KappaMask: AI-Based Cloudmask Processor for Sentinel-2

The Copernicus Sentinel-2 mission operated by the European Space Agency (ESA) provides comprehensive and continuous multi-spectral observations of all the Earth's land surface since mid-2015. Clouds and cloud shadows significantly decrease the usability of optical satellite data, especially in agricultural applications; therefore, an accurate and reliable cloud mask is mandatory for effective EO optical data exploitation. During the last few years, image segmentation techniques have developed rapidly with the exploitation of neural network capabilities. With this perspective, the KappaMask processor using U-Net architecture was developed with the ability to generate a classification mask over northern latitudes into the following classes: clear, cloud shadow, semi-transparent cloud (thin clouds), cloud and invalid. For training, a Sentinel-2 dataset covering the Northern European terrestrial area was labelled. KappaMask provides a 10 m classification mask for Sentinel-2 Level-2A (L2A) and Level-1C (L1C) products. The total dice coefficient on the test dataset, which was not seen by the model at any stage, was 80% for KappaMask L2A and 76% for KappaMask L1C for clear, cloud shadow, semi-transparent and cloud classes. A comparison with rule-based cloud mask methods was then performed on the same test dataset, where Sen2Cor reached 59% dice coefficient for clear, cloud shadow, semi-transparent and cloud classes, Fmask reached 61% for clear, cloud shadow and cloud classes and Maja reached 51% for clear and cloud classes. The closest machine learning open-source cloud classification mask, S2cloudless, had a 63% dice coefficient providing only cloud and clear classes, while KappaMask L2A, with a more complex classification schema, outperformed S2cloudless by 17%

Evaluation of the dual-polarization weather radar quantitative precipitation estimation using long-term datasets

Accurate, timely, and reliable precipitation observations are mandatory for hydrological forecast and early warning systems. In the case of convective precipitation, traditional rain gauge networks often miss precipitation maxima, due to density limitations and the high spatial variability of the rainfall field. Despite several limitations like attenuation or partial beam blocking, the use of C-band weather radar has become operational in most European weather services. Traditionally, weather-radar-based quantitative precipitation estimation (QPE) is derived from horizontal reflectivity data. Nevertheless, dual-polarization weather radar can overcome several shortcomings of the conventional horizontal-reflectivity-based estimation. As weather radar archives are growing, they are becoming increasingly important for climatological purposes in addition to operational use. For the first time, the present study analyses one of the longest datasets from fully operational polarimetric C-band weather radars; these are located in Estonia and Italy, in very different climate conditions and environments. The length of the datasets used in the study is 5 years for both Estonia and Italy. The study focuses on long-term observations of summertime precipitation and their quantitative estimations by polarimetric observations. From such derived QPEs, accumulations for 1 h, 24 h, and 1-month durations are calculated and compared with reference rain gauges to quantify uncertainties and evaluate performances. Overall, the radar products showed similar results in Estonia and Italy when compared to each other. The product where radar reflectivity and specific differential phase were combined based on a threshold exhibited the best agreement with gauge values in all accumulation periods. In both countries reflectivity-based rainfall QPE underestimated and specific differential-phase-based product overestimated gauge measurements.Peer reviewe

Climatology of Convective Storms in Estonia from Radar Data and Severe Convective Environments

Data from the C-band weather radar located in central Estonia in conjunction with the latest reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF), ERA5, and Nordic Lightning Information System (NORDLIS) lightning location system data are used to investigate the climatology of convective storms for nine summer periods (2010–2019, 2017 excluded). First, an automated 35-dBZ reflectivity threshold-based storm area detection algorithm is used to derive initial individual convective cells from the base level radar reflectivity. Those detected cells are used as a basis combined with convective available potential energy (CAPE) values from ERA5 reanalysis to find thresholds for a severe convective storm in Estonia. A severe convective storm is defined as an area with radar reflectivity at least 51 dBZ and CAPE at least 80 J/kg. Verification of those severe convective storm areas with lightning data reveals a good correlation on various temporal scales from hourly to yearly distributions. The probability of a severe convective storm day in the study area during the summer period is 45%, and the probability of a thunderstorm day is 54%. Jenkinson Collison’ circulation types are calculated from ERA5 reanalysis to find the probability of a severe convective storm depending on the circulation direction and the representativeness of the investigated period by comparing it against 1979–2019. The prevailing airflow direction is from SW and W, whereas the probability of the convective storm to be severe is in the case of SE and S airflow. Finally, the spatial distribution of the severe convective storms shows that the yearly mean number of severe convective days for the 100 km2 grid cell is mostly between 3 and 8 in the distance up to 150 km from radar. Severe convective storms are most frequent in W and SW parts of continental Estonia

Estimation of extreme precipitation events in Estonia and Italy using dual-polarization weather radar quantitative precipitation estimations

