Planet HHR Time Stacks tests in the 2019 crop season - a synthesis

The Common Agricultural Policy (CAP) Checks by Monitoring (CbM) replaces the on-the-spot-checks presently used to verify that area-based direct aid is granted correctly to EU farmers. This alternative control method is implemented through Article 40a of the implementing regulation (EU) 809/2014, and may be used since 2018. The CbM primarily relies on automatic methods to conclude on the CAP eligibility criteria, commitments and obligations from regular and systematic Copernicus Sentinel imagery. For some agricultural parcels, the spatial resolution of the Sentinel imagery could be insufficient to conclude on the support (eligibility, compliance). For this reason, the use of High High Resolution (HHR) image data has been considered to verify and possibly complement the results obtained using Sentinel data. This document summarizes the experiences of four MS Regions use of HHR data for their CbM in the crop season 2019, and compares them with the assessments made by the Joint Research Centre (JRC) either in collaboration or in parallel. A consortium formed by Planet Labs Germany GmbH and GAF AG was awarded a contract to deliver the required HHR data consisting of archive Time Stacks (TSs) and raster representations (image chips) of selected parcels. The four MS Regions covered by HHR TSs were Denmark (delivery of 800 TS), Malta (250), Italy (7,997), and finally Spain (7,190) giving a total of 16,237 TSs. As outcomes of the tests, there is a common agreement that S2 and Planet Normalized Difference Vegetation Index (NDVI) profiles are very comparable even for small parcels. However, the four MS Regions consider that the TSs, and especially the accompanying image chips, are useful in the CbM processing chain. The experiences reveal that certain parameters need to be further optimized to make the use more effective (i.e. producing the right result). First of all, it is essential to correctly determine the set of (small, narrow, etc.) parcels that should compose the HHR TS set i.e. to use the correct FOIs for input in the automatic CbM processing. A correctly extracted FOI may already solve many inconclusive parcels composing it. Then, other parameters to fine-tune are the optimization of ingest time, to ease the access to data, to increase the availability of image chips, and finally it is also suggested that MS Administrations should have a clear vision of where in their workflow the TS and image chips are needed. In this context, it is also appropriate to mention the possible future need of HHR reference within the frame of the CbM process where the spatial, and heterogeneity components of the parcel (or correct FOI) may allow to extract more relevant information than the sole use of the temporal component. The document ends with some ideas on the way forwards.JRC.D.5-Food Securit

Discussion document on the introduction of monitoring to substitute OTSC - Supporting non-paper DS/CDP/2017/03 revising R2017/809

This discussion document builds upon the non-paper DS/CDP/2017/03 to introduce the possibility for substituting the OTSC by a system of monitoring for checking the fulfilment of land use/ land cover related CAP requirements. It describes the main concepts and components that need to be considered and developed for substituting the sampled on the spot checks of aid applications with a monitoring system on all of the applications. The goal is simplification and reduction of the burden of controls and especially for what concerns number of field visits. Such substitution requires a shift in thinking, procedures as well as technology and these are topics elaborated in some detail. An annex provides illustrations, examples, field cases and elaborations of the key topics. This document constitutes the Commission’s interpretation of common standards.JRC.D.5-Food Securit

Applicability limits of Sentinel-2 data compared to higher resolution imagery for CAP checks by monitoring

