In recent years, free access to high and medium resolution data has become available, providing researchers with the opportunity to work with low resolution satellite images on a global scale. Sentinel-1 and Sentinel-2 are popular sources of information due to their high spectral and spatial resolution. To obtain a final product with a resolution of 10 meters, we have to use bands with a resolution of 10 meters. Other satellite data with lower resolution, such as Landsat-8 and Landsat-9, can improve the results of land monitoring, but their harmonization requires a process known as super-resolution. In this study, we propose a method for improving the resolution of low-resolution images using advanced deep learning techniques called Generative Adversarial Networks (GANs). The state-of-the-art neural networks, namely transformers, with the combination of channel attention and self-attention blocks were employed at the base of the GANs. Our experiments showed that this approach can effectively increase the resolution of Landsat satellite images and could be used for creating high resolution products.

Climate velocity estimation is an important task for climate change study. At the same time, analysis of biological response to climate change requires climate-analog approach for climate velocity estimation. This approach uses a nearest neighborhood technique for the climate cells matching between climate data with temporal distance. Despite benefits in the quality, usability and accuracy this approach has serious limitations related to the distance measurements, climate analog search, and parameters of algorithm that necessary to tune for each region, scale, spatial resolution and type of climate data. These limitations are making this algorithm difficult to use, less reliable and not usable on the global scale. In this paper we introducing the new method for climate-analog velocity estimation based on the optical flow iterative Lucas-Kanade (iLK) approach. This algorithm is using phase-correlation as a matching cost function and allows to avoid tuning of dissemination threshold parameters and provide robust and accurate result on any scale or resolution.

In this study, the assessment of sustainable development goal (SDG) indicator 2.4.1 for Ukraine and Germany is conducted using geospatial and satellite data. The traditional methodology for the SDG indicator 2.4.1 calculation cannot be directly applied to the Ukrainian territory due to the lack of systematic data collection of the essential indicators. Therefore, the authors have developed an integrated approach to estimate land degradation, that uses different schemes for various land cover and crop types at the national scale, utilizing satellite data and employing the WOFOST model for crop growing simulation. The research describes the information sources used for creation crop type classification maps and the necessary data for modeling leaf area index (LAI) based on the WOFOST model. The calculated indicators are determined for each Ukrainian region from 2018 to 2022. Observations in 2022 show a decline in the indicator 2.4.1 across nearly all regions of Ukraine, directly attributed to the military conflicts within the Ukraine. To assess the possibility of applying the developed technology to a large area, the indicator was calculated for a European country (Germany).

1stJuly 2021 the law on the creation of land market start effect in Ukraine. As a result, land appraisal became cornerstone task in Ukrainian agriculture sector. The official methodology on land appraisal includes use of soil fertility characteristics combined with coefficients related to the distance to the infrastructure objects or settlements and placing of field in specific functional areas, like recreational, or areas with high level of radiation pollution. In this study we collected open source infostructure geospatial information and characteristics of fields obtained from remote sensing data - crop types and Normalized Difference Vegetation Index to build land price predictive model trained on the official land market information. This work designed to investigate potential of geo-informational technologies and remote sensing in the land appraisal use. We separated all available ground truth land price data into three groups by fields size - very small, small, medium and big. We found different relationships between field characteristics and prices. For very small fields the most important features are area, altitude, slope, bonitet and distances to elevators, villages and roads. For small fields the most important are bonitet, altitude, area and distances to cities and roads. For medium and big field's area, slope, distance to cities, roads and historical NDVI.

In the past few years, medium and high-resolution data became freely available for downloading. It provides great opportunity for researchers not to select between solving the task with high-resolution data on small territory or on global scale, but with low-resolution satellite images. Due to high spectral and spatial resolution of the data, Sentinel-1 and Sentinel-2 are very popular sources of information. Nevertheless, in practice if we would like to receive final product in 10 m resolution we should use bands with 10 m resolution. Sentinel-2 has four such bands, but also has other bands, especially red-edge 20 m resolution bands that are useful for vegetation analysis and often are omitted due to lower resolution. Thus, in this study we propose methodology for enhancing resolution (super-resolution) of the existing low-resolution images to higher resolution images. The main idea is to use advanced methods of deep learning - Generative Adversarial Networks (GAN) and train it to increase the resolution for the satellite images. Experimental results for the Sentinel-2 data showed that this approach is efficient and could be used for creating high resolution products.

According to the methodology for determining land degradation adopted by the UN for the calculation of the sustainable development goal's (SDG) indicator 15.3.1, land productivity on the basis of remote sensing data is one of the three sub-indicators. At the same time, the process of land degradation is very complex and it has not yet been studied how it is affected by climate changes. This task is complicated by the fact that climate change has consequences in the future. However, satellite data have a long history of observations and therefore we can see, how climate indicators affect the process of land degradation in historical terms. In this paper, we used MODIS satellite data to calculate land productivity and estimated the relationship between land productivity and climate change vulnerability of agricultural water resources (CCV) obtained by SWAT model for Ukraine. Correlation and regression analysis show that the climate change vulnerability of agricultural water resources is one of the indicators of land degradation.

One of the parts of the Land Monitoring Service is Copernicus Urban Atlas, which provides reliable and comparable land use maps with high accuracy for large number European functional urban areas and their neighbors for every 6 years (2006, 2012, 2018). Unfortunately, there is no available such products for Ukrainian cities and there is no possibility to reproduce the technology by which they are obtained. This is due to the unavailability of sufficient high resolution satellite data information at the cities level, which is an integral part of the European methodology for obtaining the Urban Atlas. That is why we have proposed new approach on the base of open data which can be applicable to any other city. Kyiv (Ukraine) became the first city outside the Europe, for which the methodology by creating Urban Atlas was developed, which is compliant in structure and functionality to the European Copernicus Urban Atlas. The methodology was scaled for Lviv City, as well as applied and tested for other cities, in particular for Rivne, Irpin (Ukraine) and Lublin (Poland). In addition to the main management tasks that the Urban Atlas helps to solve, the obtained products can be used to unify and air quality monitoring in cities, and as a base for assessing the sustainable development goals indicator 11.6.2 “Annual mean levels of fine particulate matter in cities”.