Evaluating extreme rainfall for a certain location is commonly considered when designing stormwater management systems. Rain gauge data are widely used to estimate rainfall intensities for a given return period. However, the poor spatial and temporal resolution of operational gauges is the main limiting factor. Several studies have used rainfall estimates based on weather radar horizontal reflectivity (Zh), but they come with a great caveat: while proven reliable for low or moderate rainfall rates, they are subject to major errors in extreme rainfall and convective cases. It is widely known that C-band weather radar can underestimate precipitation intensity due to signal attenuation or overestimate it due to hail and clutter contamination. From the late 1990s, dualpolarization weather radar started to become operational in the national surveillance radar network in Europe, providing innovative quantitative precipitation estimation (QPE) based on polarimetric variables. This study circumvents Zh shortcomings by using specific differential-phase ( Kdp) data from operational dual-polarization C-band weather radars. The rain intensity estimates based on a specific differentialphase data are immune to attenuation and less affected by hail contamination.In this study, for the first time, QPEs based on polarimetric observations by operational C-band weather radars and without any rain gauge adjustments are analyzed. The purpose is to estimate return periods for 1 h rainfall total computed from polarimetric weather radar data using non-adjusted QPEs based on R.Zh;Kdp/ data and to compare the results with those derived using R.Zh / and rain gauge data. Only the warm period during the year is considered here, as most of the extreme precipitation events for such a duration occur for both places studied (Italy and Estonia) at this time. Limiting the dataset to warm periods also allows us to use the radarbased rainfall quantitative precipitation estimations, which are more reliable than the snowfall ones. Data from operational dual polarimetric C-band weather radar sites are used from both Italy and Estonia. Given climatologically homogeneous regions, this study demonstrates that polarimetric weather radar observations can provide reliable QPEs compared to single-polarization estimates with respect to rain gauges and that they can provide a reliable estimation of return periods of 1 h rainfall total, even for relatively short time series.Peer reviewe

Relating Sentinel-1 interferometric coherence to mowing events on grasslands

In this study, the interferometric coherence calculated from 12-day Sentinel-1 image pairs was analysed in relation to mowing events on agricultural grasslands. Results showed that after a mowing event, median VH (vertical transmit, horizontal receive) and VV (vertical transmit, vertical receive) polarisation coherence values were statistically significantly higher than those from before the event. The shorter the time interval after the mowing event and the first interferometric acquisition, the higher the coherence. The coherence tended to stay higher, even 24 to 36 days after a mowing event. Precipitation caused the coherence to decrease, impeding the detection of a mowing event. Given the three analysed acquisition geometries, it was concluded that afternoon acquisitions and steeper incidence angles were more useful in the context of this study. In the case of morning acquisitions, dew might have caused a decrease of coherence for mowed and unmowed grasslands. Additionally, an increase of coherence after a mowing event was not evident during the rapid growth phase, due to the 12-day separation between the interferometric acquisitions. In future studies, six-day pairs utilising Sentinel-1A and 1B acquisitions should be considered

Täppissademed

RITA1/02-52 meteoroloogia alamteema raames töötati välja Eesti oludesse sobiv täppissademete kaardistamise metoodika. Peamiseks andmeallikaks olid Eesti Keskkonnaagentuuri (KAURi) sademejaamade ning Sürgavere radari andmed aastate 2013-2019 (va 2017) kohta. Analüüsiti metoodikat, andmete ning tulemi täpsust ning edasise arenduse võimalusi

Seasonal effects on the estimation of height of boreal and deciduous forests from interferometric TanDEM-X coherence data

The aim of this study is to assess the performance of single-pass X-band bistatic SAR interferometric forest height estimation of boreal and temperate deciduous forests under variable seasonal conditions. For this, twelve acquisitions of single-and dual-polarized TanDEM-X coherence images over 118 forest stands were analyzed and compared against LiDAR forest height maps. Strong correlations were found between interferometric coherence magnitude and LiDAR derived forest stand height for pine forests (r2=0.94) and spruce forest (r2=0.87) as well as for deciduous trees (r2=0.94) during leaf-off conditions with temperatures below 0°C. It was found that coherence magnitude based forest height estimation is influenced by leaf-on and leaf-off conditions as well as daily temperature fluctuations, height of ambiguity and effective baseline. These factors alter the correlation and should be taken into account for accurate coherence-based height retrieval. Despite the influence of the mentioned factors, generally a strong relationship in regression analysis between X-band SAR coherence and LiDAR derived forest stand height can be found. Moreover, a simple semi empirical model, derived from Random Volume over Ground model, is presented. The model takes into account all imaging geometry dependent parameters and allows to derive tree height estimate without a priori knowledge. Our results show that X-band SAR interferometry can be used to estimate forest canopy height for boreal and deciduous forests in both summer and winter, but the conditions should be stable.Peer reviewe

Detecting peat extraction related activity with multi-temporal Sentinel-1 InSAR coherence time series

Monitoring of when, where and in which quantity peat is harvested is currently based on manual declarations. Synthetic Aperture Radar (SAR) is a powerful tool for change detection and monitoring. The aim of this study was to evaluate whether Sentinel-1 6-day interferometric SAR (InSAR) temporal coherence could allow peat extraction monitoring from satellite. We demonstrate that temporal median coherence enables to detect harvest related surface altering works and therefore also spatially explicitly determine active and inactive extraction areas. A polygon-based multi-orbit time series approach is sufficient for the task. Hereby, vertical–vertical polarisation (VV) is more sensitive to the changes compared to vertical-horizontal (VH). During the main harvest season the peat extraction area has median VV coherence lower than 0.2 while the abandoned area and open bog which serve as reference for undisturbed extraction area have close to 0.6. Also, the potential for coherence based milled peat extraction intensity estimation is demonstrated and an indication is given how partially extracted areas could be distinguished from fully harvested and not harvest areas, by the use of coherence standard deviation. Regarding the influence of rainfall, only heavy rain on one of the acquisitions of the image pair whereas the other is from dry conditions seems to cause decorrelation comparable to surface altering works. Moreover, deploying images from multiple consecutive orbits or introducing backscatter intensity σ 0 or reference polygons of undisturbed area helps to reduce risk for rain induced false positives. Developing an operational algorithm for peat extraction identification could be undertaken in future studies.Peer reviewe

Feature database of Estonian agricultural parcels for crop classification (years 2018-2019).

Feature database of Estonian agricultural parcels for crop classification (years 2018-2019). Sentinel-1 and -2 and additional geospatial feature set time series about Estonian agricultural parcels 2018-2019. The time series was used for neural networks model for crop classification purposed, but could in principle be used also with other models and other purposes