Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery

The wide field of view (WFV) imaging system onboard the Chinese GaoFen-1 (GF-1) optical satellite has a 16-m resolution and four-day revisit cycle for large-scale Earth observation. The advantages of the high temporal-spatial resolution and the wide field of view make the GF-1 WFV imagery very popular. However, cloud cover is an inevitable problem in GF-1 WFV imagery, which influences its precise application. Accurate cloud and cloud shadow detection in GF-1 WFV imagery is quite difficult due to the fact that there are only three visible bands and one near-infrared band. In this paper, an automatic multi-feature combined (MFC) method is proposed for cloud and cloud shadow detection in GF-1 WFV imagery. The MFC algorithm first implements threshold segmentation based on the spectral features and mask refinement based on guided filtering to generate a preliminary cloud mask. The geometric features are then used in combination with the texture features to improve the cloud detection results and produce the final cloud mask. Finally, the cloud shadow mask can be acquired by means of the cloud and shadow matching and follow-up correction process. The method was validated using 108 globally distributed scenes. The results indicate that MFC performs well under most conditions, and the average overall accuracy of MFC cloud detection is as high as 96.8%. In the contrastive analysis with the official provided cloud fractions, MFC shows a significant improvement in cloud fraction estimation, and achieves a high accuracy for the cloud and cloud shadow detection in the GF-1 WFV imagery with fewer spectral bands. The proposed method could be used as a preprocessing step in the future to monitor land-cover change, and it could also be easily extended to other optical satellite imagery which has a similar spectral setting.Comment: This manuscript has been accepted for publication in Remote Sensing of Environment, vol. 191, pp.342-358, 2017. (http://www.sciencedirect.com/science/article/pii/S003442571730038X

Automatic Delineation of Clouds and Their Shadows in Landsat and CBERS (HRCC) Data

The presence of clouds and their shadows is an obvious problem for maps obtained from multispectral images. As a matter of fact, clouds and their shadows create occluded and obscured areas, hence information gaps that need to be filled. The usual approach-pixel substitution-requires first to recognize the cloud/shadow pixels. This work presents a cloud/shadow delineation algorithm, the cloud/shadow delineation tool (CSDT) designed for Landsat and CBERS medium resolution multispectral data. The algorithm uses a set of literature indices, as well as a set of mathematical operations on the spectral bands, in order to enhance the visibility of the cloud/shadow objects. The performance of CSDT was tested on a set of scenes from the Landsat and CBERS catalogues. The obtained results showed more accurate and stable performance on Landsat data. In order to validate the proposed approach, this work presents also a comparison with the F-mask algorithm on Landsat scenes. Results show that the F-mask technique tends to overestimate the cloud cover, while CSDT slightly underestimates it. However, accuracy measures show a significantly better performance of the proposed method than the F-mask algorithm in our investigation

