scholarly journals Detection of Crop Seeding and Harvest through Analysis of Time-Series Sentinel-1 Interferometric SAR Data

2020 ◽  
Vol 12 (10) ◽  
pp. 1551 ◽  
Author(s):  
Jiali Shang ◽  
Jiangui Liu ◽  
Valentin Poncos ◽  
Xiaoyuan Geng ◽  
Budong Qian ◽  
...  
Keyword(s):  
Time Series ◽  
Dielectric Properties ◽  
Optical Sensors ◽  
Management Practices ◽  
Weather Conditions ◽  
Growth Patterns ◽  
Detection Techniques ◽  
Harvest Dates ◽  
Sar Data ◽  
Interferometric Sar

Synthetic aperture radar (SAR) is more sensitive to the dielectric properties and structure of the targets and less affected by weather conditions than optical sensors, making it more capable of detecting changes induced by management practices in agricultural fields. In this study, the capability of C-band SAR data for detecting crop seeding and harvest events was explored. The study was conducted for the 2019 growing season in Temiskaming Shores, an agricultural area in Northern Ontario, Canada. Time-series SAR data acquired by Sentinel-1 constellation with the interferometric wide (IW) mode with dual polarizations in VV (vertical transmit and vertical receive) and VH (vertical transmit and horizontal receive) were obtained. interferometric SAR (InSAR) processing was conducted to derive coherence between each pair of SAR images acquired consecutively in time throughout the year. Crop seeding and harvest dates were determined by analyzing the time-series InSAR coherence and SAR backscattering. Variation of SAR backscattering coefficients, particularly the VH polarization, revealed seasonal crop growth patterns. The change in InSAR coherence can be linked to change of surface structure induced by seeding or harvest operations. Using a set of physically based rules, a simple algorithm was developed to determine crop seeding and harvest dates, with an accuracy of 85% (n = 67) for seeding-date identification and 56% (n = 77) for harvest-date identification. The extra challenge in harvest detection could be attributed to the impacts of weather conditions, such as rain and its effects on soil moisture and crop dielectric properties during the harvest season. Other factors such as post-harvest residue removal and field ploughing could also complicate the identification of harvest event. Overall, given its mechanism to acquire images with InSAR capability at 12-day revisiting cycle with a single satellite for most part of the Earth, the Sentinel-1 constellation provides a great data source for detecting crop field management activities through coherent or incoherent change detection techniques. It is anticipated that this method could perform even better at a shorter six-day revisiting cycle with both satellites for Sentinel-1. With the successful launch (2019) of the Canadian RADARSAT Constellation Mission (RCM) with its tri-satellite system and four polarizations, we are likely to see improved system reliability and monitoring efficiency.

scholarly journals Paddy Rice Mapping in Thailand Using Time-Series Sentinel-1 Data and Deep Learning Model

2021 ◽  
Vol 13 (19) ◽  
pp. 3994
Author(s):  
Lu Xu ◽  
Hong Zhang ◽  
Chao Wang ◽  
Sisi Wei ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Sustainable Development ◽  
Large Scale ◽  
Management Practices ◽  
Conditional Random Field ◽  
Paddy Rice ◽  
Growth Patterns ◽  
Selection Strategy ◽  
Svm Classifier ◽  
Humid Climate

The elimination of hunger is the top concern for developing countries and is the key to maintain national stability and security. Paddy rice occupies an essential status in food supply, whose accurate monitoring is of great importance for human sustainable development. As one of the most important paddy rice production countries in the world, Thailand has a favorable hot and humid climate for paddy rice growing, but the growth patterns of paddy rice are too complicated to construct promising growth models for paddy rice discrimination. To solve this problem, this study proposes a large-scale paddy rice mapping scheme, which uses time-series Sentinel-1 data to generate a convincing annual paddy rice map of Thailand. The proposed method extracts temporal statistical features of the time-series SAR images to overcome the intra-class variability due to different management practices and modifies the U-Net model with the fully connected Conditional Random Field (CRF) to maintain the edge of the fields. In this study, 758 Sentinel-1 images that covered the whole country from the end of 2018 to 2019 were acquired to generate the annual paddy rice map. The accuracy, precision, and recall of the resultant paddy rice map reached 91%, 87%, and 95%, respectively. Compared to SVM classifier and the U-Net model based on feature selection strategy (FS-U-Net), the proposed scheme achieved the best overall performance, which demonstrated the capability of overcoming the complex cultivation conditions and accurately identifying the fragmented paddy rice fields in Thailand. This study provides a promising tool for large-scale paddy rice monitoring in tropical production regions and has great potential in the global sustainable development of food and environment management.

scholarly journals Irrigation Mapping Using Sentinel-1 Time Series at Field Scale

2018 ◽  
Vol 10 (9) ◽  
pp. 1495 ◽  
Author(s):  
Qi Gao ◽  
Mehrez Zribi ◽  
Maria Escorihuela ◽  
Nicolas Baghdadi ◽  
Pere Segui
Keyword(s):  
Time Series ◽  
Weather Conditions ◽  
Mean Value ◽  
Support Vector ◽  
Irrigated Crops ◽  
The Mean ◽  
Sar Data ◽  
Ground Truthing ◽  
Agricultural Site

