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Remote Sens. 2018, 10(8), 1300; https://doi.org/10.3390/rs10081300

Targeted Grassland Monitoring at Parcel Level Using Sentinels, Street-Level Images and Field Observations

Food Security Unit, Joint Research Centre (JRC), European Commission, 21027 Ispra, Italy
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Received: 6 July 2018 / Revised: 31 July 2018 / Accepted: 2 August 2018 / Published: 17 August 2018
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Abstract

The introduction of high-resolution Sentinels combined with the use of high-quality digital agricultural parcel registration systems is driving the move towards at-parcel agricultural monitoring. The European Union’s Common Agricultural Policy (CAP) has introduced the concept of CAP monitoring to help simplify the management and control of farmers’ parcel declarations for area support measures. This study proposes a proof of concept of this monitoring approach introducing and applying the concept of ‘markers’. Using Sentinel-1- and -2-derived (S1 and S2) markers, we evaluate parcels declared as grassland in the Gelderse Vallei in the Netherlands covering more than 15,000 parcels. The satellite markers—respectively based on crop-type deep learning classification using S1 backscattering and coherence data and on detecting bare soil with S2 during the growing season—aim to identify grassland-declared parcels for which (1) the marker suggests another crop type or (2) which appear to have been ploughed during the year. Subsequently, a field-survey was carried out in October 2017 to target the parcels identified and to build a relevant ground-truth sample of the area. For the latter purpose, we used a high-definition camera mounted on the roof of a car to continuously sample geo-tagged digital imagery, as well as an app-based approach to identify the targeted fields. Depending on which satellite-based marker or combination of markers is used, the number of parcels identified ranged from 2.57% (marked by both the S1 and S2 markers) to 17.12% of the total of 11,773 parcels declared as grassland. After confirming with the ground-truth, parcels flagged by the combined S1 and S2 marker were robustly detected as non-grassland parcels (F-score = 0.9). In addition, the study demonstrated that street-level imagery collection could improve collection efficiency by a factor seven compared to field visits (1411 parcels/day vs. 217 parcels/day) while keeping an overall accuracy of about 90% compared to the ground-truth. This proposed way of collecting in situ data is suitable for the training and validating of high resolution remote sensing approaches for agricultural monitoring. Timely country-wide wall-to-wall parcel-level monitoring and targeted in-season parcel surveying will increase the efficiency and effectiveness of monitoring and implementing agricultural policies. View Full-Text
Keywords: meadow; grassland; Sentinel-1; Sentinel-2; crowdsourcing; deep learning; geo-tagged street-level pictures; CAP; agriculture; TensorFlow meadow; grassland; Sentinel-1; Sentinel-2; crowdsourcing; deep learning; geo-tagged street-level pictures; CAP; agriculture; TensorFlow
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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d’Andrimont, R.; Lemoine, G.; van der Velde, M. Targeted Grassland Monitoring at Parcel Level Using Sentinels, Street-Level Images and Field Observations. Remote Sens. 2018, 10, 1300.

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