Algorithm for determining crop harvesting dates based on time series of coherence and backscattering coefficient (σ 0 ) derived from Sentinel-1 single look complex (SLC) synthetic-aperture radar (SAR) images is proposed. The algorithm allows the ability to monitor harvesting over large areas without having to install additional sensors on agricultural machinery. Coherence between SAR images allows the ability to track changes in field-scatterers configuration resulting from agricultural work. The proposed algorithm finds a step-like increase in coherence that occurs after the harvesting and is related to the conversion of a field into a bare soil area. An additional check of potential harvest dates is carried out by threshold values of σ 0 depending on vegetation height. The algorithm is adapted for the monitoring of non-homogeneous fields with traces of erosion and insertions of fallow land. The algorithm was tested on agricultural fields located in the north of Kazakhstan. The obtained accuracy (mean absolute error = 6.5 days) of determining the dates of harvesting can be deemed satisfactory. This accuracy can be increased by shortening the interval between observations from 12 to 6 days when using data from both Sentinel-1 satellites.

The algorithms for determining sugarcane harvest dates are proposed; the algorithms allow the ability to monitor large areas and are based on the publicly available Synthetic Aperture Radar (SAR) and optical satellite data. Algorithm 1 uses the NDVI (Normalized Difference Vegetation Index) time series derived from Sentinel-2 data. Sharp and continuous decrease in the NDVI values is the main sign of sugarcane harvest. The NDVI time series allows the ability to determine most harvest dates. The best estimates of the sugarcane areas harvested per month have been obtained from March to August 2018 when cloudy pixel percentage is less than 45% of the image area. Algorithm 2 of the harvest monitoring uses the coherence time series derived from Sentinel-1 Single Look Complex (SLC) images and optical satellite data. Low coherence, demonstrating sharp growth upon the harvest completion, corresponds to the harvest period. The NDVI time series trends were used to refine the algorithm. It is supposed that the descending NDVI trend corresponds to harvest. The algorithms were used to identify the harvest dates and calculate the harvested areas of the reference sample of 574 sugarcane parcels with a total area of 3745 ha in the state of São Paulo, Brazil. The harvested areas identified by visual interpretation coincide with the optical-data algorithm (algorithm 1) by 97%; the coincidence with the algorithm based on SAR and optical data (algorithm 2) is 90%. The main practical applications of the algorithms are harvest monitoring and identification of the harvested fields to estimate the harvested area.

Water resources are an important component of ecosystem services. During long periods of cloudiness and precipitation, when a ground-based sample is not available, the water bodies are detected from satellite SAR (synthetic-aperture radar) data using threshold methods (e.g., Otsu and Kittler–Illingworth). However, such methods do not enable to obtain the correct threshold value for the backscattering coefficient (σ0) of relatively small water areas in the image. The paper proposes and substantiates a method for the mapping of the surface of water bodies, which makes it possible to correctly identify water bodies, even in “water”/“land” class imbalance situations. The method operates on a principle of maximum compliance of the resulting SAR water mask with a given reference water mask. Therefore, the method enables the exploration of the possibilities of searching and choosing the optimal parameters (polarization and speckle filtering), which provide the maximum quality of SAR water mask. The method was applied for mapping natural and industrial water bodies in the Pohjois-Pohjanmaa region (North Ostrobothnia), Finland, using Sentinel-1A and -1B ground range detected (GRD) data (ascending and descending orbits) in 2018–2021. Reference water masks were generated based on optical spectral indices derived from Sentinel-2A and -2B data. The polarization and speckle filtering parameters were chosen since they provide the most accurate σ0 threshold (on average for all observations above 0.9 according to the Intersection over Union criterion) and are resistant to random fluctuations. If a reference water mask is available, the proposed method is more accurate than the Otsu method. Without a reference mask, the σ0 threshold is calculated as an average of thresholds obtained from previous observations. In this case, the proposed method is as good in accuracy as the Otsu method. It is shown that the proposed method enables the identification of surface water bodies under significant class imbalance conditions, such as when the water surface covers only a fraction of a percent of the area under study.

