of rice extent, cropping patterns, and growth stages [28]. Archived Landsat 8 and Sentinel-2 images from September 2017 to February 2019 are used in the GEE for the analysis.…”