The main aspects of the use of satellite data for agricultural lands boundaries determination in the regions of the Russian Federation are discussed in the article. The paper also contains the information about the satellite data and the products based on it, and also proposes possible scenarios for using the results of satellite data processing.

Atmospheric correction of satellite remote sensing data is a prerequisite for a large variety of applications, including time series analysis and quantitative assessment of the Earth’s vegetation cover. It was earlier reported that an atmospherically corrected KMSS-M (Meteor-M #2) dataset was produced for Russia and neighboring countries. The methodology adopted for atmospheric correction was based on localized histogram matching of target KMSS-M and MODIS reference gap-free and date-matching imagery. In this paper, we further advanced the methodology and quantitatively assessed Level-2 surface reflectance analysis-ready datasets, operatively produced for KMSS-2 instruments over continental scales. Quantitative assessment was based on accuracy, precision, and uncertainty (APU) metrics produced for red and near-infrared bands of the KMSS-2 instrument based on a reference derived from a MODIS MOD09 reconstructed surface reflectance. We compared error distributions at 5%, 20%, and 50% levels of cloudiness and indicated that the cloudiness factor has little impact on the robustness of the atmospheric correction regardless of the band. Finally, the spatial and temporal gradients of accuracy metrics were investigated over northern Eurasia and across different seasons. It was found that for the vast majority of observations, accuracy falls within the −0.010–0.035 range, while precision and uncertainty were below 0.06 for any band. With the successful launch of the most recent Meteor-M #2.3 with a new KMSS-2 instrument onboard, the efficiency and interoperability of the constellation are expected to increase.

The creation of bus rapid transit systems requires significant investments in transport infrastructure. It often requires changes in roadway parameters, building boarding platforms, new bus depots, as well as creating a priority passage system at intersections with individual vehicles flows. In world practice, the routing of bus rapid transit (hereinafter—BRT) corridors is often based on the criterion of an opinion of transport experts who assess passenger flows and the location of main attraction points. This article describes an algorithm for building BRT corridors based on the criterion of economically viable passenger flow. The method is based on an iterative algorithm built on the principle of passenger flows redistribution over the transport network in the event of a change in its characteristics. Specifically, what changes is the speed of transit in certain areas due to inclusion of the area in a BRT corridor when the area reaches the threshold of economically viable passenger flow. A threshold value of passenger flow for different cities, grouped by population size, is determined on the basis of passenger flow statistics in globally operated BRT systems. The condition for exiting the iterative algorithm can be either the absence of new network areas where an economically viable passenger flow is achieved, or the achievement of 90% of the labor commuting share in the city during the time specified in the urban planning standard. This method can be used to identify new and extend existing BRT corridors in cities with populations from 100 to 2000 thousand people.

Building a more resilient food system for sustainable development and reducing uncertainty in global food markets both require concurrent and near-real-time and reliable crop information for decision making. Satellite-driven crop monitoring has become a main method to derive crop information at local, regional, and global scales by revealing the spatial and temporal dimensions of crop growth status and production. However, there is a lack of quantitative, objective, and robust methods to ensure the reliability of crop information, which reduces the applicability of crop monitoring and leads to uncertain and undesirable consequences. In this paper, we review recent progress in crop monitoring and identify the challenges and opportunities in future efforts. We find that satellite-derived metrics do not fully capture determinants of crop production and do not quantitatively interpret crop growth status; the latter can be advanced by integrating effective satellite-derived metrics and new onboard sensors. We have identified that ground data accessibility and the negative effects of knowledge-based analyses are two essential issues in crop monitoring that reduce the applicability of crop monitoring for decisions on food security. Crowdsourcing is one solution to overcome the restrictions of ground-truth data accessibility. We argue that user participation in the complete process of crop monitoring could improve the reliability of crop information. Encouraging users to obtain crop information from multiple sources could prevent unconscious biases. Finally, there is a need to avoid conflicts of interest in publishing publicly available crop information.

