New sensors benchmark report on Sentinel-2A

Geometric benchmarking for Sentinel-A2 sensor over Maussane test site for CAP purposesJRC.H.6-Digital Earth and Reference Dat

New sensors benchmark report on SPOT7

The main objective of the present study is to assess whether SPOT7 sensor can be qualified for Control with Remote Sensing program (CwRS), in Common Agriculture Policy (CAP). The benchmarking presented herein aims at evaluating the usability of SPOT7 for the CAP checks through an estimation of its geometric (positional) accuracy, as well as measuring the influence of different factors (viewing angle, number of GCPs, software implementation) on this accuracy. For that purpose, the External Quality Control of SPOT7 orthoimagery conforms to the standard method developed by JRC and follows a procedure already adopted in the validation of previous high (HR) and very-high resolution (VHR) products.JRC.H.6-Digital Earth and Reference Dat

New sensors benchmark report on WorldView-4 - Geometric benchmarking over Maussane test site for CAP purposes

Imagery collected by recently launched WorldView-4 satellite can be potentially used in The Common Agricultural Policy (CAP) image acquisition Campaign. The qualification and certificate is conducted by performing benchmarking tests, i.e. it has to be checked whether planimetric accuracy of produced orthoimagery does not exceed certain values regulated by JRC. Therefore, benchmarking tests were carried out on three WorldView-4 imagery collected in March and April 2017. This report describes in details how the tests were performed i.e. auxiliary data used, methodology and workflow as well as outcome from the Internal Quality Control. However, to make the tests objective, the orthoimagery have been handed to JRC for External Quality Control which is a base for certification of the sensorJRC.D.5-Food Securit

New sensors benchmark report on Kompsat-3

The following document has been drawn up as a follow up to the Quality Control Record L [i] on the commissioning phase of the Kompsat-3 imagery, planned benchmarking tests as well as the methodology used in the tests. Benchmarking is necessary to be performed in order to estimate the usability of the imagery collected by particular sensor in The Common Agricultural Policy (CAP) image acquisition Campaign. The main requirement that should be fulfilled concerns the planimetric accuracy of the orthoimagery which should not exceed particular thresholds given in VHR Specifications [iii]. The methodologies used in the benchmarking tests were performed based on Guidelines for Best Practice and Quality Checking of Ortho Imagery [ii]. However, in addition the tests were performed according to alternative methodology, described in [i], which differs from the standard one, the GCPs selection/measurement phase i.e. image to image correlation techniques are used.JRC.H.6-Digital Earth and Reference Dat

New sensors benchmark report on PlanetScope: Geometric benchmarking test for Common Agricultural Policy (CAP) purposes

The main objective of the report is to assess whether images produced by PlanetScope sensor are suitable for usage in CAP CwRS programme, specifically in the Common Agricultural Policy (CAP). The benchmarking presented herein aims at evaluating the usability of PlanetScope images for the CAP checks through an estimation of its geometric (positional) accuracy. The tests have been performed on PlanetScope Ortho Tile product data. For that purpose, the External Quality Control of Planetscope orthoimagery conforms to the standard method developed by JRC and follows a procedure already adopted in the validation of previous high and very-high resolution products.JRC.D.5-Food Securit

New sensors benchmark report on WorldView-3

Imagery collected by recently launched WorldView-3 satellite can be potentially used in The Common Agricultural Policy (CAP) image acquisition Campaign. The qualification and certificate is conducted by performing benchmarking tests namely, it has to be checked whether planimetric accuracy of produced orthoimagery does not exceed certain values regulated by JRC. Therefore, benchmarking tests were carried out on two WorldView-3 imagery acquired in October and November 2014. This report describes in detail how the tests were performed i.e. auxiliary data used, methodology and workflow as well as outcome from the Internal Quality Control. However, to make the tests objective, the orthoimagery was handed to JRC for External Quality Control which is a base for certification of the sensor. Such external QC has been performed by the JRC and included in Chapter 7.JRC.H.6-Digital Earth and Reference Dat

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

New sensors benchmark report on KOMPSAT-3A: Geometric benchmarking over Maussane test site for CAP purposes

Imagery collected by KOMPSAT-3A satellite can be potentially used in The Common Agricultural Policy (CAP) image acquisition Campaign. The qualification and certificate is conducted by performing benchmarking tests, i.e. it has to be checked whether the planimetric accuracy of the produced orthoimages does not exceed certain values regulated by JRC. Therefore, a benchmarking test was carried out based on two KOMPSAT-3A imagery collected in February 2017 and March 2018. The first part of the report (chapter 2-6) describes in detail how the tests were performed, i.e. the auxiliary data, methodology and workflow used as well as outcome from the Internal Quality Control (IQC). The second part (chapter 7-9) deals with External Quality Control (EQC) and conclusions made on the basis of this assessment with regards to the VHR Image technical specifications criteria [iii]. While the first part, i.e. production of ortho imagery, was carried out by external an contractor (European Space Imaging - EUSI) the second part, i.e EQC, was performed by Joint Research Centre (JRC). In this way an independent and objective test was assured.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