Purpose. Solving the tasks of subsurface resources management based on the created GIS RAPID geoinformation technology.Methods. Close spatial relationships of lineament network characteristics and earthquake epicenters were detected in 3 seismically active areas located in the mountainous regions of Central Europe. Digital elevation models (DEM) based on ASTER satellite surveys and earthquake epicenter data were used. The nature of spatial relationship of lineament network and vein ore objects was studied in the territory of Congo DR, in the Lake Kivu area using space imagery. Gold ore objects were searched and forecasted in Uzbekistan in the site of Jamansai Mountains. Highresolution imagery from QuickBird 2 satellite, geophysical field surveys, geological and geochemical data were used.Findings. It was found that a significant number of epicenters are located in areas of high concentration of "nonstandard" azimuths lineaments -from 27 to 34% of the total number of lineaments. It was revealed that 59.6% of the epicenters are located within 10% of sites with the highest values of complex deformation maps; 50% of the areas with the highest values of these maps contain, on average, 89% of all earthquake epicenters. It was found that satellite image lineament concentration maps with "non-standard" azimuths reflect the spatial relationship with known deposits much better than the concentration map of all lineaments. It was detected that the total area of gold ore objects perspective sites is about 20 km 2 .Originality. The use of GIS RAPID in a number of earth's crust areas has allowed to establish new regularities linking the networks of physical field and landscape lineament characteristics with ore bodies and earthquake epicenters localization. Practical implications.A new technology has been developed for solving geological forecasting and prospecting problems. The technology can be used to solve a wide range of practical problems, especially in difficult geological conditions when searching for deep objects weakly presented in external fields and landscape.

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<p>Monitoring and mapping open-pit mining activity is essential to identify operation sites and unaffected surfaces of mining areas. Vertical displacements of the earth's surface associated with open pit mining can be detected using high spatial resolution Digital Surface Model (DSM) data or based on all-weather Synthetic Aperture Radar (SAR) Single Look Complex (SLC) satellite images using Differential Interferometry Synthetic Aperture Radar (DInSAR) technique. In some cases, activity in an open pit may not be accompanied by changes in terrain heights but cause violations of land cover integrity accompanied by earth's surface texture changes (for example, deforestation or recultivation, violation of quarries and dump slope integrity, changes in surface conditions, hydrological disturbances, etc.) and can be detected using coherence maps generated from SAR SLC data.</p> <p>Coherence is the modulus of the complex correlation coefficient between two SLC images containing information about the amplitude and phase of the radar signal. If there is no surface change between the two survey dates, the coherence values are close to 1. Mining activities change the surface texture, so the coherence decreases to values close to 0. The frequency approach estimates the total changes in coherence over the season. For example, the Temporal Activity Index (TAI) is a relative coherence frequency below a given threshold across the time series of SAR images. In the case of monitoring open pit mining, activity areas with consistently low coherence over a time series of observations are of primary interest.</p> <p>The study area is an open-pit mining area of the Pyhäsalmi Mine located in the Pohjois-Pohjanmaa region, Finland. It includes an old open pit, a backfill open pit, and several waste dumps [1]. Time series of Sentinel-1 SLC Interferometric Wide (IW) images were used to detect active areas in operation for the study area. Images were collected every 12 days from May to  September 2020-2022 and provided by the GOLDEN-AI platform [2].</p> <p>For each observation year, a time series of Sentinel-1 SLC coherence was generated for the Pyhäsalmi mine. Active areas in operation were identified for open pits and waste dumps based on TAI maps (Fig. 1), providing information about the intensity of surface changes during the observation periods.</p> <p><img src="" alt="" width="436" height="338" /></p> <p>Figure 1. Temporal Activity Index maps for the Pyhäsalmi Mine area.</p> <p>Funding. This work was funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 869398 “Earth observation and Earth GNSS data acquisition and processing platform for safe, sustainable and cost-efficient mining operations” (Goldeneye).</p> <p>Acknowledgments. The authors gratefully acknowledge Maria Hänninen, Environmental Manager at Pyhäsalmi Mine Oy for specification locations for measurements and study planning, and the OPT/NET BV company (opt-net.eu) and GOLDEN-AI platform for supplying Sentinel-1 data. The authors would like to thank the European Commission, the European Space Agency, and the Copernicus Program for providing Sentinel-1 data.</p> <p>References:</p> <p>[1] Siikanen, S., Savolainen, M., Karinen, A., Puputti, J., Kauppinen, T., Uusitalo, S., & Paavola, M., 2022. Drone-based near-infrared multispectral and hyperspectral imaging in monitoring structural changes in mine tailing ponds. Thermal Infrared Applications XLIV, Vol. 12109, pp. 58-64). https://doi.org/10.1117/12.2618294</p> <p>[2] Havisto, J., Matselyukh, T., Paavola, M., Uusitalo, S., Savolainen, M., González, A. S., Knobloch, A. & Bogdanov, K., 2021. Golden AI Data Acquisition and Processing Platform for Safe, Sustainable and Cost-Efficient Mining Operations. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, pp. 5775-5778. https://ieeexplore.ieee.org/document/9554181</p> <p> </p>