Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm

Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable information on seasonal cultivated as well as seasonal fallow cropland extents and areas based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Seasonal ACCA development process involves writing series of iterative decision tree codes to separate cultivated and fallow croplands from noncroplands, aiming to accurately mirror reliable reference data sources. A pixel-by-pixel accuracy assessment when compared with the U.S. Department of Agriculture (USDA) cropland data showed, on average, a producer's accuracy of 93% and a user's accuracy of 85% across all months. Further, ACCA-derived cropland maps agreed well with the USDA Farm Service Agency crop acreage-reported data for both cultivated and fallow croplands with R-square values over 0.7 and field surveys with an accuracy of >= 95% for cultivated croplands and >= 76% for fallow croplands. Our results demonstrated the ability of ACCA to generate cropland products, such as cultivated and fallow cropland extents and areas, accurately, automatically, and repeatedly throughout the growing season

USING THE LANDSAT SATELLITE SYSTEM FOR WINTER WHEAT MONITORING IN THE WESTERN UKRAINE TERRITORY

Сегодня существует множество способов мониторинга посевов на протяжении сезона. Среди них – применение спутников и дронов, листовая диагностика, анализ проб почвы. В данной статье рассмотрены технологии использования данных ДЗЗ Landsat для решения задач мониторинга состояния озимой пшеницы в условиях Западной Украины. Также рассмотрены вопросы прогнозирования урожайности. Исследования прово-дились на территории агрохозяйства им. Воловикова с использованием снимков системы дистанционного зондирования Landsat за 2010–2011 гг. (август, апрель, июль). Описана технология создания геоинформационных моделей основных индексов, а именно: моделей распределения индекса NDVI – и моделей распределения индекса GVI (Green Vegetation Index). Этимодели выступали показателями вегетации зеленых растений. На их основании проводилась количественная оценка восхождения и роста озимой пшеницы. Также в этом исследовании проводилась интеграция цифровыхмоделей рельефа и материалов дистанционного зондирования Landsat, используя инструментарий картографической алгебры были определены проблемные участки, на которых наблюдается снижение урожайности озимой пшеницы. Установлены причины такого состояния, и приняты решения о корректировании структуры севооборота (на будущее), а также определены координаты участков, которые нуждаются в особом агрономическом обеспечении с дальнейшим внесением этой геопространственной информации в систему точного земледелия. Проведенные исследования показывают, что после геоинформационногоанализа картографических материалов агрохимических показателей, рельефа, данных дистанционного зондирования за разные периоды вегетации растений, возможно получать актуальную информационную модель исследуемой территории и эффективно внедрять землеустроительные и агротехнические мероприятия, а именно: корректировать структуру севооборотов, вносить удобрения, обнаруживать участки пораженные вредителями и болезнями, обрабатывать точно установленные проблемные участки, а также принимать обоснованные управленческие решения.Сегодня существует множество способов мониторинга посевов на протяжении сезона. Среди них – применение спутников и дронов, листовая диагностика, анализ проб почвы. В данной статье рассмотрены технологии использования данных ДЗЗ Landsat для решения задач мониторинга состояния озимой пшеницы в условиях Западной Украины. Также рассмотрены вопросы прогнозирования урожайности. Исследования прово-дились на территории агрохозяйства им. Воловикова с использованием снимков системы дистанционного зондирования Landsat за 2010–2011 гг. (август, апрель, июль). Описана технология создания геоинформационных моделей основных индексов, а именно: моделей распределения индекса NDVI – и моделей распределения индекса GVI (Green Vegetation Index). Этимодели выступали показателями вегетации зеленых растений. На их основании проводилась количественная оценка восхождения и роста озимой пшеницы. Также в этом исследовании проводилась интеграция цифровыхмоделей рельефа и материалов дистанционного зондирования Landsat, используя инструментарий картографической алгебры были определены проблемные участки, на которых наблюдается снижение урожайности озимой пшеницы. Установлены причины такого состояния, и приняты решения о корректировании структуры севооборота (на будущее), а также определены координаты участков, которые нуждаются в особом агрономическом обеспечении с дальнейшим внесением этой геопространственной информации в систему точного земледелия. Проведенные исследования показывают, что после геоинформационногоанализа картографических материалов агрохимических показателей, рельефа, данных дистанционного зондирования за разные периоды вегетации растений, возможно получать актуальную информационную модель исследуемой территории и эффективно внедрять землеустроительные и агротехнические мероприятия, а именно: корректировать структуру севооборотов, вносить удобрения, обнаруживать участки пораженные вредителями и болезнями, обрабатывать точно установленные проблемные участки, а также принимать обоснованные управленческие решения.Today, there are many ways to monitor crops throughout the season. Among them – the use of satellites and drones, sheet diagnostics, analysis of soil samples. This article discusses the technology of using Landsat remote sensing data for solving problems of monitoring the state of winter wheat in WesternUkraine. The issues of yield forecasting were also considered. The studies were conducted on one of the fields of the agro-farm. Volovikov using Landsat remote sensing imagery for 2010-2011. (August, April, July). The technology of creating geo-information models of the main indices is described, namely: the distribution models of the NDVI index – and the distribution models of the GVIindex (Green Vegetation Index). These models were indicators of the vegetation of green plants. On their basis, a quantitative assessment of the ascent and growth of winter wheat was carried out. Also in this study, the integration of digital elevation models and Landsat remote sensing materials was carried out, using the tools of cartographic algebra, problem areas were identified in which adecrease in the yield of winter wheat was observed. The reasons for this state were established, and decisions were made to correct the structure of the crop rotation (for the future), and the coordinates of sites that need special agronomic support with further integration of this geospatial information into the system of precision farming were determined. Studies show that aftergeoinformational analysis of cartographic materials of agrochemical indicators, topography, remote sensing data for different periods of plant vegetation, it is possible to obtain an upto-date informational picture of the state of the study area and effectively implement land management and agrotechnical measures, namely: correct the structure of crop rotation, fertilize, detect areas affected by pests and diseases, process well-defined problems areas, and to make informed management decisions

