Opportunity for GNSS Reflectometry in Sensing the Regional Climate and Soil Moisture Instabilities in Myanmar

The climate crisis is happening globally, and the consequent process has revealed soil evolution and meteorological interactions. The GNSS reflectometry (GNSS-R) technique recently encompassed sea surface monitoring, land changes, and snow sensing in addition to position, navigation, and timing. After the launch of NASA’s eight CYGNSS satellites, spaceborne soil moisture retrieval has become more opportune in a global and regional investigation. The research carried out by the CYGNSS DDM SNR with SMAP data to correlate diurnal mean soil moisture sensing was analyzed in the regional study of Myanmar, which is prone to climatic and weather conditions. The results showed that spaceborne GNSS-R soil moisture sensitivity was very useful during seasonal changes in regional observation. The DDM SNR surface reflectivity was strongly correlated with soil moisture according to surface temperature variations prepared from SMAP passive reflectometry. Sentinel SAR-1 data included the validation and verification of flood-prone areas affected by tropical storm surges or weather depressions in the monsoon season. The availability of surface reflectivity primarily relied on the surface roughness, surface temperature, and vegetation opacity for soil moisture retrieval.

Daily Flood Monitoring Based on Spaceborne GNSS-R Data: A Case Study on Henan, China

Flood is a kind of natural disaster that is extremely harmful and occurs frequently. To reduce losses caused by the hazards, it is urgent to monitor the disaster area timely and carry out rescue operations efficiently. However, conventional space observers cannot achieve sufficient spatiotemporal resolution. As spaceborne GNSS-R technique can observe the Earth’s surface with high temporal and spatial resolutions; and it is expected to provide a new solution to the problem of flood hazards. During 19–21 July 2021, Henan province, China, suffered a catastrophic flood and urban waterlogging. In order to test the feasibility of flood disaster monitoring on a daily basis by using GNSS-R observations, the CYGNSS (Cyclone Global Navigation Satellite System) Level 1 Science Data were processed for a few days before and after the flood to obtain surface reflectivity by correcting the analog power. Afterwards, the flood was monitored and mapped daily based on the analysis of changes in surface reflectivity from spaceborne GNSS-R mission. The results were evaluated based on the image from MODIS (Moderate Resolution Imaging Spectroradiometer) data, and compared with the observations of SMAP (Soil Moisture Active Passive) in the same period. The results show that the area with high CYGNSS reflectivity corresponds to the flooded area monitored by MODIS, and it is also in high agreement with SMAP. Moreover, CYGNSS can achieve more detailed mapping and quantification of the inundated area and the duration of the flood, respectively, in line with the specific situation of the flood. Thus, spaceborne GNSS-R technology can be used as a method to monitor floods with high temporal resolution.

Impact of deforestation on radiative and thermal regimes of the territory of Ukraine on the base of global climate models data

This paper is dedicated to the influence of partial deforestation with using global retrospective modelling data from The Land Use Model Intercomparison Project (LUMIP) for the territory of Ukraine. This experiment aims to global gradual deforestation and has two phases. The first phase, defined as the pre-industrial period (1850—1899) with constant unchangeable anthropogenic impact. For this period deforestation modelled with further replacement with grass cover with a linear trend 400000 km2/yr or 20 million km2 per 50 years in general. The second phase is next 30 years with no significant changes in forest cover (1900—1929). For conducting this research the data of several global climate models were applied. The results of analysis have demonstrated that a partial deforestation with grass substitution influences the surface reflectivity or albedo and redistribution of shortwave radiative fluxes. In turn, it provokes changes in thermal regime. It was found that the most significant changes in surface reflectivity and the strongest correlation coefficients between albedo and deforestation are in the winter season due to the presence of snow cover. As a result, statistical significant increase of albedo is with maximum values up to 24 %/50 years in some grids in winter. Then in the summer season maximal changes are up to 2.7 %/50 years due to small differences between forest and grass albedos. As a consequence, changes in albedo cause changes in surface and air temperature regimes. Strong dependencies were found in winter between changes in albedo and temperatures with maximum temperature decrease 2.5…2.0 %/50 years. In warm season correlations are weaker in comparison to cold season, but nevertheless, temperatures decrease also take place with maximum values 2.0…1.5 %/50 years. The analysis between deforestation and daily air temperature range has shown that particularly in winter season there is an increase of 0.5...1.5 %/50 years, whereas such tendency is not observed in warm season. Calculations of year air temperature range demonstrated controversial results among climate models, as follows it is hard to make a conclusion about the contribution of forest cover reduction to changes in this index. It was revealed, that global climate models with higher resolution are more sensitive to changes in albedo and, as a consequence to other characteristics than models with coarse ones. It should be noticed that obtained results concern pre-industrial period with minimal anthropogenic impact, when observed a stable snow cover in winter in Ukraine. In the current climate change with significant warming and reduction of snow season duration deforestation can have opposite effects on radiative and thermal regimes that require further studying.

