the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 01 Nov 2023
A directional surface reflectance climatology determined from TROPOMI observations
Martin de Graaf
Victor Trees
Pavel Litvinov
Oleg Dubovik
Piet Stammes
Abstract. In this paper we introduce a spectral surface reflectivity climatology based on observations made by the TROPOMI instrument on board the Sentinel-5P satellite. The database contains the directionally dependent Lambertian-equivalent reflectivity (DLER) of the Earth’s surface for 21 wavelength bands ranging from 328 nm to 2314 nm and for each calendar month. The spatial resolution of the database grid is 0.125° × 0.125° . A recently developed cloud shadow detection technique is implemented to avoid dark scenes due to cloud shadow. In the database, the anisotropy of the surface reflection is described using a third-order parameterisation of the viewing angle dependence. The viewing angle dependence of the DLER is analysed globally and for a selection of surface type regions. The dependence is found to agree with the viewing angle dependence found in the GOME-2 surface DLER database. Differences exist, related to the actual solar position. On average, the viewing angle dependence in TROPOMI DLER is weaker than for GOME-2 DLER, but still important.
Validation of the new database was first performed by comparison of the non-directional TROPOMI surface LER with heritage LER databases based on GOME-1, OMI, SCIAMACHY, and GOME-2 data. Agreement was found within 0.002–0.02 in the UV-VIS (below 500 nm), up to 0.003 in the NIR (670–772 nm), and below 0.001 in the SWIR (2314 nm). These performance numbers are dominated by the performance over ocean, but they are in most cases also representative for land surfaces. For the validation of the directional TROPOMI surface DLER we made use of comparison with MODIS surface BRDF for a selection of surface type regions. In all cases the DLER performed significantly better than the traditional LER and we found good agreement with MODIS surface BRDF.
The TROPOMI surface DLER database is a clear improvement on previous surface albedo databases and can be used as input not only for satellite retrievals from TROPOMI observations, but also for retrievals from observations from other polar-orbiting satellite instruments provided that their equator crossing time is close to that of TROPOMI. The algorithm that is introduced in this paper can be used for the retrieval of surface reflectivity climatologies from other polar satellite missions as well, including OLCI on the Sentinel-3 satellites, Sentinel-5 and 3MI on the MetOp-SG-A1 satellite to be launched in 2025, and the future CO2M mission.
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Lieuwe G. Tilstra et al.
Status: open (until 27 Dec 2023)
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RC1: 'Comment on amt-2023-222', Anonymous Referee #1, 27 Nov 2023
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I found this paper to be carefully constructed and well written. The authors provide a clear motivation for the work, and did a good job explaining details that might not be obvious to all readers. For the most part the reader need not be familiar with previous work to understand and follow the discussion in this paper.
Section 1
The authors fail to discuss the version of the TropOMI Level 1B product used in this work until Section 6.3. The version should be cited early in the paper along with the doi of the data. As the authors note, the TOA reflectance changes between product versions so it's important to state key facts early.A similar criticism can be made regarding accuracy requirements. The requirements for this work best belong in the introduction where their origin can also be described.
Use of a DLER product from TropOMI is pretty much limited to satellite observations in a 1330 orbit, possibly a few others if reciprocity is assumed. While there are a significant number of instruments orbiting at this time of day it still limits the application of these results. If the authors were to assume surface BRDF models (e.g. from MODIS) it should be possible to derive a total hemispheric reflectance (THR) from these measurements. A THR product can broaden the reach of these data, allowing a larger pool of potential comparisons including instruments in morning polar orbits and geostationary instruments. The authors have effectively already performed such a comparison between afternoon orbit TropOMI data and the morning orbit MODIS data. Just a thought for a future paper.Section 4.5
It would be useful to know how sensitive LER is to the AI screening level as a way of evaluating the chosen threshold. Have the authors performed such a study? Can they provide some more justification for the screening threshold of AI = 2?Sections 4.6 & 4.7
In these two sections the authors are perhaps a bit too reliant on the reader having read and remembered their previous publication on this topic. The reader is left wondering about the general approach. For example, an elaborate method of selecting a representative LER for each grid is described in Section 4.6. No mention is given to view angles, so one must assume that all angles are included in the final distributions. However, a selection of values is then made based on the lowest 10% (or the mode of the distribution in the case of snow/ice). Such a selection is necessarily biased toward view angles where the BRDF is at a minimum, meaning the Section 4.6 LER is dependent on viewing conditions. In Eqn. 8 a quantity ALER is introduced for the first time with no explanation of where it comes from. The reader can deduce from Lines 237-239 that ALER is the reflectivity of water, which is not a Lambertian quantity except in ideal conditions. How does the LER of Section 4.6 relate to Eqn. 8? Section 4.7 stands out in its need for clearer explanation when compared with the others parts of this paper.Section 5.4
The authors use a visual example to demonstrate the need for and the effectiveness of their cloud screening method. In the example shown in Figure 6 it is not immediately obvious that every feature in black is a cloud shadow that should be removed. Shadows at this location should appear to the north north-east of the actual cloud, and it's rather difficult to imagine clouds that could produce some of the shadows seen to the north and north-east of Iceland in Figure 6a. It would be helpful if the authors could include a VIIRS RGB image or a TropOMI reflectivity image for the same time period to show the actual cloud field. There may be clever ways of including this as a transparency overlayed on the minimum reflectivity maps.Section 6.1
The authors should cite the product versions used in all the comparisons described in this section.Section 6.2.1, Lines 408-411
The authors cite Rayleigh scattering effects as a reason for not comparing DLER in the UV to the MODIS BRDF. To first order Rayleigh scattering should already be taken into account via the derivation of surface reflectivity given by Eqn. 2. The more important reason for not comparing in the UV is that MODIS makes no measurements at these wavelengths and few, if any, estimates of BRDF exist at these wavelengths. It's worth noting that a UV comparison in conjunction with a long-wave VIS comparison to the MODIS BRDF could provide a useful assessment of how BRDF might change between VIS and the UV.Section 6.3, Lines 481, 482
The authors state that TropOMI calibration is much improved in the v2.1 Level 1 product compared to v1.0. Can they provide a reference to substantiate this claim? There exists evidence that the TOA reflectance in the latter version is actually less accurate than in the earlier version. Figure 10d in particular is suggestive of a calibration difference of ~2%.Citation: https://doi.org/10.5194/amt-2023-222-RC1
Lieuwe G. Tilstra et al.
Lieuwe G. Tilstra et al.
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