scholarly journals Pixel-level rice planting information monitoring in Fujin City based on time-series SAR imagery

2021 ◽  
Vol 104 ◽  
pp. 102551
Author(s):  
Jiatai Pang ◽  
Rui Zhang ◽  
Bin Yu ◽  
Mingjie Liao ◽  
Jichao Lv ◽  
...  
Keyword(s):  
Time Series ◽  
Information Monitoring ◽  
Sar Imagery

scholarly journals Combined Multi-Time Series SAR Imagery and InSAR Technology for Rice Identification in Cloudy Regions

2021 ◽  
Vol 11 (15) ◽  
pp. 6923
Author(s):  
Rui Zhang ◽  
Zhanzhong Tang ◽  
Dong Luo ◽  
Hongxia Luo ◽  
Shucheng You ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Identification Accuracy ◽  
Optical Remote Sensing ◽  
Sar Image ◽  
Single Time ◽  
Growing Period ◽  
Sensing Technology ◽  
Sar Imagery ◽  
Cloudy Region

The use of remote sensing technology to monitor farmland is currently the mainstream method for crop research. However, in cloudy and misty regions, the use of optical remote sensing image is limited. Synthetic aperture radar (SAR) technology has many advantages, including high resolution, multi-mode, and multi-polarization. Moreover, it can penetrate clouds and mists, can be used for all-weather and all-time Earth observation, and is sensitive to the shape of ground objects. Therefore, it is widely used in agricultural monitoring. In this study, the polarization backscattering coefficient on time-series SAR images during the rice-growing period was analyzed. The rice identification results and accuracy of InSAR technology were compared with those of three schemes (single-time-phase SAR, multi-time-phase SAR, and combination of multi-time-phase SAR and InSAR). Results show that VV and VH polarization coherence coefficients can well distinguish artificial buildings. In particular, VV polarization coherence coefficients can well distinguish rice from water and vegetation in August and September, whereas VH polarization coherence coefficients can well distinguish rice from water and vegetation in August and October. The rice identification accuracy of single-time series Sentinel-1 SAR image (78%) is lower than that of multi-time series SAR image combined with InSAR technology (81%). In this study, Guanghan City, a cloudy region, was used as the study site, and a good verification result was obtained.

scholarly journals Early Season Mapping of Sugarcane by Applying Machine Learning Algorithms to Sentinel-1A/2 Time Series Data: A Case Study in Zhanjiang City, China

2019 ◽  
Vol 11 (7) ◽  
pp. 861 ◽  
Author(s):  
Hao Jiang ◽  
Dan Li ◽  
Wenlong Jing ◽  
Jianhui Xu ◽  
Jianxi Huang ◽  
...  
Keyword(s):  
Time Series ◽  
South China ◽  
Time Series Data ◽  
Growing Season ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Early Season ◽  
Cloudy Weather ◽  
Sar Imagery ◽  
Harvest Year

More than 90% of the sugar production in China comes from sugarcane, which is widely grown in South China. Optical image time series have proven to be efficient for sugarcane mapping. There are, however, two limitations associated with previous research: one is that the critical observations during the sugarcane growing season are limited due to frequent cloudy weather in South China; the other is that the classification method requires imagery time series covering the entire growing season, which reduces the time efficiency. The Sentinel-1A (S1A) synthetic aperture radar (SAR) data featuring relatively high spatial-temporal resolution provides an ideal data source for all-weather observations. In this study, we attempted to develop a method for the early season mapping of sugarcane. First, we proposed a framework consisting of two procedures: initial sugarcane mapping using the S1A SAR imagery time series, followed by non-vegetation removal using Sentinel-2 optical imagery. Second, we tested the framework using an incremental classification strategy based on S1A imagery covering the entire 2017–2018 sugarcane season. The study area was in Suixi and Leizhou counties of Zhanjiang city, China. Results indicated that an acceptable accuracy, in terms of Kappa coefficient, can be achieved to a level above 0.902 using time series three months before sugarcane harvest. In general, sugarcane mapping utilizing the combination of VH + VV as well as VH polarization alone outperformed mapping using VV alone. Although the XGBoost classifier with VH + VV polarization achieved a maximum accuracy that was slightly lower than the random forest (RF) classifier, the XGBoost shows promising performance in that it was more robust to overfitting with noisy VV time series and the computation speed was 7.7 times faster than RF classifier. The total sugarcane areas in Suixi and Leizhou for the 2017–2018 harvest year estimated by this study were approximately 598.95 km2 and 497.65 km2, respectively. The relative accuracy of the total sugarcane mapping area was approximately 86.3%.

scholarly journals Estimation of Arctic land-fast ice cover based on dual-polarized Sentinel-1 SAR imagery

2018 ◽  
Vol 12 (8) ◽  
pp. 2595-2607 ◽  
Author(s):  
Juha Karvonen
Keyword(s):  
Time Series ◽  
Cross Correlation ◽  
Detection Rates ◽  
Antarctic Research Institute ◽  
Time Period ◽  
Fast Ice ◽  
Antarctic Research ◽  
Overall Performance ◽  
Dual Polarized ◽  
Sar Imagery