Directionally dependent Lambertian-equivalent reflectivity (DLER) of the Earth's surface measured by the GOME-2 satellite instruments

In this paper we introduce the new concept of directionally dependent Lambertian-equivalent reflectivity (DLER) of the Earth's surface retrieved from satellite observations. This surface DLER describes Lambertian (isotropic) surface reflection which is extended with a dependence on the satellite viewing geometry. We apply this concept to data of the GOME-2 satellite instruments to create a global database of the reflectivity of the Earth's surface, providing surface DLER for 26 wavelength bands between 328 and 772 nm as a function of the satellite viewing angle via a second-degree polynomial parameterisation. The resolution of the database grid is 0.25∘ by 0.25∘, but the real, intrinsic spatial resolution varies over the grid from 1.0∘ by 1.0∘ to 0.5∘ by 0.5∘ down to 0.25∘ by 0.25∘ by applying dynamic gridding techniques. The database is based on more than 10 years (2007–2018) of GOME-2 data from the MetOp-A and MetOp-B satellites. The relation between DLER and bi-directional reflectance distribution function (BRDF) surface reflectance is studied using radiative transfer simulations. For the shorter wavelengths (λ<500 nm), there are significant differences between the two. For instance, at 463 nm the difference can go up to 6 % at 30∘ solar zenith angle. The study also shows that, although DLER and BRDF surface reflectances have different properties, they are comparable for the longer wavelengths (λ>500 nm). Based on this outcome, the GOME-2 surface DLER is compared with MODIS surface BRDF data from MODIS band 1 (centred around 645 nm) using both case studies and global comparisons. The conclusion of this validation is that the GOME-2 DLER compares well to MODIS BRDF data and that it does so much better than the non-directional LER database. The DLER approach for describing surface reflectivity is therefore an important improvement over the standard isotropic (non-directional) LER approaches used in the past. The GOME-2 surface DLER database can be used for the retrieval of atmospheric properties from GOME-2 and from previous satellite instruments like GOME and SCIAMACHY. It will also be used to support retrievals from the future Sentinel-5 UVNS (ultraviolet, visible, near-infrared, and short-wave infrared) satellite instrument.

MICRU: an effective cloud fraction algorithm designed for UV–vis satellite instruments with large viewing angles

Clouds impact the radiative transfer of the Earth's atmosphere and strongly influence satellite measurements in the ultraviolet–visible (UV–vis) and infrared (IR) spectral ranges. For satellite measurements of trace gases absorbing in the UV–vis spectral range, particularly clouds ultimately determine the vertical sensitivity profile, mainly by reducing the sensitivity for trace-gas columns below the cloud. The Mainz iterative cloud retrieval utilities (MICRU) algorithm is specifically designed to reduce the error budget of trace-gas retrievals, such as those for nitrogen dioxide (NO2), which strongly depends on the accuracy of small cloud fractions (CFs) in particular. The accuracy of MICRU is governed by an empirical parameterisation of the viewing-geometry-dependent background surface reflectivity taking instrumental and physical effects into account. Instrumental effects are mainly degradation and polarisation effects; physical effects are due to the anisotropy of the surface reflectivity, e.g. shadowing of plants and sun glitter. MICRU is applied to main science channel (MSC) and polarisation measurement device (PMD) data collected between April 2007 and June 2013 by the Global Ozone Monitoring Experiment 2A (GOME-2A) instrument aboard the MetOp-A satellite. CFs are retrieved at different spectral bands between 374 and 758 nm. The MICRU results for MSC and PMD at different wavelengths are intercompared to study CF precision and accuracy, which depend on wavelength and spatial correlation. Furthermore, MICRU results are compared to FRESCO (fast retrieval scheme for clouds from the oxygen A band) and OCRA (optical cloud recognition algorithm) operational cloud products. We show that MICRU retrieves small CFs with an accuracy of 0.04 or better for the entire 1920 km wide swath with a potential bias between −0.01 and −0.03. CFs retrieved at shorter wavelengths are less affected by adverse surface heterogeneities. The comparison to the operational CF algorithms shows that MICRU significantly reduces the dependence on viewing angle, time, and sun glitter. Systematic effects along coasts are particularly small for MICRU due to its dedicated treatment of land and ocean surfaces. The MICRU algorithm is designed for spectroscopic instruments ranging from the GOME to Sentinel-5P/Tropospheric Monitoring Instrument (TROPOMI) but is also applicable to UV–vis imagers like the Advanced Very High Resolution Radiometer (AVHRR), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and Sentinel-2.

Effect of Different Atmospheric Correction Algorithms on Sentinel-2 Imagery Classification Accuracy in a Semiarid Mediterranean Area

Multi-temporal imagery classification using spectral information and indices with random forest allows improving accuracy in land use and cover classification in semiarid Mediterranean areas, where the high fragmentation of the landscape caused by multiple factors complicates the task. Hence, since data come from different dates, atmospheric correction is needed to retrieve surface reflectivity values. The Sen2Cor, MAJA and ACOLITE algorithms have proven their good performances in these areas in different comparative studies, and DOS is a basic method that is widely used. The aim in this study was to test the feasibility of its application to the data set to improve the values of accuracy in classification and the performance in properly labelling different classes. Additionally, we tried to correct accuracy and separability mixing predictors with different algorithms. The results showed that, using a single algorithm, the general accuracy and kappa index from ACOLITE were the highest, 0.80 ± 0.01 and 0.76 ± 0.01., but the separability between problematic classes was slightly improved by using MAJA. Any combination of the different algorithms tested increased the values of classification, although they may help with separability between some pairs of classes.