Monitoring Harvesting by Time Series of Sentinel-1 SAR Data

Kavats, Olena; Khramov, Dmitriy; Sergieieva, Kateryna; Vasyliev, Volodymyr V.

doi:10.3390/rs11212496

Cited by 30 publications

(29 citation statements)

References 20 publications

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“…In another research, grassland mowing events were studied by modelling NDVI on a parcel level with recurrent neural networks, using Sentinel-1 backscatter and coherence time series as an input [14]. Sentinel-1 coherence time series have also been demonstrated to be useful for crop harvesting detection [15,16] and land cover mapping [17].…”

Section: Introductionmentioning

confidence: 99%

Separability of Mowing and Ploughing Events on Short Temporal Baseline Sentinel-1 Coherence Time Series

et al. 2020

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Short temporal baseline regular Synthetic Aperture Radar (SAR) interferometry is a tool well suited for wide area monitoring of agricultural activities, urgently needed in European Union Common Agricultural Policy (CAP) enforcement. In this study, we demonstrate and describe in detail, how mowing and ploughing events can be identified from Sentinel-1 6-day interferometric coherence time series. The study is based on a large dataset of 386 dual polarimetric Sentinel-1 VV/VH SAR and 351 Sentinel-2 optical images, and nearly 2000 documented mowing and ploughing events on more than 1000 parcels (average 10.6 ha, smallest 0.6 ha, largest 108.5 ha). Statistical analysis revealed that mowing and ploughing cause coherence to increase when compared to values before an event. In the case of mowing, the coherence increased from 0.18 to 0.35, while Sentinel-2 NDVI (indicating the amount of green chlorophyll containing biomass) at the same time decreased from 0.75 to 0.5. For mowing, there was virtually no difference between the polarisations. After ploughing, VV-coherence grew up to 0.65 and VH-coherence to 0.45, while NDVI was around 0.2 at the same time. Before ploughing, both coherence and NDVI values were very variable, determined by the agricultural management practices of the parcel. Results presented here can be used for planning further studies and developing mowing and ploughing detection algorithms based on Sentinel-1 data. Besides CAP enforcement, the results are also useful for food security and land use change detection applications.

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Section: Introductionmentioning

confidence: 99%

Separability of Mowing and Ploughing Events on Short Temporal Baseline Sentinel-1 Coherence Time Series

et al. 2020

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“…The primary dataset of the Agricultural SandboxNL is the Sentinel-1 radar backscatter because the value of backscatter has been widely demonstrated in mapping and monitoring activities. Several recent studies have highlighted the potential value of interferometric coherence for crop type mapping 49 , crop emergence, harvest detection 45,50 and grassland monitoring 51 . However, GEE does not host the Sentinel-1 SLC products required to produce interferometric coherence 35 .…”

Section: Sentinel-1 Interferometric Coherencementioning

confidence: 99%

Agricultural Sandbox NL: A national-scale database of parcel-level, processed Sentinel-1 SAR data

Kumar¹,

Huber²,

Rommen³

et al. 2021

Preprint

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Synthetic Aperture Radar (SAR) data handling, processing, and interpretation are barriers preventing a rapid uptake of SAR data by application specialists and non-expert domain users in the field of agricultural monitoring. To improve the accessibility of Sentinel-1 data, we have generated a reduced-volume, multi-year Sentinel-1 SAR database. It includes mean and standard deviation of VV, VH and VH/VV backscatter, pixel counts, geometry, crop type, local incidence angle and azimuth angle at parcel-level. The database uses around 3100 Sentinel-1 images (5 TB) to produce a 12 GB time series database for approximately 770,000 crop parcels over the Netherlands for a period of three years. The database can be queried by Sentinel-1 system parameters (e.g. relative orbit) or user application-specific parameters (e.g. crop type, spatial extent, time period) for parcel level assessment. The database can be used to accelerate the development of new tools, applications and methodologies for agricultural and water related applications, such as parcel-level crop bio-geophysical parameter estimation, inter-annual variability analysis, drought monitoring, grassland monitoring and agricultural management decision-support.

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“…The coherence is derived from two complex SAR images and composed of information gathered from both phase and amplitude. Low coherence values indicate the decorrelation between the images which might be due to the changes of the scatterers (Kavats et al, 2019). Tamm et al (2016) showed the feasibility of 12 days Sentinel-1 pairs for grassland mowing.…”

Section: Introductionmentioning

confidence: 96%

“…In most cases, backscatter values are the main inputs for image classification. Some of the current studies also used the advantage of short-time repeat-pass of Sentinel-1 to determine time series of coherence for crop monitoring (Kavats et al 2019, Khabbazan et al 2019. The coherence is derived from two complex SAR images and composed of information gathered from both phase and amplitude.…”

Section: Introductionmentioning

confidence: 99%

“…Tamm et al (2016) showed the feasibility of 12 days Sentinel-1 pairs for grassland mowing. Kavats et al (2019) studied detection of harvesting date using backscatter and coherence of 12 days Sentinel-1 dataset. Khabbazan et al (2019) investigated the relationship between ground measurements and time-series of backscatter and coherence obtained from Sentinel-1.…”

Section: Introductionmentioning

confidence: 99%

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COHERENCE and BACKSCATTER BASED CROPLAND MAPPING USING MULTI-TEMPORAL SENTINEL-1 with DYNAMIC TIME WARPING

Narin

Abdikan

Bayık

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Cropland mapping is an important inventory for food security and decision making operated by governments. Crop mapping is used to identify the croplands and their spatial distribution. For a reliable analysis and forecast for projection, multi-temporal data play a key role. Even current open and frequent optical satellite data such as Sentinel-2 and Landsat support monitoring, they are not always operational due to atmospheric conditions (rain, cloud cover, haze, etc.). On the other hand, Synthetic Aperture Radar (SAR) satellites provide alternative data sets compared to optical satellites since they can acquire images under all weather conditions. In this study, an annual cropland monitoring study is conducted using Sentinel-1 SAR. For the investigation, Tokat Province an agricultural region of Turkey, where the main source of income is agriculture, was selected. There are 4 different vegetation species (wheat, sunflower, sugar beet, corn) in the study area. Sentinel-1 data was used to generate time-series of each class and phenological structures of the crops. In this context, backscatter images of both vertical-vertical (VV) and vertical-horizontal (VH) polarized data, and coherence of both VV and VH were produced from Sentinel-1 data. Time-Weighted Dynamic Time-Warping (TWDTW) classification approach was used over cropland. The produced time-series are classified under different scenarios. The results showed that only coherence has provided higher accuracies about 81% compared to using only backscatter images as 49%.

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Monitoring Harvesting by Time Series of Sentinel-1 SAR Data

Cited by 30 publications

References 20 publications

Separability of Mowing and Ploughing Events on Short Temporal Baseline Sentinel-1 Coherence Time Series

Separability of Mowing and Ploughing Events on Short Temporal Baseline Sentinel-1 Coherence Time Series

Agricultural Sandbox NL: A national-scale database of parcel-level, processed Sentinel-1 SAR data

COHERENCE and BACKSCATTER BASED CROPLAND MAPPING USING MULTI-TEMPORAL SENTINEL-1 with DYNAMIC TIME WARPING

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