Проведён анализ пригодности использования данных различного пространственного разрешения при мониторинге объектов на основе осреднённых в их границах характеристик.Для этого изучалось влияние пространственного разрешения данных дистанционного зондирования Земли (ДЗЗ) на средние значения вегетационного индекса NDVI (англ.Normalized Difference Vegetation Index) в границах сельскохозяйственных полей в зависимости от их площади.Использовались три набора данных, полученных на основе информации с приборов MODIS (англ.Moderate Resolution Imaging Spectroradiometer), КМСС (Комплекс многозональной спутниковой съёмки) и MSI (англ.MultiSpectral Instrument) с пространственным разрешением 250, 60 и 10 м/пиксель соответственно.Для каждого набора использован временной ряд восстановленных ежедневных безоблачных изображений.Взята выборка сельскохозяйственных полей в различных регионах России.Для каждого поля рассчитано среднее значение индекса NDVI по каждому набору данных и проведён корреляционный анализ значений, полученных по разным наборам.Результаты анализа при учёте всего сезона вегетации показали общую высокую согласованность: даже для полей с размером менее 10 га значение коэффициента корреляции Пирсона превысило 0,75 для пар КМСС -MSI и MODIS -MSI и 0,85 для пары MODIS -КМСС.Однако коэффициент корреляции Пирсона существенно падает при анализе данных в отдельные периоды года: в начале сезона в паре MODIS -MSI для полей площадью менее 2,5 га -до 0,45, для полей площадью от 2,5 до 5 га -до 0,58.Далее минимальное за период значение равномерно растёт с увеличением полей и при площадях свыше 15 га уже для всех недель составляет более 0,82.Таким образом, сделан вывод о том, что для полей площадью более 10-15 га ход среднего индекса NDVI имеет схожие тренды по данным с пространственным разрешением 250, 60 и 10 м/пиксель.Для полей меньшего размера присутствуют большие различия в период наименьших значений индекса NDVI, соответствующих распашке.Точный критерий пригодности данных c низким пространственным разрешением для анализа объектов определённой площади зависит от задачи и используемого периода года.

Представлены результаты анализа состояния озимых сельскохозяйственных культур в субъектах Южного, Северо-Кавказского, Центрального (ЦФО) и Приволжского (ПФО) федеральных округов весной 2023 г.Приводится обзор агрометеорологических условий на рассматриваемой территории для оценки факторов, оказавших влияние на состояние посевов.Сложившиеся метеорологические условия осени 2022 г. в ряде регионов негативно повлияли на ход посевной кампании: так, в пяти субъектах ЦФО по данным спутниковых наблюдений на конец 2022 г. зафиксирован недосев озимых по сравнению с концом 2021 г.В ряде регионов ЦФО и ПФО неблагоприятное воздействие на состояние культур оказали условия перезимовки: сильные перепады температуры и недостаточный снежный покров повлекли за собой ухудшение состояния посевов вплоть до их гибели.В целом анализ спутниковых данных на вторую -начало третьей декады апреля 2023 г. показал, что благоприятная ситуация с озимыми наблюдается в Южном и Северо-Кавказском федеральных округах

Современные проблемы ДЗЗ из космоса, 20(

This study elaborates the perspectives of using multi-season satellite information to improve of satellite mapping of winter crops over large scales using machine learning were investigated. The reconstructed seasonal time series of daily NDVI obtained using MODIS (Terra and Aqua) in the period from 2015 to 2019 were used as mapping features. To train the model, we used winter crops seasonal maps being built in Space Research Institute of the Russian Academy of Sciences and updated on a regular basis. The multi-season model was created using satellite data and winter crops maps for five consecutive years, Random Forest and XGBoost classifiers, as well as approaches for optimization and analysis of big data. The winter crop maps derived with created multi-season model were used to calculate areas under winter crops at oblast level of Russian Federation in order to compare them with state statistics data (ROSSTAT). It was found that multi-season-trained machine learning models provide a systematic improvement winter crops mapping compared to the original seasonal maps based solely on intra-seasonal phenological information.

