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

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.

Download Full-text

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

Remote Sensing ◽

10.3390/rs11060629 ◽

2019 ◽

Vol 11 (6) ◽

pp. 629 ◽

Cited By ~ 18

Author(s):

Fuyou Tian ◽

Bingfang Wu ◽

Hongwei Zeng ◽

Xin Zhang ◽

Jiaming Xu

Keyword(s):

Synthetic Aperture Radar ◽

Large Scale ◽

Census Data ◽

Google Earth ◽

Synthetic Aperture ◽

Cloud Platform ◽

Optical Images ◽

Google Earth Engine ◽

Sentinel 2 ◽

Aperture Radar

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 R2 = 0.91 and a root mean square error (RMSE) of 470.90 km2. 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.

Download Full-text

Change Detection and Flood Water Mapping Using Sentinel-1A Synthetic Aperture Radar Images

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2019.8321 ◽

2019 ◽

Vol 16 (8) ◽

pp. 3544-3549

Author(s):

N. Anusha ◽

B. Bharathi

Keyword(s):

Synthetic Aperture Radar ◽

Uttar Pradesh ◽

Google Earth ◽

Water Levels ◽

Control Measures ◽

Synthetic Aperture ◽

Radar Images ◽

Sar Data ◽

Specular Reflector ◽

Aperture Radar

Prodigious flooding in the state of Uttar Pradesh, India during the month of August 2017 was induced by heavy rainfall, causing water levels in several rivers to cross the danger mark bringing normal life to a standstill. The peculiar rainfall pattern in India makes it highly vulnerable to floods. Demand for crisis information, for instance, natural disasters like severe flood events has increased. A simple but effective method is proposed in this study to find the areas that are affected due to floods, to detect the changes and for flood mapping. These indicators were derived from the Sentinel-1A Synthetic Aperture Radar (SAR) data by taking the crisis and archive images. An open flood surface can be detected easily in SAR data as it acts as a specular reflector that scatters the energy away from the sensor, causing relatively dark pixels of low backscattered SAR data. In contrast, the surrounding non-water areas usually exhibit a higher return due to surface roughness. Red, Green, Blue (RGB) composite is made for highlighting the flooded areas and for detecting changes by combining both archive and crisis images. Finally the flood map is compared with the optical imagery on the Google earth by integrating the resultant RGB composite image on the Google earth. Identification of the flood-prone areas is crucial to action the appropriate control measures in the flood-affected regions.

Download Full-text

Quantification of Mangrove Extent using a Combination of Optical and Radar Images in Google Earth Engine Platform: The case of Anlo Beach Wetland Complex, Shama District, Western Region, Ghana

10.21203/rs.3.rs-398182/v1 ◽

2021 ◽

Author(s):

Daniel Aja ◽

Michael Miyittah ◽

Donatus Bapentire Angnuureng

Keyword(s):

Synthetic Aperture Radar ◽

Google Earth ◽

Kappa Coefficient ◽

Synthetic Aperture ◽

Optical Data ◽

Radar Images ◽

Google Earth Engine ◽

Sar Data ◽

Aperture Radar

Abstract Mangrove Forest classification in tropical coastal zones based on only passive remote sensing methods is hampered by Mangrove complexities, topographic considerations and cloud cover effects among other things. This paper reports on a novel approach that combines Optical Satellite images and Synthetic Aperture Radar alongside their derived parameters to overcome the challenges of distinguishing Mangrove stand in cloud prone regions. Google Earth Engine (GEE) cloud-based geospatial processing platform was used to extract several scenes of Landsat Surface Reflectance Tier1 and synthetic aperture radar (C-band and L-band). The imageries were enhanced by creating a function that masks out clouds from the optical satellite image and by using speckle filter to remove noise from the radar data. The random forest algorithm proved to be a robust and accurate machine learning approach for mangrove classification and assessment. Our result show that about 16% of the mangrove extent was lost in the last decade. The accuracy was assessed based on three classification scenarios: classification of optical data only, classification of SAR data only, and combination of both optical and SAR data. The overall accuracies were 99.1% (Kappa Coefficient =0.797), 84.6% (Kappa Coefficient = 0.687) and 98.9% (Kappa Coefficient = 0.828) respectively. This case study demonstrates how mangrove mapping can help focus conservation practices locally in climate change setting, coupled with sea level rise and related threats to coastal ecosystems.

Download Full-text

Urban objects detection from C-band synthetic aperture radar (SAR) satellite images through simulating filter properties

Scientific Reports ◽

10.1038/s41598-021-85121-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Deepak Kumar

Keyword(s):

Remote Sensing ◽

Synthetic Aperture Radar ◽

Microwave Remote Sensing ◽

Google Earth ◽

Alternative Methods ◽

Synthetic Aperture ◽

Radar Images ◽

Radar Sensing ◽

Objects Detection ◽

Aperture Radar

AbstractSatellite-based remote sensing has a key role in the monitoring earth features, but due to flaws like cloud penetration capability and selective duration for remote sensing in traditional remote sensing methods, now the attention has shifted towards the use of alternative methods such as microwave or radar sensing technology. Microwave remote sensing utilizes synthetic aperture radar (SAR) technology for remote sensing and it can operate in all weather conditions. Previous researchers have reported about effects of SAR pre-processing for urban objects detection and mapping. Preparing high accuracy urban maps are critical to disaster planning and response efforts, thus result from this study can help to users on the required pre-processing steps and its effects. Owing to the induced errors (such as calibration, geometric, speckle noise) in the radar images, these images are affected by several distortions, therefore these distortions need to be processed before any applications, as it causes issues in image interpretation and these can destroy valuable information about shapes, size, pattern and tone of various desired objects. The present work aims to utilize the sentinel-1 SAR datasets for urban studies (i.e. urban object detection through simulation of filter properties). The work uses C-band SAR datasets acquired from Sentinel-1A/B sensor, and the Google Earth datasets to validate the recognized objects. It was observed that the Refined-Lee filter performed well to provide detailed information about the various urban objects. It was established that the attempted approach cannot be generalised as one suitable method for sensing or identifying accurate urban objects from the C-band SAR images. Hence some more datasets in different polarisation combinations are required to be attempted.

Download Full-text