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

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

Crop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information

Accurate monitoring of croplands helps in making decisions (for insurance claims, crop management and contingency plans) at the macro-level, especially in drylands where variability in cropping is very high owing to erratic weather conditions. Dryland cereals and grain legumes are key to ensuring the food and nutritional security of a large number of vulnerable populations living in the drylands. Reliable information on area cultivated to such crops forms part of the national accounting of food production and supply in many Asian countries, many of which are employing remote sensing tools to improve the accuracy of assessments of cultivated areas. This paper assesses the capabilities and limitations of mapping cultivated areas in the Rabi (winter) season and corresponding cropping patterns in three districts characterized by small-plot agriculture. The study used Sentinel-2 Normalized Difference Vegetation Index (NDVI) 15-day time-series at 10m resolution by employing a Spectral Matching Technique (SMT) approach. The use of SMT is based on the well-studied relationship between temporal NDVI signatures and crop phenology. The rabi season in India, dominated by non-rainy days, is best suited for the application of this method, as persistent cloud cover will hamper the availability of images necessary to generate clearly differentiating temporal signatures. Our study showed that the temporal signatures of wheat, chickpea and mustard are easily distinguishable, enabling an overall accuracy of 84%, with wheat and mustard achieving 86% and 94% accuracies, respectively. The most significant misclassifications were in irrigated areas for mustard and wheat, in small-plot mustard fields covered by trees and in fragmented chickpea areas. A comparison of district-wise national crop statistics and those obtained from this study revealed a correlation of 96%

Comparative geospatial approach for agricultural crops identification in interfluvial plain - A case study of Sahiwal district, Pakistan

Agricultural crop cover identification is a major issue and time-consuming effort to verify the crop type through surveys of the individual field or using prehistoric methods. To establish the scenario of crop identification, the stage of crop provides diverse spatial information about the variety of crops due to its spectral changes. The main aim of this study was to the identify the crop types and their behavior using remote sensing and geographical information system-based approach. Moreover, two main methods were applied to the Sentinel-2 satellite data in which one is random forest based supervised classification and another was Normalize Difference Vegetation Index (NDVI) density estimation method through the google earth engine to procure the data in time-efficient way. This study also established the comparison between classified and vegetation index based seasonal compositional datasets for wheat, cotton, maize, and fodder crops. Study discussed the best fit technique for crops identification in the light of observed methods. Furthermore, the vegetation index ranges by the zonal statistics of the field samples were established according to crop precision. Results showed that -22.94, -43.72, 20.61, and 32.49 % dissimilarities existed in wheat, fodder, cotton, and maize results respectively, after comparison of both techniques. Although, the accuracy assessment was performed on the classified dataset for validation of results by confusion matrix accuracy assessment process using field sample data. Moreover, the vegetation index was used to evaluate crop land surface temperature to estimate the crop growth stage valuation that revealed noticeably enthralling outcomes. The results determined that the classified accuracies of wheat, cotton, maize and fodder were 84, 80, 81 and 71 % respectively. This study also revealed that the random forest classifier has used more features and information potentially during the classifier trainings but vegetation index just implies the limited number of features such as crop growing status

Using Machine Learning to generate an open-access cropland map from satellite images time series in the Indian Himalayan Region

Crop maps are crucial for agricultural monitoring and food management and can additionally support domain-specific applications, such as setting cold supply chain infrastructure in developing countries. Machine learning (ML) models, combined with freely-available satellite imagery, can be used to produce cost-effective and high spatial-resolution crop maps. However, accessing ground truth data for supervised learning is especially challenging in developing countries due to factors such as smallholding and fragmented geography, which often results in a lack of crop type maps or even reliable cropland maps. Our area of interest for this study lies in Himachal Pradesh, India, where we aim at producing an open-access binary cropland map at 10-meter resolution for the Kullu, Shimla, and Mandi districts. To this end, we developed an ML pipeline that relies on Sentinel-2 satellite images time series. We investigated two pixel-based supervised classifiers, support vector machines (SVM) and random forest (RF), which are used to classify per-pixel time series for binary cropland mapping. The ground truth data used for training, validation and testing was manually annotated from a combination of field survey reference points and visual interpretation of very high resolution (VHR) imagery. We trained and validated the models via spatial cross-validation to account for local spatial autocorrelation and selected the RF model due to overall robustness and lower computational cost. We tested the generalization capability of the chosen model at the pixel level by computing the accuracy, recall, precision, and F1-score on hold-out test sets of each district, achieving an average accuracy for the RF (our best model) of 87%. We used this model to generate a cropland map for three districts of Himachal Pradesh, spanning 14,600 km2, which improves the resolution and quality of existing public maps