The Common Agricultural Policy (CAP) ‘checks by monitoring’, replacing the on-the-spot-checks presently used to verify that the area-based direct aid is granted correctly to EU farmers, can be introduced already as of crop campaign 2019. In fact, according to the recently adopted Article 40a of the implementing regulation (EU) 746/2018 of 18 May 2018 amending the Implementing Regulation (EU) No. 809/2014, several MS Regions are, opting to introduce an agricultural aid check system based on monitoring. Such checks rely on automatic methods to observe, track and assess the CAP eligibility criteria, commitments and obligations. Regular and systematic observations are carried out using the Copernicus Sentinel imagery or equivalent, making use of automatic machine learning techniques coupled with an efficient handling of farmer aid applications. In the case where the spatial resolution of above mentioned imagery is not sufficient to conclude on the support (eligibility, holding compliance), the competent authority must undertake appropriate ‘follow up activity’. This can be in form of efficient interaction with the beneficiaries, or for example by making use of ‘time stacks’ of information derived from a higher resolution image source (i.e. High High Resolution- HHR- satellite imagery with a ground sampling distance approximately two or more times better than the Sentinel-2). Before introducing such HHR approach, it is supposed that the MS has run through the so-called ‘sifting” preparatory operation. At the end of such iterative process, the set of “small” parcels for which alternative check methods should be made will be known. The question is to understand when the HHR use is effective (i.e. adequate to accomplish its purpose), and therefore really gives an enhanced information, superior to that extracted from the coarser resolution imagery.JRC.D.5-Food Securit

A simple similarity index for the comparison of remotely sensed time series with scarce simultaneous acquisitions

Emergence of new state-of-the-art technologies has enabled an unprecedented amount of high spatial resolution satellite data having great potential for exploitation of extracted time series for a vast range of applications. Despite the high temporal resolution of time series, the number of real observations of optical data that can be utilized is reduced due to meteorological conditions (such as cloud or haze) prevailing at the time of acquisition. This fact has an effect on the density of the retrieved time series and subsequently on a number of coincidental observations when comparing the similarity of time series from two different data sources for which the simultaneous acquisition date is already scarce. Classical tools for assessing the similarity of such time series can prove to be difficult or even impossible because of a lack of simultaneous observations. In this paper, we propose a simple method in order to circumvent this scarcity issue. In the first step, we rely on an interpolation in order to produce artificial time series on the union of the original acquisition dates. Then, we extend the theory of the correlation coefficient (CC) estimator to these interpolated time series. After validation on synthetic data, this simple approach proved to be extremely efficient on a real case study where Sentinel-2 and PlanetScope NDVI time series on parcels in The Netherlands are compared. Indeed, compared to other methods, it reduced the number of undecided cases while also improving the power of the statistical test on the similarity between both types of time series and the precision of the estimated CC.JRC.D.5-Food Securit

A Simple Similarity Index for the Comparison of Remotely Sensed Time Series with Scarce Simultaneous Acquisitions

Emergence of new state-of-the-art technologies has enabled an unprecedented amount of high spatial resolution satellite data having great potential for exploitation of extracted time series for a vast range of applications. Despite the high temporal resolution of time series, the number of real observations of optical data that can be utilized is reduced due to meteorological conditions (such as cloud or haze) prevailing at the time of acquisition. This fact has an effect on the density of the retrieved time series and subsequently on a number of coincidental observations when comparing the similarity of time series from two different data sources for which the simultaneous acquisition date is already scarce. Classical tools for assessing the similarity of such time series can prove to be difficult or even impossible because of a lack of simultaneous observations. In this paper, we propose a simple method in order to circumvent this scarcity issue. In the first step, we rely on an interpolation in order to produce artificial time series on the union of the original acquisition dates. Then, we extend the theory of the correlation coefficient (CC) estimator to these interpolated time series. After validation on synthetic data, this simple approach proved to be extremely efficient on a real case study where Sentinel-2 and PlanetScope NDVI time series on parcels in The Netherlands are compared. Indeed, compared to other methods, it reduced the number of undecided cases while also improving the power of the statistical test on the similarity between both types of time series and the precision of the estimated CC

Assessing Spatial Limits of Sentinel-2 Data on Arable Crops in the Context of Checks by Monitoring