Leveraging Sentinel-2 data to complement ground-based cloud cover statistics

none5noPrecise assessment of local cloud cover at a detailed spatial resolution can be useful for various types of applications, including for example forecast of power production by photovoltaic panels [1], crop quality prediction [2], or even micro-climatic and eco-dynamics studies [3]. Figure 1: The ACTRIS network of ground stations with cloud profiling capabilities. Various sensor networks exist, which locally sense the presence and profile of clouds; an example is reported in Figure 1. Such networks are however geographically too coarse to determine the cloud cover at any given location where it is needed with high positional accuracy as an input to an e.g. microclimatic model. Spaceborne remote sensing can be used as an alternate source of cloud cover information. As it is well-known, geostationary-orbiting (GEO) weather satellites such as Meteosat-8 can provide large-scale images of the Earth, from which cloud maps can be derived. Data from such platforms, however, is generated at a spatial resolution of a few kilometres, that may be too coarse for some applications, such as microclimate studies. An alternate approach, still based on spaceborne data but at a higher resolution, may be attempted using multispectral low-Earth-orbit sensors. As visible in Figure 2, sensors like Sentinel-2 provide a clear view of which areas are covered by clouds at a fairly high spatial resolution (10 m at best), and various algorithms are available in scientific literature to identify cloudy pixels, like Fmask [4], ACCA [5] and others [6]. Figure 2: two true-colour images derived from Sentinel-2 data over Dubai, UAE with (left) and without (right) cloud cover. Normalization of values for true colour representation results in altered tones on land. EO-data-processing environments such as the ESA Research and User Support (RUS) [7], or Google Earth Engine (GEE), feature indeed cloud detection and mapping capabilities, which we are exploiting for our work. In Figure 3, an example of output is visualized. In the case of Sentinel-2 data, a cloud mask is even provided as a standard component of the downloaded dataset. Figure 3: cloud masks automatically extracted from the datasets represented in figure 2. We have chosen 4 test sites around the globe, in 4 different climate zones according to the standard Köppen classification of climate zones [8], in order to diversify the contexts of our experiments: Pavia, Italy (Cfa: hot-summer, temperate, humid climate) Dubai, UAE (Bwh: very hot desert climate) Nassau, Bahamas (Aw: savannah-like climate) Stockholm, Sweden (Dwb: cold climate, dry winter) We have found and stored precise cloud cover statistics for all the 4 test sites. We then registered to ESA RUS, to handle and process Sentinel data, and to Google Earth Engine for LANDSAT data; we opted for remote, “cloud” processing systems to facilitate building and processing of thick stacks of data, in order to generate more significant statistics. We are now in the process of extracting long series of Sentinel-2 and LANDSAT data in order to systematically map cloud cover and generate the corresponding cloud statistics. These derived cloud statistics will be compared with the “ground truth” ones generated by ground stations and collected before stating the experiment. A thorough comparison will be made with the available, ground-based statistics. Although experiments are still at an initial stage, we have identified some issues that may interfere with the production of reliable statistics. One is illustrated in Figure 4, and consists of junk values along diagonal stripes in LANDSAT data, which happen to be mistaken for clouds by the cloud detection algorithm. Figure 4: the Dubai site. On the left, a true-colour representation of a LANDSAT multispectral dataset acquired in July 2017, on the right the corresponding cloud map. Note the diagonal spurious cloudy pixels matching the diagonal, bright line found in the upper half of the left-side image. The second one consists of sandy riverbeds that may be confused for clouds in summer months where water is at its minima. An example is shown in Figure 5, where the almost-dry Po river leaves visible large swathes of bright sand, which the cloud classifier wrongly associates with cloud presence. Figure 5: same as figure 4, on the test site of Pavia. Note the sandy riverbeds have been mistaken for clouds. Suitable countermeasures will be developed in order to suppress these causes of errors. Another possible matter is the temporal density of statistics. In order to assess the density of our statistical sample, we have made a preliminary search for LANDSAT data and found the figures illustrated in Table 1 for selected months in year 2017. Pavia (Italy) Dubai (UAE) Stockholm (Sweden) Nassau (Bahamas) January 4 4 none 2 April 5 4 6 2 July 6 4 7 2 November 2 4 none 2 Table 1: number of LANDSAT images identified on each test site for some selected months of year 2017 Some months are not well-covered, but these figures refer to a single sensor; we are confident that merging more datasets from multiple sources -as easily feasible within the selected processing environments- will remarkably improve the situation. Our results will be presented at the conference and discussed in light of the intended goal of this work. The purpose is not that of raising doubts on the validity of ground-based measurement, but rather to assess whether spaceborne data can be used as a valid replacement where ground stations may not be installed such as in remote locations whose statistics are nonetheless significant for purposes of climate studies. Future developments will include investigating possible fuzzy definitions of cloud cover and the introduction of multi-level statistics where the binary splitting into cloudy/non-cloudy class will be replaced by a "degree of cloudiness" on each image, and statistics adjusted accordingly. This work is being carried out as a group exercise within a Remote Sensing course at the University of Pavia, which has recently been selected as a new FabSpace under the H2020 “FabSpace 2.0” project of the European Union. A “Space Communication and Sensing” graduate track is currently active within the Engineering Faculty, and the aim of these exercises is that of showing the benefits of Earth Observation and encouraging public involvement in spaceborne monitoring of the terrestrial environment. References [1] Lipperheide, M., J. L. Bosch, and J. Kleissl. "Embedded nowcasting method using cloud speed persistence for a photovoltaic power plant." Solar Energy 112 (2015): 232-238. [2] Hoogenboom G. Contribution of agrometeorology to the simulation of crop production and its applications. Agricultural and Forest Meteorology. 2000;103(1–2):137–57. [3] Nicole M. Hughes, Kaylyn L. Carpenter, David K. Cook, Timothy S. Keidel, Charlene N. Miller, Junior L. Neal, Adriana Sanchez, William K. Smith, Effects of cumulus clouds on microclimate and shoot-level photosynthetic gas exchange in Picea engelmannii and Abies lasiocarpa at treeline, Medicine Bow Mountains, Wyoming, USA. Agricultural and Forest Meteorology, Volume 201, 2015, Pages 26-37, ISSN 0168-1923. [4] Z. Zhu, C.E. Woodcock: Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens. Environ., 118 (2012), pp. 83-94 [5] R.R. Irish, J.L. Barker, S.N. Goward, T. Arvidson: Characterization of the Landsat-7 ETM + automated cloud-cover assessment (ACCA) algorithm. Photogramm. Eng. Remote Sens., 72 (10) (2006), pp. 1179- 1188, 10.14358/PERS.72.10.1179 [6] Harb, Mostapha, Paolo Gamba, and Fabio Dell’Acqua. "Automatic delineation of clouds and their shadows in Landsat and CBERS (HRCC) data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, no. 4 (2016): 1532-1542. [7] European Space Agency (ESA) Research and User Support (RUS) service portal. [Online] Available at: https://rus-copernicus.eu/portal/ [8] Koppen, W. (1923). Die Klimate der Erde. Walter de Gruyter, Berlin, Germany (in German).openBresciani, Laura; Curti, Alberto; Di Lorenzo, Benedetta; Modica, Camilla; Dell'Acqua, FabioBresciani, Laura; Curti, Alberto; Di Lorenzo, Benedetta; Modica, Camilla; Dell'Acqua, Fabi