A comparison of global agricultural monitoring systems and current gaps

Global and regional scale agricultural monitoring systems aim to provide up-to-date information regarding food production to different actors and decision makers in support of global and national food security. To help reduce price volatility of the kind experienced between 2007 and 2011, a global system of agricultural monitoring systems is needed to ensure the coordinated flow of information in a timely manner for early warning purposes. A number of systems now exist that fill this role. This paper provides an overview of the eight main global and regional scale agricultural monitoring systems currently in operation and compares them based on the input data and models used, the outputs produced and other characteristics such as the role of the analyst, their interaction with other systems and the geographical scale at which they operate. Despite improvements in access to high resolution satellite imagery over the last decade and the use of numerous remote-sensing based products by the different systems, there are still fundamental gaps. Based on a questionnaire, discussions with the system experts and the literature, we present the main gaps in the data and in the methods. Finally, we propose some recommendations for addressing these gaps through ongoing improvements in remote sensing, harnessing new and innovative data streams and the continued sharing of more and more data

Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)

Understanding the use of current land cover, along with monitoring change over time, is vital for agronomists and agricultural agencies responsible for land management. The increasing spatial and temporal resolution of globally available satellite images, such as provided by Sentinel-2, creates new possibilities for researchers to use freely available multi-spectral optical images, with decametric spatial resolution and more frequent revisits for remote sensing applications such as land cover and crop classification (LC&CC), agricultural monitoring and management, environment monitoring. Existing solutions dedicated to cropland mapping can be categorized based on per-pixel based and object-based. However, it is still challenging when more classes of agricultural crops are considered at a massive scale. In this paper, a novel and optimal deep learning model for pixel-based LC&CC is developed and implemented based on Recurrent Neural Networks (RNN) in combination with Convolutional Neural Networks (CNN) using multi-temporal sentinel-2 imagery of central north part of Italy, which has diverse agricultural system dominated by economic crop types. The proposed methodology is capable of automated feature extraction by learning time correlation of multiple images, which reduces manual feature engineering and modeling crop phenological stages. Fifteen classes, including major agricultural crops, were considered in this study. We also tested other widely used traditional machine learning algorithms for comparison such as support vector machine SVM, random forest (RF), Kernal SVM, and gradient boosting machine, also called XGBoost. The overall accuracy achieved by our proposed Pixel R-CNN was 96.5%, which showed considerable improvements in comparison with existing mainstream methods. This study showed that Pixel R-CNN based model offers a highly accurate way to assess and employ time-series data for multi-temporal classification tasks

Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

The use of satellites to monitor crops and support their management is gathering increasing attention. The improved temporal, spatial, and spectral resolution of the European Space Agency (ESA) launched Sentinel-2 A + B twin platform is paving the way to their popularization in precision agriculture. Besides the Sentinel-2 A + B constellation technical features the open-access nature of the information they generate, and the available support software are a significant improvement for agricultural monitoring. This paper was motivated by the challenges faced by researchers and agrarian institutions entering this field; it aims to frame remote sensing principles and Sentinel-2 applications in agriculture. Thus, we reviewed the features and uses of Sentinel-2 in precision agriculture, including abiotic and biotic stress detection, and agricultural management. We also compared the panoply of satellites currently in use for land remote sensing that are relevant for agriculture to the Sentinel-2 A + B constellation features. Contrasted with previous satellite image systems, the Sentinel-2 A + B twin platform has dramatically increased the capabilities for agricultural monitoring and crop management worldwide. Regarding crop stress monitoring, Sentinel-2 capacities for abiotic and biotic stresses detection represent a great step forward in many ways though not without its limitations; therefore, combinations of field data and different remote sensing techniques may still be needed. We conclude that Sentinel-2 has a wide range of useful applications in agriculture, yet still with room for further improvements. Current and future ways that Sentinel-2 can be utilized are also discussed.This research was funded by the Spanish projects AGL2016-76527-R and IRUEC PCIN-2017-063 from the Ministerio de Economía y Competividad (MINECO, Spain) and by the support of Catalan Institution for Research and Advanced Studies (ICREA, Generalitat de Catalunya, Spain), through the ICREA Academia Program