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

Abstract

The distribution of corn cultivation areas is crucial for ensuring food security, eradicating hunger, adjusting crop structures, and managing water resources. The emergence of high-resolution images, such as Sentinel-1 and Sentinel-2, enables the identification of corn at the field scale, and these images can be applied on a large scale with the support of cloud computing technology. Hebei Province is the major production area of corn in China, and faces serious groundwater overexploitation due to irrigation. Corn was mapped using multitemporal synthetic aperture radar (SAR) and optical images in the Google Earth Engine (GEE) cloud platform. A total of 1712 scenes of Sentinel-2 data and 206 scenes of Sentinel-1 data acquired from June to October 2017 were processed to composite image metrics as input to a random forest (RF) classifier. To avoid speckle noise in the classification results, the pixel-based classification result was integrated with the object segmentation boundary completed in eCognition software to generate an object-based corn map according to crop intensity. The results indicated that the approach using multitemporal SAR and optical images in the GEE cloud platform is reliable for corn mapping. The corn map had a high F1-Score of 90.08% and overall accuracy of 89.89% according to the test dataset, which was not involved in model training. 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]. 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]. The use of coarse spatial resolution images as original data at scales of hundreds of meters, such as Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High-Resolution Radiometer (AVHRR), is predominant in previous studies because of the fine temporal resolution of the Aqua and Terra satellites. These two satellites revisit the same location at least four times per day, and provide the composition products for free [24][25][26][27][28]. To distinguish cropland and grassland, Estel et al. used MODIS normalized differenced vegetation index (NDVI) time series and rand...

Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids

“…However, mapping croplands over large areas with multi-spectral, multi-temporal remotely sensed data remains challenging due to the large volume of data caused by large area and the inconsistent of available imagery caused by cloudiness and uneven revisit times. A series of cloud computing platform were introduced, which greatly solving the calculation problem caused by a large volume of data [9][10][11][12][13]. In addition, both NASA's NEX system for global processing and Infor Terra's Pixel Factory for massive imagery auto processing use cluster-based platforms (a group of computers that are interconnected by a high-speed network) for calculation speed optimization [14,15].…”

“…However, due to the temporal inconsistency of image acquisition, there are still limitations in multi-temporal cropland classification [16][17][18][19][20][21][22], including (a) most of supervised classifiers require consistent number of features. [23][24][25]; (b) insufficient temporal information is used caused by the abandon of images acquired at a specific time without covering the full study area [9,26,27]. Thus, the overall accuracy of large scale cropland mapping is less than ideal (between 66% and 79%) [16,18,28,29].…”

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

“…The GEE greatly improves the processing efficiency when using substantial amounts of remote sensing data. In recent years, the GEE was used in land cover mapping [49][50][51][52][53][54][55][56][57][58], agricultural applications [59][60][61][62][63], disaster management, and earth sciences studies [64][65][66]. This remote sensing data processing cloud platform makes the rapid processing of Sentinel-2 images covering large areas possible.…”

Understanding the Spatial Distribution of Urban Forests in China Using Sentinel-2 Images with Google Earth Engine

“…GEE did not ingest images with atmospheric correction when we executed our classification. Moreover, it was difficult to perform the atmospheric correction in the GEE platform because of difficulties in parameter acquisition [31,40,48]. Therefore, the top-of-atmosphere (TOA) reflectance data, from Sentinel-2, was directly used to extract urban forest cover in this study, which may affect the results to some extent.…”

“…We used an RF classifier to gather knowledge based on training data. The RF classifier is conducive to mapping land cover by mitigating the influence of data noise and overfitting [31,39,40]. It is a non-parametric machine learning method, used to construct multiple random decision trees, each of them possessing several nodes to divide the input pixels into different classes until each node represents every class [41,42].…”