Laboratory for
Climatology and Remote Sensing

Abstract:

Cloud top height (CTH) plays an important role in the Earth’s radiation budget and thus in climate change. However, accurate CTH retrieval based on satellite data remains a challenge when using currently widespread methods based on passive remote sensing satellite data, particularly over the Arctic Ocean. It is well known that using geometric correlation between CTH and cloud-shadow distance along the normal direction of sunlight, cloud-edge top height (CETH) can be determined. However, this cloud-shadow method (CSM) exhibited larger CTH errors due to low-resolution weather satellite data of the past. The aim of the paper is to analyze the effect of higher-resolution images (MODIS with 250-m resolution) on CSM accuracy. We present a method in which CETH is first retrieved using the MODIS data, and to calculate CTH in the center of cloud region by combining the calculated CETH and cloud top brightness temperature using an environmental temperature lapse rate method. We validated 14 cases over the Arctic Ocean by the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) CTH lidar product and another two cases by radiosonde data. CSM revealed better per- formance (mean difference is 0.45 km and standard deviation is 0.92 km) compared to the operational MODIS CTH product (mean difference is 0.78 km and standard deviation is 1.78 km), when validated by the CALIPSO product. When validated by sounding data, CSM also performed better than the MODIS CTH product: the CSM CTH errors are 0.41 and 0.52 km in two sounding cases, while the corresponding MODIS CTH errors are 1.10 and 1.07 km. The CSM technique especially showed better performance for double-deck cloud systems. We conclude that further improvements in CTH accuracy based on CSM can be achieved by mainly using higher- resolution satellite imagery.

Abstract:

Understanding of evapotranspiration (ET) processes over Andean mountain environments is crucial, particularly due to the importance of these regions to deliver water-related ecosystem services. In this context, the detection of spatio-temporal changes in ET remains poorly investigated for specific Andean ecosystems, like the páramo. To overcome this lack of knowledge, we implemented the energy-balance model METRIC with Landsat 7 ETM+ and MODIS-Terra imagery for a páramo catchment. The implementation contemplated adjustments for complex terrain in order to obtain daily, monthly and annual ET maps (between 2013 and 2014). In addition, we compared our results to the global ET product MOD16. Finally, a rigorous validation of the outputs was conducted with residual ET from the water balance. ET retrievals from METRIC (Landsat-based) showed good agreement with the validation-related ET at monthly and annual steps (mean bias error <8 mm·month?1 and annual deviation <17%). However, METRIC (MODIS-based) outputs and the MOD16 product were revealed to be unsuitable for our study due to the low spatial resolution. At last, the plausibility of METRIC to obtain spatial ET retrievals using higher resolution satellite data is demonstrated, which constitutes the first contribution to the understanding of spatially-explicit ET over an alpine catchment in the neo-tropical Andes.

Abstract:

Knowledge of cloud properties such as cloud effective radius (aef) and optical thickness (?) is essential to understand their role in the dynamic radiation budget and climate change. The Spinning Enhanced Visible and Infrared Instrument (SEVIRI) on board Meteosat Second Generation (MSG) with its high temporal resolution (15 min), permits a quasi-continuous monitoring of the evolution of cloud properties. This has motivated the adaptation of the SLALOM (SimpLe Approximations for cLOudy Media) algorithm, a semi-analytical cloud property retrieval technique to MSG SEVIRI. The optical properties retrieved by SLALOM are compared against the well known and validated NASA MODIS cloud property product (MODIS 06) as well as the cloud optical depth product (2B-TAU) of CloudSat. The results are shown over the North Atlantic and over the European continent with the intention of determine the relative accuracy between SLALOM and the other retrievals. Over the North Atlantic, SLALOM-based cloud properties retrieved from SEVIRI datasets show a good agreement with the MODIS 06 product with correlation coefficients of 0.93 (?) and 0.82 (aef). The largest deviations were found in less homogeneous cloud areas that are characterized by broken clouds and toward the cloud borders. Moreover, SLALOM optical thickness values are well within the range of corresponding CloudSat 2B-TAU optical thickness values which can be found within a SEVIRI pixel, except for ? < 5 where SLALOM tends to overestimate ?. Despite the different sensor characteristics and viewing geometries, the retrieved cloud properties compare very well. Over Europe, the evaluation between SLALOM and MODIS 06 showed larger differences. We attribute this to (a) uncertainties related to the surface albedo which is treated differently in the algorithms and is based on different albedo maps and (b) inhomogeneities of clouds which exhibit quite complex structures particularly over land. The latter are detected on different scales by MODIS and SEVIRI because of their different spatial resolutions. Given the demonstrated accuracy of SLALOM using MSG SEVIRI data there is a wide spread of potential applications.

