Mapping up-to-Date Paddy Rice Extent at 10 M Resolution in China through the Integration of Optical and Synthetic Aperture Radar Images

Abstract

Rice is a staple food in East Asia and Southeast Asia—an area that accounts for more than half of the world’s population, and 11% of its cultivated land. Studies on rice monitoring can provide direct or indirect information on food security, and water source management. Remote sensing has proven to be the most effective method for the large-scale monitoring of croplands, by using temporary and spectral information. The Google Earth Engine (GEE) is a cloud-based platform providing access to high-performance computing resources for processing extremely large geospatial datasets. In this study, by leveraging the computational power of GEE and a large pool of satellite and other geophysical data (e.g., forest and water extent maps, with high accuracy at 30 m), we generated the first up-to-date rice extent map with crop intensity, at 10 m resolution in the three provinces with the highest rice production in China (the Heilongjiang, Hunan and Guangxi provinces). Optical and synthetic aperture radar (SAR) data were monthly and metric composited to ensure a sufficient amount of up-to-date data without cloud interference. To remove the common confounding noise in the pixel-based classification results at medium to high resolution, we integrated the pixel-based classification (using a random forest classifier) result with the object-based segmentation (using a simple linear iterative clustering (SLIC) method). This integration resulted in the rice planted area data that most closely resembled official statistics. The overall accuracy was approximately 90%, which was validated by ground crop field points. The F scores reached 87.78% in the Heilongjiang Province for monocropped rice, 89.97% and 80.00% in the Hunan Province for mono- and double-cropped rice, respectively, and 88.24% in the Guangxi Province for double-cropped rice.

Efficient Identification of Corn Cultivation Area with Multitemporal Synthetic Aperture Radar and Optical Images in the Google Earth Engine Cloud Platform

“…Compared with optical images for which repeat observations can provide more information for a time series, the overlap of SAR is redundant, because the incidence in overlap area ranges from approximately 30 to 50 degrees. Therefore, the image with a lower incidence angle is kept in the repeated observational areas [1].…”

“…Food security a major challenge to the sustainable development of human beings [1,2], and the eradication of hunger is the second target of the United Nation (UN)'s Sustainable Development Goals (SDGs) [3]. Corn serves as livestock feed, industrial raw material, and human food, and is one of the cardinal crops in the world.…”

“…The corn area estimated from optical and SAR images was well correlated with the census data, with an R 2 = 0.91 and a root mean square error (RMSE) of 470.90 km 2 . The results of the corn map are expected to provide detailed information for optimizing crop structure and water management, which are critical issues in this region.Traditional approaches to obtaining crop information, such as ground surveys and sampling, are time consuming, labor intensive, and costly, and cannot obtain continuous spatial distribution data for crops [1,6]. Earth observation satellites that monitor and regularly revisit cropland are inexpensive and excellent data sources that provide full spatial detailed information for crop mapping [8,9].…”

“…Earth observation satellites that monitor and regularly revisit cropland are inexpensive and excellent data sources that provide full spatial detailed information for crop mapping [8,9]. In recent decades, many studies have focused on crop monitoring with optical [10-14] and synthetic aperture radar (SAR) images [1,[15][16][17][18]. Multitemporal and time series data are powerful tools for identifying different crops and handling the problem of spectral similarity in heterogeneous underlying surfaces in single-period imagery [19][20][21][22][23].…”

“…Traditional approaches to obtaining crop information, such as ground surveys and sampling, are time consuming, labor intensive, and costly, and cannot obtain continuous spatial distribution data for crops [1,6]. Earth observation satellites that monitor and regularly revisit cropland are inexpensive and excellent data sources that provide full spatial detailed information for crop mapping [8,9].…”