The recently launched Sentinel-1 satellite with a Synthetic Aperture Radar (SAR) sensor onboard offers a powerful tool for irrigation monitoring under various weather conditions, with high spatial and temporal resolution. This research discusses the potential of different metrics calculated from the Sentinel-1 time series for mapping irrigated fields. A methodology for irrigation mapping using SAR data is proposed. The study is performed using VV (vertical–vertical) and VH (vertical–horizontal) polarizations over an agricultural site in Urgell, Catalunya (Spain). With field segmentation information from SIGPAC (the Geographic Information System for Agricultural Parcels), the backscatter intensities are averaged within each field. From the Sentinel-1 time series for each field, the statistics and metrics, including the mean value, the variance of the signal, the correlation length, and the fractal dimension, are analyzed. With the Support Vector Machine (SVM), the classification of irrigated crops, irrigated trees, and non-irrigated fields is performed with the metrics vector. The results derived from the SVM are validated with ground truthing from SIGPAC over the whole study area, with a good overall accuracy of 81.08%. Random Forest (RF) machine classification is also tested in this study, which gives an accuracy of around 82.2% when setting the tree depth at three. The methodology is based only on SAR data, which makes it applicable to all areas, even with frequent cloud cover, but this method may be less robust when irrigation is less dominated to soil moisture change.

scholarly journals Estimation of Equivalent Number of Looks in Time-Series Pol(In)SAR Data

2020 ◽  
Vol 12 (17) ◽  
pp. 2715
Author(s):  
Peng Shen ◽  
Changcheng Wang ◽  
Haiqiang Fu ◽  
Jianjun Zhu ◽  
Jun Hu
Keyword(s):  
Time Series ◽  
Speckle Noise ◽  
Statistical Characteristics ◽  
Data Types ◽  
Sar Images ◽  
Practical Applications ◽  
Equivalent Number ◽  
Essential Parameter ◽  
Sar Data ◽  
Interferometric Sar

As an essential parameter in synthetic aperture radar (SAR) images, the equivalent number of looks (ENL) not only indicates the speckle noise level in multi-look SAR data but also can be used for evaluating the region homogeneity level. Currently, time-series polarimetric (interferometric) SAR (TSPol(In)SAR) data are increasingly abundant, but traditional equivalent number of looks (ENL) estimators only use polarimetric information from a mono-temporal observation and do not consider the temporal characteristics or interferometric coherence of ground targets. Therefore, this paper puts forward four novel ENL estimators to overcome the restrictions of inadequate observation information. Firstly, based on the traditional trace moment estimator for polarimetric SAR data (TM-PolSAR), we extend it to both PolInSAR and TSPolInSAR data and then propose both TM-PolInSAR and TM-TSPolInSAR estimators, respectively. Secondly, for both TSPolSAR and single-reference TSPolInSAR data, we estimate the ENL by stacking the trace moments (STM) of multitemporal coherency matrices, called STM-TSPolSAR and STM-TSPolInSAR estimators, respectively. Therefore, these proposed ENL estimators can effectively deal with most of the requirements of TSPol(In)SAR data types in practical applications, mainly including statistical distribution modeling and region homogeneity evaluation. The simulation and real experiments detailedly compare the proposed four ENL estimators to the classical TM-PolSAR estimator and quantitatively analyze the estimation performance. The proposed estimators have obtained the ENL with less bias and standard deviation than the traditional estimator, especially in case of small spatial samples coherency matrices. Additionally, these STM-TSPolSAR, STM-TSPolInSAR, and TM-TSPolInSAR estimators have provided more effective statistical characteristics with the increase of the time-series size. It has been demonstrated that the proposed STM-TSPolSAR estimator considers the time-varying polarimetric characteristics of the crop and detects many edges that the traditional estimator cannot discover, which means a superior capability of region homogeneity evaluation.

scholarly journals Deep Learning-based Damage Mapping with InSAR Coherence Time Series

2021 ◽  
Author(s):  
Oliver Stephenson ◽  
Tobias Köhne ◽  
Eric Zhan ◽  
Brent Cahill ◽  
Sang-Ho Yun ◽  
...  
Keyword(s):  
Time Series ◽  
Detection Threshold ◽  
Time History ◽  
Weather Conditions ◽  
Coherence Time ◽  
Full Time ◽  
The Earth ◽  
The Difference ◽  
Damage Mapping ◽  
Interferometric Sar