Abstract:

The relationship between satellite-derived low cloud frequency and the occurrence of tropical montane cloud forest (TMCF) in Taiwan was investigated. From daily MODIS cloud mask products between 2003 and 2012 the low cloud class was extracted and mean low cloud frequency was calculated for Taiwan. This low cloud frequency map was blended with an existing plot-based vegetation classification for Taiwan to analyze the relationship between low cloud frequency and TMCF occurrence. Receiver operating characteristics curves and the area under the ROC curve (AUC) were used to analyze if a relationship exists. No relationship was found for all four TMCF types taken together (AUC = 0.61) and for the dominant TMCF type, Quercus montane evergreen broad-leaved cloud forest (AUC = 0.5). Strong relationships were found for the two spatially-restricted TMCF types, Fagus montane deciduous broad-leaved cloud forest (AUC = 0.91) and Pasania-Elaeocarpus montane evergreen broad-leaved forest (AUC = 0.84), as well as for the second dominant type Chamaecyparis montane mixed cloud forest (AUC = 0.74). The results show that low cloud frequency thresholds might be associated with specific cloud forest types in Taiwan. Further studies should incorporate information about cloud base height, cloud density, and cloud immersion time as well as satellite-based cloud frequency information with a higher temporal resolution. Combination with satellite-based land cover classifications for Taiwan would allow quasi-continuous observation of TMCF changes. Such knowledge would be the precondition for effective protective actions concerning this exceptional but threatened ecosystem.

Abstract:

The distinction made by satellite data between ground fog and low stratus is still an open problem. A proper detection scheme would need to make a determination between low stratus thickness and top height. Based on this information, stratus base height can be computed and compared with terrain height at a specific picture element. In the current paper, a procedure for making the distinction between ground fog and low-level stratus is proposed based on Moderate Resolution Imaging Spectroradiometer (MODIS, flying on board the NASA Terra and Aqua satellites) daytime data for Germany. Stratus thickness is alternatively derived from either empirical relationships or a newly developed retrieval scheme (lookup table approach), which relies on multiband albedo and radiative transfer calculations. A trispectral visible–near-infrared (VIS–NIR) approach has been proven to give the best results for the calculation of geometrical thickness. The comparison of horizontal visibility data from synoptic observing (SYNOP) stations of the German Weather Service and the results of the ground fog detection schemes reveals that the lookup table approach shows the best performance for both a valley fog situation and an extended layer of low stratus with complex local visibility structures. Even if the results are very encouraging [probability of detection (POD) = 0.76], relatively high percentage errors and false alarm ratios still occur. Uncertainties in the retrieval scheme are mostly due to possible collocation errors and known problems caused by comparing point and pixel data (time lag between satellite overpass and ground observation, etc.). A careful inspection of the pixels that mainly contribute to the false alarm ratio reveals problems with thin cirrus layers and the fog-edge position of the SYNOP stations. Validation results can be improved by removing these suspicious pixels (e.g., percentage error decreases from 28% to 22%).