Abstract. Here a method for estimating the land-fast ice (LFI) extent from dual-polarized Sentinel-1 SAR mosaics of an Arctic study area over the Kara and Barents seas is presented. The method is based on temporal cross-correlation between adjacent daily SAR mosaics. The results are compared to the LFI of the Russian Arctic and Antarctic Research Institute (AARI) ice charts. Two versions of the method were studied: in the first version (FMI-A) the overall performance was optimized, and in the second version (FMI-B) the target was a low LFI misdetection rate. FMI-A detected over 73 % of the AARI ice chart LFI, and FMI-B a little over 50 % of the AARI ice chart LFI. During the winter months the detection rates were higher than during the melt-down season for both the studied algorithm versions. An LFI time series covering the time period from October 2015 to the end of August 2017 computed using the proposed methodology is provided on the FMI ftp server. The time series will be extended twice annually.

scholarly journals Permafrost Dynamics and Degradation in Polar Arctic From Satellite Radar Observations, Yamal Peninsula

2021 ◽  
Vol 9 ◽  
Author(s):  
Kanayim Teshebaeva ◽  
Ko J. van Huissteden ◽  
Helmut Echtler ◽  
Alexander V. Puzanov ◽  
Dmitry N. Balykin ◽  
...  
Keyword(s):  
Time Series ◽  
Continuous Process ◽  
Yamal Peninsula ◽  
Observation Data ◽  
Surface Dynamics ◽  
Stream Channels ◽  
Small Stream ◽  
Surface Features ◽  
Satellite Radar ◽  
Sar Imagery

We investigate permafrost surface features revealed from satellite radar data in the Siberian arctic at the Yamal peninsula. Surface dynamics analysis based on SRTM and TanDEM-X DEMs shows up to 2 m net loss of surface relief between 2000 and 2014 indicating a highly dynamic landscape. Surface features for the past 14 years reflect an increase in small stream channels and a number of new lakes that developed, likely caused by permafrost thaw. We used Sentinel-1 SAR imagery to measure permafrost surface changes. Owing to limited observation data we analyzed only 2 years. The InSAR time-series has detected surface displacements in three distinct spatial locations during 2017 and 2018. At these three locations, 60–120 mm/yr rates of seasonal surface permafrost changes are observed. Spatial location of seasonal ground displacements aligns well with lithology. One of them is located on marine sediments and is linked to anthropogenic impact on permafrost stability. Two other areas are located within alluvial sediments and are at the top of topographic elevated zones. We discuss the influence of the geologic environment and the potential effect of local upwelling of gas. These combined analyses of InSAR time-series with analysis of geomorphic features from DEMs present an important tool for continuous process monitoring of surface dynamics as part of a global warming risk assessment.

scholarly journals Permafrost seasonal surface changes revealed from Sentinel-1 InSAR time-series, Yamal peninsula

2020 ◽  
Vol 382 ◽  
pp. 183-187
Author(s):  
Kanayim Teshebaeva ◽  
Ko J. van Huissteden ◽  
Alexander V. Puzanov ◽  
Dmitry N. Balykin ◽  
Anton I. Sinitsky ◽  
...  
Keyword(s):  
Time Series ◽  
Active Layer ◽  
Global Climate ◽  
Yamal Peninsula ◽  
Active Layers ◽  
Eurasian Continent ◽  
Time Period ◽  
Sar Imagery ◽  
Displacement Patterns ◽  
Surface Changes

Abstract. Widespread thawing of permafrost in the northern Eurasian continent causes severe problems for infrastructure and global climate. We test the potential of Sentinel-1 SAR imagery to enhance detection of permafrost surface changes in the Siberian lowlands of the northern Eurasian continent at the Yamal peninsula site. We used InSAR time-series technique to detect seasonal surface movements related to permafrost active layer changes. The satellite InSAR time-series analysis has detected continuous movements, subsidence in three zones, which have occurred during the time period from 2017 to 2018. Observed subsidence zones show up to 180 mm yr−1 rates of seasonal active layers changes. These seasonal ground displacement patterns align well with lithology and linked to anthropogenic impact on the permafrost surface changes in the area. The results show that Sentinel-1 mission is of great importance for the longer-term monitoring of active layer thickening in permafrost regions. The combined analyses of the obtained InSAR time series with additional field observations may support regular process monitoring as part of a global warming risk assessment.