A cultivated planet in 2010 – Part 1: The global synergy cropland map

Information on global cropland distribution and agricultural production is critical for the world's agricultural monitoring and food security. We present datasets of cropland extent and agricultural production in a two-paper series of a cultivated planet in 2010. In the first part, we propose a new Self-adapting Statistics Allocation Model (SASAM) to develop the global map of cropland distribution. SASAM is based on the fusion of multiple existing cropland maps and multilevel statistics of the cropland area, which is independent of training samples. First, cropland area statistics are used to rank the input cropland maps, and then a scoring table is built to indicate the agreement among the input datasets. Secondly, statistics are allocated adaptively to the pixels with higher agreement scores until the cumulative cropland area is close to the statistics. The multilevel allocation results are then integrated to obtain the extent of cropland. We applied SASAM to produce a global cropland synergy map with a 500 m spatial resolution for circa 2010. The accuracy assessments show that the synergy map has higher accuracy than the input datasets and better consistency with the cropland statistics. The synergy cropland map is available via an open-data repository (https://doi.org/10.7910/DVN/ZWSFAA; Lu et al., 2020). This new cropland map has been used as an essential input to the Spatial Production Allocation Model (SPAM) for producing the global dataset of agricultural production for circa 2010, which is described in the second part of the two-paper series

Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity

Accurate cropland information is of paramount importance for crop monitoring. This study compares five existing cropland mapping methodologies over five contrasting Joint Experiment for Crop Assessment and Monitoring (JECAM) sites of medium to large average field size using the time series of 7-day 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) mean composites (red and near-infrared channels). Different strategies were devised to assess the accuracy of the classification methods: confusion matrices and derived accuracy indicators with and without equalizing class proportions, assessing the pairwise difference error rates and accounting for the spatial resolution bias. The robustness of the accuracy with respect to a reduction of the quantity of calibration data available was also assessed by a bootstrap approach in which the amount of training data was systematically reduced. Methods reached overall accuracies ranging from 85% to 95%, which demonstrates the ability of 250 m imagery to resolve fields down to 20 ha. Despite significantly different error rates, the site effect was found to persistently dominate the method effect. This was confirmed even after removing the share of the classification due to the spatial resolution of the satellite data (from 10% to 30%). This underlines the effect of other agrosystems characteristics such as cloudiness, crop diversity, and calendar on the ability to perform accurately. All methods have potential for large area cropland mapping as they provided accurate results with 20% of the calibration data, e.g. 2% of the study area in Ukraine. To better address the global cropland diversity, results advocate movement towards a set of cropland classification methods that could be applied regionally according to their respective performance in specific landscapes.Instituto de Clima y AguaFil: Waldner, François. Université catholique de Louvain. Earth and Life Institute - Environment, Croix du Sud; BelgicaFil: De Abelleyra, Diego. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Veron, Santiago Ramón. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; ArgentinaFil: Zhang, Miao. Chinese Academy of Science. Institute of Remote Sensing and Digital Earth; ChinaFil: Wu, Bingfang. Chinese Academy of Science. Institute of Remote Sensing and Digital Earth; ChinaFil: Plotnikov, Dmitry. Russian Academy of Sciences. Space Research Institute. Terrestrial Ecosystems Monitoring Laboratory; RusiaFil: Bartalev, Sergey. Russian Academy of Sciences. Space Research Institute. Terrestrial Ecosystems Monitoring Laboratory; RusiaFil: Lavreniuk, Mykola. Space Research Institute NAS and SSA. Department of Space Information Technologies; UcraniaFil: Skakun, Sergii. Space Research Institute NAS and SSA. Department of Space Information Technologies; Ucrania. University of Maryland. Department of Geographical Sciences; Estados UnidosFil: Kussul, Nataliia. Space Research Institute NAS and SSA. Department of Space Information Technologies; UcraniaFil: Le Maire, Guerric. UMR Eco&Sols, CIRAD; Francia. Empresa Brasileira de Pesquisa Agropecuária. Meio Ambiante; BrasilFil: Dupuy, Stéphane. Centre de Coopération Internationale en Recherche Agronomique pour le Développement. Territoires, Environnement, Télédétection et Information Spatiale; FranciaFil: Jarvis, Ian. Agriculture and Agri-Food Canada. Science and Technology Branch. Agri-Climate, Geomatics and Earth Observation; CanadáFil: Defourny, Pierre. Université Catholique de Louvain. Earth and Life Institute - Environment, Croix du Sud; Belgic

Cropland area estimates using Modis NDVI time series in the state of Mato Grosso, Brazil.

The objective of this work was to evaluate a simple, semi?automated methodology for mapping cropland areas in the state of Mato Grosso, Brazil. A Fourier transform was applied over a time series of vegetation index products from the moderate resolution imaging spectroradiometer (Modis) sensor. This procedure allows for the evaluation of the amplitude of the periodic changes in vegetation response through time and the identification of areas with strong seasonal variation related to crop production. Annual cropland masks from 2006 to 2009 were generated and municipal cropland areas were estimated through remote sensing. We observed good agreement with official statistics on planted area, especially for municipalities with more than 10% of cropland cover (R2 = 0.89), but poor agreement in municipalities with less than 5% crop cover (R2 = 0.41). The assessed methodology can be used for annual cropland mapping over large production areas in Brazil