Abstract:

A scheme for the detection of fog and low stratus over land during daytime based on data of the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument is presented. The method is based on an initial threshold test procedure in the MODIS solar bands 1–7 (0.62–2.155µm). Fog and low stratus detection generally relies on the definition of minimum and maximum fog and low stratus properties, which are converted to spectral thresholds by means of radiative transfer calculations (RTC). Extended sensitivity studies reveal that thresholds mainly depend on the solar zenith angle and, hence, illumination-dependent threshold functions are developed. Areas covered by snow, ice and mid-/high-level clouds as well as bright/hazy land surfaces are omitted from the initial classification result by means of a subsequent cloud-top height test based on MODIS IR band 31 (at 12 µm) and a NIR/VIS ratio test. The validation of the final fog and low stratus mask generally shows a satisfactory performance of the scheme. Validation problems occur due to the late overpass time of the TERRA platform and the time lag between SYNOP and satellite observations. Apparent misclassifications are mainly found at the edge of the fog layers, probably due to over- or underestimation of fog and low stratus cover in the transition zone from fog to haze.

Abstract:

Fog is a crucial driver of epiphyte richness in tropical montane cloud forests but its spatial occurrence and role in tropical lowland areas is poorly understood. Recent studies in French Guiana have reported high epiphyte richness in previously undescribed “tropical lowland cloud forest” (LCF) due to radiation fog. Here, we analyze the spatial extent of fog/low stratus (FLS) in lowland forests of French Guiana using the frequency distribution by means of night-time MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data for the years 2007–2010. The analysis is based on a newly developed dynamic threshold-test method relying on brightness temperature differences between thermal and mid-infrared bands. Individual thresholds for the discrimination between fog/low stratus and cloud-free pixels were retrieved by radiative transfer calculations and validated using discriminant analysis. The thresholds dynamically depend on total precipitable water (TPW) and the terrain-induced maximum possible sub-pixel fog coverage. The results of the new retrieval were validated using in-situ data and compared to results from existing fog detection algorithms, showing an improvement of the new detection scheme regarding the capability to detect sub-pixel fog coverage under varying TPW. FLS frequency maps derived from the novel fog classification scheme indicate a widespread distribution of night-time fog in river valleys, marking a multitude of potential areas for LCF throughout French Guiana. LCF is probably not only a local phenomenon but also may be widely distributed in river valleys in the lowland tropics, with significant consequences for biodiversity mapping in tropical lowland areas.

Abstract:

Plant coverage is a basic indicator of the biomass production in ecosystems. On the Tibetan Plateau, the biomass of grasslands provides major ecosystem services with regard to the predominant transhumance economy. The pastures, however, are threatened by progressive degradation, resulting in a substantial reduction in plant coverage with currently unknown consequences for the hydrological/climate regulation function of the plateau and the major river systems of SE Asia that depend on it and provide water for the adjacent lowlands. Thus, monitoring of changes in plant coverage is of utmost importance, but no reliable tools have been available to date to monitor the changes on the entire plateau. Due to the wide extent and remoteness of the Tibetan Plateau, remote sensing is the only tool that can recurrently provide area-wide data for monitoring purposes. In this study, we develop and present a grassland-cover product based on multi-sensor satellite data that is applicable for monitoring at three spatial resolutions (WorldView type at 2–5 m, Landsat type at 30 m, MODIS at 500 m), where the data of the latter resolution cover the entire plateau. Four different retrieval techniques to derive plant coverage from satellite data in boreal summer (JJA) were tested. The underlying statistical models are derived with the help of field observations of the cover at 640 plots and 14 locations, considering the main grassland vegetation types of the Tibetan Plateau. To provide a product for the entire Tibetan Plateau, plant coverage estimates derived by means of the higher-resolution data were upscaled to MODIS composites acquired between 2011 and 2013. An accuracy assessment of the retrieval methods revealed best results for the retrieval using support vector machine regressions (RMSE: 9.97%, 7.13% and 5.51% from the WorldView to the MODIS scale). The retrieved values coincide well with published coverage data on the different grassland vegetation types.