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

Abstract

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.

Grassland Mowing Detection Using Sentinel-1 Time Series: Potential and Limitations

“…On aggregated coherence time series of about 1000 grassland parcels, the authors of [29] observed the typical signature profiles of mowing and ploughing events, showing lower coherence values during the growing phase and high values after the events, as expected in theory. Although the authors of [29,31] identify several confounding factors, such as farming activities or precipitation, they conclude that mowing detection using C-band SAR interferometric coherence is feasible and encourage further research to evaluate mowing detection accuracy of different methods and identify other confounding factors.…”

“…InSAR coherence is a measure of the signal decorrelation between pairs of subsequent SAR acquisitions. Coherence is higher on bare soil and short vegetation than on high grass, because the canopy structure changes over time as grass moves with the wind or gradually grows, causing temporal decorrelation [27][28][29], while bare soil or short vegetation remain mostly unchanged. After a mowing event, the coherence magnitude of a grassland should rise, as there is less movement in short cut grass.…”

Exploiting time series of Sentinel-1 and Sentinel-2 to detect grassland mowing events using deep learning with reject region

“…We used the Sentinel Application Platform (SNAP) toolbox for processing S-1 data. More specifically, we followed the same following pre-processing steps in 16 : apply orbit file, back-geocoding (using Shuttle Radar Topography Mission (SRTM) data), coherence calculation, deburst, terrain correction, and reprojection to the local projection (EPSG:3301). Lastly, we resampled the data to 4m resolution to preserve the maximum spatial resolution and square-shaped pixels.…”

“…However, the availability of optical data during the growing season is limited due to cloud cover that may result in shorter or longer gaps in time series of images which motivates studying other remote sensing techniques for monitoring grasslands 15 . Laser scanning produces reliable vegetation height estimates 16 however, it is too expensive for frequent monitoring of large areas. Spaceborne synthetic aperture radar (SAR) data is considered an effective solution for monitoring agriculture activities 17 , as the SAR signal can penetrate clouds in various weather conditions while providing continuous data covering a large area.…”

Quantitatively Estimating of InSAR Decorrelation Based on Landsat-Derived NDVI

“…Some studies had suggested that there was a certain non-linear relationship between coherence and some vegetation indices, but the reliability of this relationship needs to be verified [20]. Most of these pieces of research focus on the qualitative analysis [21,22], while the quantitative relationship between coherence and vegetation coverage is still unclear.…”