INTRODUCTION

Sentinel 4 is an imaging UVN (UV-VIS-NIR) dispersive spectrometer, developed by Airbus DS under an ESA contract in the frame of the joint EU/ESA COPERNICUS program. The instrument is introduced in a dedicated presentation in this conference.

Sentinel 4 relies on two Charge-Coupled Devices (CCDs) to collect the photon flux. The photo-generated charges are then transferred across the length of the CCD, from pixel to pixel, towards an output node. Of great importance is the so called ‘charge transfer inefficiency’ (CTI) defined as the fraction of the charge packet lost at each transfer.

During the development phase, the CTI effect may be improved by a variety of strategies ranging from silicon wafer improvements to CCD clocking optimization. Through such efforts, scientific CCDs typically achieve CTI performances lower than 0.00001.

Once in space, the CCDs will be inevitably exposed to energetic particles, mostly MeV protons emitted by the sun, which will induce defects in the CCD silicon lattice by displacement damage. As a result, the radiation induced (RICTI) of the devices will slowly increase (deteriorate). Over the last decade, this type of CTI has been identified as an extremely critical radiometric error in astronomic missions such as Hubble, Gaia, and Euclid. Due to ever stringent radiometric requirements, newer Earth observation missions, such as Sentinel 4, are also expected to be critically affected.

Ultimately, the RICTI effect translates into a spectral (and spatial) distortion at L1b. The distortion is rather complex as it depends, on at least, the following parameters:

This work aims at providing a brief overview of the tools developed in the context of the S4 project to quantify and correct the detrimental effects of RICTI. The paper is divided into four sections each devoted to elaborating on the following topics:

GOVERNING EQUATION

Consider an electron cloud sitting within a detector pixel which has been damaged by radiation. The cloud contains a total of N_T traps. Some of these are occupied by electrons, N_{o, i}. The question arises: given such an initial state, how would the number of free and occupied traps evolve after a certain time has passed? This section provides a brief overview of our physical understanding of this problem.

This basic problem may be described by a rate equation, afterwards referred to as the Governing Equation, of the form:

The capture of charges by a trap is considered to be stochastic processes best described by an exponential decay time constants given by:

The release of charges from a trap is thought of in similar terms as charge capture:

The electron density within the cloud is directly linked to the capture time. It is formulated as:

Where the volume of the charge is related to the number of electrons present as

The parameter β may take values between 0 and 1. For the case β = 0, the volume of the cloud would remain constant independently of the signal level. For the case β = 1, the volume of the cloud scales linearly with signal level.

CTI MODEL

Applying the basic problem outlined in the previous section to solving the RICTI effect to an entire spectral or spatial line in the S4 detector is not straightforward. Consider for a second what happens as charges are transferred across a CCD and assume that the observer is pegged to a particular pixel on the detector. The important points are illustrated in Fig. 2.

Fig. 1.

Governing equation. The basic problem of trap occupation change within an electron cloud

Fig. 2.

Evolution of cloud size and trap occupation as the detector transfer takes place

In particular, the following steps are noted:

To account for this, the basic equation needs to be solved independently in volume A and volume B, and the net change of occupied traps in the pixel corresponds to the summation of the net change of occupied traps in each of the two volumes. A generic scheme of the Sentinel 4 CTI model is presented in Fig. 3.

Fig. 3.

Overall scheme of the S4 RICTI forward model

The overall principle is as follows:

PERFORMANCES

In this chapter we utilized the RICTI forward model to compute how RICTI would distort real S4 spectra. A few important comments follow:

Table 1

Trap parameters within then NIR detector

Table 2

Detector parameters for the NIR path

Keeping the above points in mind, a typical applicable scene in the nir, is pushed through two simulations. In the first case, the detector does not have any radiation damage (nominal case). In the second case, the detector has the RICTI damages that are expected to be incurred by the end of life of our instrument. The resulting L1b spectra are shown in the Fig. 4.

Fig. 4.

L1b spectra produced in the simulation. The ‘nominal’ corresponds to the L1b product associated with a detector lacking RICTI. The ‘cti’ spectrum corresponds to the L1b product associated with the S4’s end of life

On a first impression, the two spectra coincide. Nevertheless, when the difference between these is plotted, it becomes clear that significant deviations are found as shown in Fig. 5. This is especially true for the regions close to the deep absorption lines.

Fig. 5.

Difference between the ‘nominal’ and the ‘cti’ spectrum revealing spectral distortion caused by the RICTI effect

The ARA and the RSRA were then computed using the distorted CTI spectra and the results are shown in Fig. 6. It is clear that the impact of the RICTI effect on these two performance requirements is extremely critical. In the case of the ARA, the RICTI effect consumes almost 50% of the budget. In the case of the RSRA, the situation is more critical as the RICTI effect by itself consumes the entire budget available

Fig. 6.

Applicable performances. On the right: the ARA. On the left: the RSRA. In both cases the effect of the RICTI effect is critical

CORRECTION

It was shown that the radiometric errors induced by the radiation-CTI effect can simply not be accommodated within the performance budgets. In particular the case of RSRA becomes severe. Strategies are thus necessary to reduce achieve overall compliances to these two performance requirements. A simple alternative would be to allow for the detectors to deteriorate through the lifetime of the instrument, but to introduce a specific RICTI correction algorithm into the L1b processor. For this approach to be feasible, the demand on computational resources must be kept low, the speed of the correction must be fast, and the physical parameters required must be either known a priori or must be accessible through the calibration/verification routines in orbit.

A RICTI correction concept which meets all these constrains is sketched out in Fig. 7. It is a forward correction concept, very similar to the straylight correction concepts found in state of the art scientific instrumentation.

Fig. 7.

Schematic showing the proposed RICTI correction concept

The algorithm works as follows:

The improvement in ARA and RSRA was tested by carrying out a dedicated simulation. The scenes ingested were slightly different from the scenes used in the previous chapter. Nevertheless, the trap and detector characteristics were equivalent. The improvement in ARA and RSRA when the proposed correction concept is implemented is shown in Fig. 8.

Fig. 8.

Correction efficiency comparing the performances obtained with an uncorrected spectrum against a corrected spectrum. On the right: ARA. On the left: the RSRA

These results show that the proposed concept has the capability of reducing the error on AbsRA and on RsRA induced by the RICTI effect by approximately 3 decades. Such large correction efficiencies imply that the CTI effect would be a minimal contributor to the radiometric error budgets

PARAMETER LIST

For clarity, we summarize all the parameters that are used in the document in this section.