Automatic flood extent mapping using long time series of SAR imagery and water levels or discharge data

2020 ◽  
Author(s):  
Tomasz Berezowski
Keyword(s):  
Time Series ◽  
Groundwater Discharge ◽  
Threshold Value ◽  
Water Levels ◽  
Backscatter Coefficient ◽  
Discharge Data ◽  
River Flooding ◽  
Long Time Series ◽  
Long Time ◽  
Sar Imagery

<p>Long time series of flood extent mapping are valuable for flooding frequency analysis, wetlands monitoring and hydrological model validation. In this study an automatic algorithm for flood extent mapping using long time series of synthetic aperture radar (SAR) imagery and observed water levels or discharge is presented. The key assumption of this algorithm is that the flooding extent is correlated to these two observed variables and the highest correlation is obtained when the flood/no flood threshold value of SAR backscatter coefficient is optimal. This study is conducted in the Biebrza River floodplain (approximately 220km<sup>2</sup>) located in NE Poland. The floodplain is a natural wetland, relatively untouched by human, with complex inundation that involves not only river flooding, but also groundwater discharge and rain or snowmelt local inundation. In order to map 2014-2018 flooding series the automatic thresholding algorithm is run on Sentinel 1 data from one relative orbit, yielding 161 SAR scenes. The estimated 2014-2018  water line match well water levels from independent water gauge and the inundation maps agree with the MODIS 500m reflectance image. This approach was unable to identify inundation in remote parts of the floodplain except very intensive groundwater discharge events. This behavior may have several reasons, of which the most probable are that the dense vegetation obscuring inundated ground and that groundwater, snowmelt or rainfall inundation is not correlated to the variables recorded at a water gauge located in the river.</p>

Landfast ice zoning from SAR imagery

2020 ◽  
Author(s):  
Valeria Selyuzhenok ◽  
Denis Demchev ◽  
Thomas Krumpen
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Drift ◽  
Landfast Ice ◽  
Sea Ice Drift ◽  
Ice Conditions ◽  
Sar Imagery

<p>Landfast sea ice is a dominant sea ice feature of the Arctic coastal region. As a part of Arctic sea ice cover, landfast ice is an important part of coastal ecosystem, it provides functions as a climate regulator and platform for human activity. Recent changes in sea ice conditions in the Arctic have also affected landfast ice regime. At the same time, industrial interest in the Arctic shelf seas continue to increase. Knowledge on local landfast ice conditions are required to ensure safety of on ice operations and accurate forecasting.  In order to obtain a comprehensive information on landfast ice state we use a time series of wide swath SAR imagery.  An automatic sea ice tracking algorithm was applied to the sequential SAR images during the development stage of landfast ice cover. The analysis of resultant time series of sea ice drift allows to classify homogeneous sea ice drift fields and timing of their attachment to the landfast ice. In addition, the drift data allows to locate areas of formation of grounded sea ice accumulation called stamukha. This information сan be useful for local landfast ice stability assessment. The study is supported by the Russian Foundation for Basic Research (RFBR) grant 19-35-60033.</p>

scholarly journals An Assessment of the Filling Process of the Grand Ethiopian Renaissance Dam and Its Impact on the Downstream Countries

2021 ◽  
Vol 13 (4) ◽  
pp. 711
Author(s):  
Prakrut Kansara ◽  
Wenzhao Li ◽  
Hesham El-Askary ◽  
Venkataraman Lakshmi ◽  
Thomas Piechota ◽  
...  
Keyword(s):  
Time Series ◽  
Google Earth ◽  
Monthly Precipitation ◽  
Nile Basin ◽  
Blue Nile ◽  
Blue Nile Basin ◽  
Lake Nasser ◽  
Computationally Intensive ◽  
Sar Imagery ◽  
Average Monthly Precipitation

The Grand Ethiopian Renaissance Dam (GERD), formerly known as the Millennium Dam, has been filling at a fast rate. This project has created issues for the Nile Basin countries of Egypt, Sudan, and Ethiopia. The filling of GERD has an impact on the Nile Basin hydrology and specifically the water storages (lakes/reservoirs) and flow downstream. In this study, through the analysis of multi-source satellite imagery, we study the filling of the GERD reservoir. The time-series generated using Sentinel-1 SAR imagery displays the number of classified water pixels in the dam from early June 2017 to September 2020, indicating a contrasting trend in August and September 2020 for the upstream/downstream water bodies: upstream of the dam rises steeply, while downstream decreases. Our time-series analysis also shows the average monthly precipitation (derived using IMERG) in the Blue Nile Basin in Ethiopia has received an abnormally high amount of rainfall as well as a high amount of runoff (analyzed using GLDAS output). Simultaneously, the study also demonstrates the drying trend downstream at Lake Nasser in Southern Egypt before December 2020. From our results, we estimate that the volume of water at GERD has already increased by 3.584 billion cubic meters, which accounts for about 5.3% of its planned capacity (67.37 billion cubic meters) from 9 July–30 November 2020. Finally, we observed an increasing trend in GRACE anomalies for GERD, whereas, for the Lake Nasser, we observed a decreasing trend. In addition, our study discusses potential interactions between GERD and the rainfall and resulting flood in Sudan. Our study suggests that attention should be drawn to the connection between the GERD filling and potential drought in the downstream countries during the upcoming dry spells in the Blue Nile River Basin. This study provides an open-source technique using Google Earth Engine (GEE) to monitor the changes in water level during the filling of the GERD reservoir. GEE proves to be a powerful as well as an efficient way of analyzing computationally intensive SAR images.

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