The availability of large amounts of Sentinel-2 data has been a trigger for its increasing exploitation in various types of applications. It is, therefore, of importance to understand the limits above which these data still guarantee a meaningful outcome. This paper proposes a new method to quantify and specify restrictions of the Sentinel-2 imagery in the context of checks by monitoring, a newly introduced control approach within the European Common Agriculture Policy framework. The method consists of a comparison of normalized difference vegetation index (NDVI) time series constructed from data of different spatial resolution to estimate the performance and limits of the coarser one. Using similarity assessment of Sentinel-2 (10 m pixel size) and PlanetScope (3 m pixel size) NDVI time series, it was estimated that for 10% out of 867 fields less than 0.5 ha in size, Sentinel-2 data did not provide reliable evidence of the activity or state of the agriculture field over a given timeframe. Statistical analysis revealed that the number of clean or full pixels and the proportion of pixels lost after an application of a 5-m (1/2 pixel) negative buffer are the geospatial parameters of the field that have the highest influence on the ability of the Sentinel-2 data to qualify the field’s state in time. We specified the following limiting criteria: at least 8 full pixels inside a border and less than 60% of pixels lost. It was concluded that compliance with the criteria still assures a high level of extracted information reliability. Our research proved the promising potential, which was higher than anticipated, of Sentinel-2 data for the continuous state assessment of small fields. The method could be applied to other sensors and indicators

Assessing spatial limits of Sentinel-2 data on arable crops in the context of checks by monitoring

The availability of large amounts of Sentinel-2 data has been a trigger for its increasing exploitation in various types of applications. It is, therefore, of importance to understand the limits above which these data still guarantee a meaningful outcome. This paper proposes a new method to quantify and specify restrictions of the Sentinel-2 imagery in the context of checks by monitoring, a newly introduced control approach within the European Common Agriculture Policy framework. The method consists of a comparison of normalized difference vegetation index (NDVI) time series constructed from data of different spatial resolution to estimate the performance and limits of the coarser one. Using similarity assessment of Sentinel-2 (10 m pixel size) and PlanetScope (3 m pixel size) NDVI time series, it was estimated that for 10% out of 867 fields less than 0.5 ha in size, Sentinel-2 data did not provide reliable evidence of the activity or state of the agriculture field over a given timeframe. Statistical analysis revealed that the number of clean or full pixels and the proportion of pixels lost after an application of a 5-m (1/2 pixel) negative buffer are the geospatial parameters of the field that have the highest influence on the ability of the Sentinel-2 data to qualify the field’s state in time. We specified the following limiting criteria: at least 8 full pixels inside a border and less than 60% of pixels lost. It was concluded that compliance with the criteria still assures a high level of extracted information reliability. Our research proved the promising potential, which was higher than anticipated, of Sentinel-2 data for the continuous state assessment of small fields. The method could be applied to other sensors and indicators.JRC.D.5-Food Securit

Geomatics in support of the Common Agricultural Policy - Proceedings of the 16th GeoCAP Annual Conference, 2010

The 2010 Annual Conference was the 16th edition. The conference entitled „Geomatics in support of the CAP. was held in Bergamo and organised by the GeoCAP action of the Joint Research Centre (Ispra, Italy) alone. The conference covered the 2010 Control with Remote sensing campaign activities and ortho-imagery use in all the CAP management and control procedures. There has been a specific focus on the Land Parcel Identification Systems quality assessment process. The conference was structured over three days – 24th to 26th November. The first day was mainly dedicated to future Common Agriculture Policy perspectives and futures challenges in Agriculture as well as overview of 2010 CwRS campaign. The second was shared in technical parallel sessions addressing the following topics: i) LPIS Quality Assurance and geo-databases features; ii) New sensors, new software, and their use within the CAP, and iii) Good Agriculture and Environmental Conditions (GAEC): control methods and implementing measures. The last day was dedicated to the GPS validation process and to the conclusions of the conference. The presentations were made available on line, and this publication represents the best presentations judged worthy of inclusion in a conference proceedings aimed at recording the state of the art of technology and practice of that time.JRC.DDG.H.4-Monitoring agricultural resource