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.

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Abstract. Today, 3D models of complex urban buildings are one of the most popular applications of 3D modeling. 3D models of complex urban buildings provide high data interpretation that accurately transfers information about objects or area changes and allows one to solve a number of applied tasks. The quality of the 3D models depends on the quality of the initial images and the method of the object recognition. First of all, the 3D-model building requires identification and classification building borders, which requires determination of the building roof form. The article reviews the existing classification and recognition methods for the 3D further modeling.