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

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.

[1]  Ramon F. Hanssen,et al.  Temporal Decorrelation in L-, C-, and X-band Satellite Radar Interferometry for Pasture on Drained Peat Soils , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Brian Brisco,et al.  Improved Spatial Mapping of Rainfall Events with Spaceborne SAR Imagery , 1983, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Guido Lemoine,et al.  Targeted Grassland Monitoring at Parcel Level Using Sentinels, Street-Level Images and Field Observations , 2018, Remote. Sens..

[4]  Malcolm Davidson,et al.  GMES Sentinel-1 mission , 2012 .

[5]  Stefano Tebaldini,et al.  Vegetated Target Decorrelation in SAR and Interferometry: Models, Simulation, and Performance Evaluation , 2020, Remote. Sens..

[6]  Urs Wegmüller,et al.  Signatures of ERS–Envisat Interferometric SAR Coherence and Phase of Short Vegetation: An Analysis in the Case of Maize Fields , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Irena Hajnsek,et al.  Towards a detection of grassland cutting practices with dual polarimetric TerraSAR-X data , 2013 .

[8]  Christian Ginzler,et al.  Regional Scale Mapping of Grassland Mowing Frequency with Sentinel-2 Time Series , 2018, Remote. Sens..

[9]  Jaan Praks,et al.  Long Term Interferometric Temporal Coherence and DInSAR Phase in Northern Peatlands , 2020, Remote. Sens..

[10]  Jaan Praks,et al.  Monitoring of Agricultural Grasslands With Time Series of X-Band Repeat-Pass Interferometric SAR , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[11]  Urs Wegmüller,et al.  Retrieval of vegetation parameters with SAR interferometry , 1997, IEEE Trans. Geosci. Remote. Sens..

[12]  Liina Talgre,et al.  Relating Sentinel-1 Interferometric Coherence to Mowing Events on Grasslands , 2016, Remote. Sens..

[13]  Marco Lavalle,et al.  A Temporal Decorrelation Model for Polarimetric Radar Interferometers , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Jiali Shang,et al.  Detection of Crop Seeding and Harvest through Analysis of Time-Series Sentinel-1 Interferometric SAR Data , 2020, Remote. Sens..

[15]  Fabio Rocca,et al.  Modeling Interferogram Stacks , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Lars M. H. Ulander,et al.  Repeat-pass SAR interferometry over forested terrain , 1995 .

[17]  Paris W. Vachon,et al.  Coherence estimation for SAR imagery , 1999, IEEE Trans. Geosci. Remote. Sens..

[18]  Jong-Sen Lee,et al.  Intensity and phase statistics of multilook polarimetric and interferometric SAR imagery , 1994, IEEE Trans. Geosci. Remote. Sens..

[19]  Irena Hajnsek,et al.  Assessment of soil moisture effects on L-band radar interferometry , 2015 .

[20]  R. Bamler,et al.  Phase statistics of interferograms with applications to synthetic aperture radar. , 1994, Applied optics.

[21]  N. Hamano,et al.  Digital processing of synthetic aperture radar data , 1984 .

[22]  Olena Kavats,et al.  Monitoring Harvesting by Time Series of Sentinel-1 SAR Data , 2019, Remote. Sens..

[23]  R. Treuhaft,et al.  Vertical structure of vegetated land surfaces from interferometric and polarimetric radar , 2000 .

[24]  Howard A. Zebker,et al.  Decorrelation in interferometric radar echoes , 1992, IEEE Trans. Geosci. Remote. Sens..

[25]  A. Hopkins,et al.  Grassland for agriculture and nature conservation: production, quality and multi-functionality. , 2006 .

[26]  Irena Hajnsek,et al.  Observations of Cutting Practices in Agricultural Grasslands Using Polarimetric SAR , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[27]  Jaan Praks,et al.  Sentinel-1 InSAR Coherence for Land Cover Mapping: A Comparison of Multiple Feature-Based Classifiers , 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[28]  Maxim Neumann,et al.  Application of Repeat-Pass TerraSAR-X Staring Spotlight Interferometric Coherence to Monitor Pasture Biophysical Parameters: Limitations and Sensitivity Analysis , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[29]  Alireza Taravat,et al.  Automatic Grassland Cutting Status Detection in the Context of Spatiotemporal Sentinel-1 Imagery Analysis and Artificial Neural Networks , 2019, Remote. Sens..