The study describes the LOWESS methodology based on locally weighted moving-window regression and its use for the uniform reconstruction of harmonized gap-free seasonal and multiannual time series of daily surface reflectance for vegetation cover of Russia on the example of four satellite systems: VIIRS (NPP), Terra\Aqua (MODIS), Sentinel-2A\B (MSI), Meteor-M-2\2.2 (KMSS)

Представлены результаты анализа состояния озимых и яровых зерновых культур в России в 2022 г. совместно со статистическими данными об урожайности этих групп культур за предыдущие годы.Анализ базируется на картах порайонных отклонений максимальных значений нормализованного разностного вегетационного индекса NDVI (англ.Normalized Difference Vegetation Index) озимых и яровых культур от среднемноголетних максимумов и максимумов отдельных лет, формируемых в системе спутникового мониторинга «Вега».Показано, что во всех федеральных округах -лидерах по производству озимых зерновых потенциальная урожайность этой группы культур в 2022 г. по спутниковым данным оценивается на уровне рекордной.По яровым зерновым культурам получена более разнородная картина: в одних федеральных округах (например, в Центральном

The paper presents the results of remote assessment of the state of crops in the regions of Russia in first ten days of July 2022.It is noted that in most regions of the European territory of Russia, the maximum values of the NDVI vegetation index of winter crops have passed, and the NDVI values in many of them exceeded the average long-term maximums.From the analysis of remote sensing data, the values of the forecasted winter wheat yield are higher than the long-term average in most regions.Meteorological conditions in spring 2022 caused spring crops development to lag behind the long-term average in a significant number of regions of the European Russia.The maximum values of NDVI for spring crops in the European and Asian Russia have not been reached yet.However, as of the first decade of July, in a number of regions (mainly in the south of the Central and Volga regions and in the north of the Southern Federal District), NDVI exceeded the average long-term maximums indicating a potentially higher productivity of spring crops in these regions than on average in recent years.If weather conditions remain favorable, spring crop yields exceeding the average annual values may be obtained in these regions.

В августе 2021 г. в Российской Федерации проведена сельскохозяйственная микроперепись.В целях перехода на новый уровень верификации данных об использовании сельскохозяй ственных угодий (пашни, залежи, сенокосов и пастбищ), полученных в ходе проведения мик ропереписи 2021 г., была разработана Технология контроля данных сельскохозяйственной микропереписи с использованием средств спутникового мониторинга (далее -Технология контроля

The integration of remote sensing (RS) technology with machine learning (ML) algorithms can facilitate accurate prediction of sugarcane yield.This paper presents an assessment of the random forest (RF)-based prediction model and second-degree polynomial regression models for sugarcane yield prediction.The models are developed utilizing vegetation indices (VIs) computed from the Sentinel-2 satellite and sugarcane yield data.The sugarcane yield data were acquired from sugarcane fields around the Godavari Bio-refineries Limited (GBL) factory in Karnataka, India, during the 2017-2018 sugarcane growing season.A dataset detailing agronomic information and VIs was prepared for yield prediction.The study area comprises seven sugarcane growing talukas.The second-degree polynomial regression was used for predicting the sugarcane yield as it had the best fit for the distribution of variables.The green normalized difference vegetation index (GNDVI) recorded the highest R2, i.e., 0.71 during November month with a coefficient of variance of 0.83, with all other indices characterized by R2 values ranging from 0.42(modified chlorophyll absorption ratio index) to 0.69 (normalized difference red edge), suggesting the GNDVI's potential for sugarcane yield prediction. Comparing the actual yield with the predicted yield, the RF prediction and second-degree polynomial regression model exhibited accuracies of 90.42% and 88%, respectively.This indicates that the models are sufficiently accurate and beneficial in decision-making for sugar mill operational planning.