Illegal logging in Ukraine is a big problem that negatively affects both environmental and socio-economic indicators of the country. The main reason for this problem is the lack of independent control over the forest industry. Lack of control, in turn, makes it possible to provide inaccurate information about the permitted logging and to hide the fact of logging. The solution to this problem is the use of modern approaches of Remote Sensing and deep learning to implement mechanisms for forestry monitoring and logging detection based on the satellite data. Most researches on satellite-based logging detection technology are based on the optical satellite missions. However, for countries with temperate and cold climates, the use of such approaches is problematic in winter and autumn due to the lack of vegetative biomass and the high percentage of clouds and snow in satellite images. In this study, we assessed a methodology for detecting logging based on optical and radar images of Copernicus satellite missions, namely Sentinel-l and 2. The obtained results show that when using this approach, it is possible to monitor and detect logging with high accuracy both in summer and in winter with the frequency of data updates once a week. The basis of this methodology is a convolutional neural network with U -Net architecture, which input is a stack of optical and radar images in summer and spring, and works on radar images only in winter and autumn.

In the past few years, deep learning methods developed and progressed in the many applied fields of science due to the appearance of large amounts of freely available data and improvement of computing resources. The task of crop mapping based on satellite data is not an exclusion. However, the appearance of clouds and shadows on the optical images cause difficulties on applying typical deep learning methods that recommend themselves in other issues. The easiest solution was to consider only images with a small percentage of clouds, but such way decreased the available data informativeness. In this study, we propose the new deep learning method based on recurrent neural network for efficient and precise crop mapping based on Sentinel-1 and Sentinel-2 imagery. The main idea of the study is to utilize all available information from the satellites and to provide an opportunity for neural network to extract the necessary features without any expert knowledge. Taking into account that regular feedforward neural networks could not deal effectively with such issue, authors suggest recurrent neural network with long short-term memory cells. Experimental results for the part of Kyiv region showed that this approach is rather efficient and outperformed traditional machine learning approaches and deep U-net architecture in terms of overall accuracy.

Built-up area mapping is essential task for Sustainable Development Goals (SDGs) indicators related to sustainable cities and communities. The calculation of indicator 11.3.1: “Ratio of land consumption rate to population growth rate” can be used by governments and decision makers for efficient smart city growth monitoring and planning. In this work, the built-up area map based on local data were built by using land cover classification approach for smart cities, developed in Horizon-2020 ERA-Planet SMURBS project. These maps were validated and compared with use of Global Human Settlement Layer, ground truth data and Maryland Forest product. The results shows that global products, such as Global Human Settlement Layer are very useful and have good accuracy especially in the case for global indicator 11.3.1 assessment. It can show the full picture of global urbanization changes. But, it is better to use local data for city scale, to provide accurate tracking of urban area development. Local data in this case could be more informative for the decision makers in purpose of the city growth management and proper use of environmental resources.

This paper presents fire monitoring studies based on heterogeneous satellite data. In particular, this paper describes fire monitoring service developed by the Space Research Institute of NASU and SSAU for Ukraine and other existing fire monitoring systems and services. We consider different data sources for automatic fire detection at the national level in Ukraine. For this research were used data acquired by MODIS, Landsat-8, Sentinel-2 and Sentinel-3. The fire detection methodology for the first three satellites is implemented in Google Earth Engine. Workflow for automatic fire detection using Sentinel-3 data and fire detection system for Ukraine is developed within the Horizon-2020 ERA-Planet SMURBS project in Amazon cloud platform.

For accurate crop classification, it is necessary to use time-series of high-resolution satellite data to better discriminate among certain crop types. This task brings the following challenges: a large amount of satellite data for download, Big data processing and computational resources for utilization of state-of-the-art classification approaches. For solving these problems, we have developed an automated crop classification workflow, which is based on machine-learning techniques. By deployment of the workflow on the cloud platform, we can overcome challenges of Big data downloading and processing. In this paper, we present the system architecture and describe the experiments on structural and parametric identification of machine learning models utilized in the system.

For evaluating how far we are from achieving the Sustainable Development Goals and how big the progress is a global indicator framework was developed by the Inter-Agency and Expert Group on Sustainable Development Goals Indicators (IAEG-SDGs). In this paper, we propose an improved methodology for calculating indicator 2.4.1 "Proportion of agricultural area under productive and sustainable agriculture" within the ERA-PLANET Horizon 2020 project "The European Network for Observing our Changing Planet". The main improvements are in using accurate 10 m resolution crop classification map and Leaf Area Index derived from satellite data and WOFOST biophysical model. Based on proposed methodology indicator 2.4.1 has been calculated for the territory of Ukraine.

In this paper we propose method for land degradation indicators identification within the ERA-PLANET project. We also consider data sources that can be used for these purposes and consider the possibility of using high spatial resolution satellite-based classification maps. Project GEOEssential within the framework of the ERA-PLANET Project, using heterogeneous satellite data has the potential to make positive changes in land use. In this paper the ways of forming essential variables are described with specific attention to domain where and how it is possible to improve the state of productivity of land and to identify the non-proper use of land, which leads to desertification, forest degradation and the general degradation of land resources.