<p>Satellite remote sensing is playing an increasing role in rapid mapping of damage after natural disasters. In particular, synthetic aperture radar (SAR) can image the Earth’s surface and map damage in all weather conditions, day and night. However, current SAR damage mapping methods struggle to separate damage from other changes in the Earth’s surface. In this study, we propose to map damage using the full time history of SAR observations of an impacted region from a single satellite constellation in order to detect anomalous variations in the Earth’s surface properties due to a natural disaster. We quantify Earth surface change using time series of sequential interferometric SAR coherence, then use a recurrent neural network (RNN) as a probabilistic anomaly detector on these coherence time series. The RNN is first trained on pre-event coherence time series, and then forecasts a probability distribution of the coherence between pre- and post-event SAR images. The difference between the forecast and observed co-event coherence provides a measure of the confidence in the identification of damage. The method allows the user to choose a damage detection threshold that is customized for each location, based on the local temporal behavior before the event. We apply this method to calculate estimates of damage for three earthquakes using multi-year time series of Sentinel-1 SAR acquisitions. Our approach shows good agreement with measured damage and quantitative improvement compared to using pre- to co-event coherence loss as a damage proxy.</p>

scholarly journals Earthworm Abundance Changes Depending on Soil Management Practices in Slovenian Vineyards

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1241
Author(s):  
Stanko Vršič ◽  
Marko Breznik ◽  
Borut Pulko ◽  
Jesús Rodrigo-Comino
Keyword(s):  
Management Practices ◽  
Human Impacts ◽  
Soil Layer ◽  
Soil Management ◽  
Weather Conditions ◽  
Soil Tillage ◽  
Soil Biodiversity ◽  
Straw Mulch ◽  
Green Cover ◽  
The Impact

Earthworms are key indicators of soil quality and health in vineyards, but research that considers different soil management systems, especially in Slovenian viticultural areas is scarce. In this investigation, the impact of different soil management practices such as permanent green cover, the use of herbicides in row and inter-row areas, use of straw mulch, and shallow soil tillage compared to meadow control for earthworm abundance, were assessed. The biomass and abundance of earthworms (m2) and distribution in various soil layers were quantified for three years. Monitoring and a survey covering 22 May 2014 to 5 October 2016 in seven different sampling dates, along with a soil profile at the depth from 0 to 60 cm, were carried out. Our results showed that the lowest mean abundance and biomass of earthworms in all sampling periods were registered along the herbicide strip (within the rows). The highest abundance was found in the straw mulch and permanent green cover treatments (higher than in the control). On the plots where the herbicide was applied to the complete inter-row area, the abundance of the earthworm community decreased from the beginning to the end of the monitoring period. In contrast, shallow tillage showed a similar trend of declining earthworm abundance, which could indicate a deterioration of soil biodiversity conditions. We concluded that different soil management practices greatly affect the soil’s environmental conditions (temperature and humidity), especially in the upper soil layer (up to 15 cm deep), which affects the abundance of the earthworm community. Our results demonstrated that these practices need to be adapted to the climate and weather conditions, and also to human impacts.

scholarly journals Meso- and microscale sea-ice motion in the East Siberian Sea as determined from ERS-1 SAR Data

1999 ◽  
Vol 45 (150) ◽  
pp. 370-383 ◽  
Author(s):  
Kim Morris ◽  
Shusun Li ◽  
Martin Jeffries
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Large Scale ◽  
Vector Fields ◽  
Lateral Displacement ◽  
Motion Vector ◽  
Sar Interferometry ◽  
East Siberian Sea ◽  
Pack Ice ◽  
Sar Data

Abstract Synthetic aperture radar- (SAR-)derived ice-motion vectors and SAR interferometry were used to study the sea-ice conditions in the region between the coast and 75° N (~ 560 km) in the East Siberian Sea in the vicinity of the Kolyma River. ERS-1 SAR data were acquired between 24 December 1993 and 30 March 1994 during the 3 day repeat Ice Phase of the satellite. The time series of the ice-motion vector fields revealed rapid (3 day) changes in the direction and displacement of the pack ice. Longer-term (≥ 1 month) trends also emerged which were related to changes in large-scale atmospheric circulation. On the basis of this time series, three sea-ice zones were identified: the near-shore, stationary-ice zone; a transitional-ice zone;and the pack-ice zone. Three 3 day interval and one 9 day interval interferometric sets (amplitude, correlation and phase diagrams) were generated for the end of December, the begining of February and mid-March. They revealed that the stationary-ice zone adjacent to the coast is in constant motion, primarily by lateral displacement, bending, tilting and rotation induced by atmospheric/oceanic forcing. The interferogram patterns change through time as the sea ice becomes thicker and a network of cracks becomes established in the ice cover. It was found that the major features in the interferograms were spatially correlated with sea-ice deformation features (cracks and ridges) and major discontinuities in ice thickness.

Monitoring of Agricultural Grasslands With Time Series of X-Band Repeat-Pass Interferometric SAR

2016 ◽  
Vol 9 (8) ◽  
pp. 3687-3697 ◽  
Author(s):  
Karlis Zalite ◽  
Oleg Antropov ◽  
Jaan Praks ◽  
Kaupo Voormansik ◽  
Mart Noorma
Keyword(s):  
Time Series ◽  
X Band ◽  
Interferometric Sar

Extraction of linear objects from interferometric SAR data

2002 ◽  
Vol 23 (3) ◽  
pp. 461-475 ◽  
Author(s):  
Olaf Hellwich ◽  
Ivan Laptev ◽  
Helmut Mayer
Keyword(s):  
Sar Data ◽  
Interferometric Sar
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