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Globally, drought constitutes a serious threat to food and water security. The complexity and multivariate nature of drought challenges its assessment, especially at local scales. The study aimed to assess spatiotemporal patterns of crop condition and drought impact at the spatial scale of field management units with a combined use of time-series from optical (Landsat, MODIS, Sentinel-2) and Synthetic Aperture Radar (SAR) (Sentinel 1) data. Several indicators were derived such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Land Surface Temperature (LST), Tasseled cap indices and Sentinel-1 based backscattering intensity and relative surface moisture. We used logistic regression to evaluate the drought-induced variability of remotely sensed parameters estimated for different phases of crop growth. The parameters with the highest prediction rate were further used to estimate thresholds for drought/non-drought classification. The models were evaluated using the area under the receiver operating characteristic curve and validated with in-situ data. The results revealed that not all remotely sensed variables respond in the same manner to drought conditions. Growing season maximum NDVI and NDMI (70–75%) and SAR derived metrics (60%) reflect specifically the impact of agricultural drought. These metrics also depict stress affected areas with a larger spatial extent. LST was a useful indicator of crop condition especially for maize and sunflower with prediction rates of 86% and 71%, respectively. The developed approach can be further used to assess crop condition and to support decision-making in areas which are more susceptible and vulnerable to drought.
Gohar Ghazaryan; Olena Dubovyk; Valerie Graw; Nataliia Kussul; Jürgen Schellberg. Local-scale agricultural drought monitoring with satellite-based multi-sensor time-series. GIScience & Remote Sensing 2020, 57, 704 -718.
AMA StyleGohar Ghazaryan, Olena Dubovyk, Valerie Graw, Nataliia Kussul, Jürgen Schellberg. Local-scale agricultural drought monitoring with satellite-based multi-sensor time-series. GIScience & Remote Sensing. 2020; 57 (5):704-718.
Chicago/Turabian StyleGohar Ghazaryan; Olena Dubovyk; Valerie Graw; Nataliia Kussul; Jürgen Schellberg. 2020. "Local-scale agricultural drought monitoring with satellite-based multi-sensor time-series." GIScience & Remote Sensing 57, no. 5: 704-718.
A combination of Landsat 8 and Sentinel-2 offers a high frequency of observations (3–5 days) at moderate spatial resolution (10–30 m), which is essential for crop yield studies. Existing methods traditionally apply vegetation indices (VIs) that incorporate surface reflectances (SRs) in two or more spectral bands into a single variable, and rarely address the incorporation of SRs into empirical regression models of crop yield. In this work, we address these issues by normalizing satellite data (both VIs and SRs) derived from NASA’s Harmonized Landsat Sentinel-2 (HLS) product, through a phenological fitting. We apply a quadratic function to fit VIs or SRs against accumulated growing degree days (AGDDs), which affects the rate of crop development. The derived phenological metrics for VIs and SRs, namely peak, area under curve (AUC), and fitting coefficients from a quadratic function, were used to build empirical regression winter wheat models at a regional scale in Ukraine for three years, 2016–2018. The best results were achieved for the model with near infrared (NIR) and red spectral bands and derived AUC, constant, linear, and quadratic coefficients of the quadratic model. The best model yielded a root mean square error (RMSE) of 0.201 t/ha (5.4%) and coefficient of determination R2 = 0.73 on cross-validation.
Sergii Skakun; Eric Vermote; Belen Franch; Jean-Claude Roger; Nataliia Kussul; Junchang Ju; Jeffrey Masek. Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models. Remote Sensing 2019, 11, 1768 .
AMA StyleSergii Skakun, Eric Vermote, Belen Franch, Jean-Claude Roger, Nataliia Kussul, Junchang Ju, Jeffrey Masek. Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models. Remote Sensing. 2019; 11 (15):1768.
Chicago/Turabian StyleSergii Skakun; Eric Vermote; Belen Franch; Jean-Claude Roger; Nataliia Kussul; Junchang Ju; Jeffrey Masek. 2019. "Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models." Remote Sensing 11, no. 15: 1768.
There is a growing recognition of the interdependencies among the supply systems that rely upon food, water and energy. Billions of people lack safe and sufficient access to these systems, coupled with a rapidly growing global demand and increasing resource constraints. Modeling frameworks are considered one of the few means available to understand the complex interrelationships among the sectors, however development of nexus related frameworks has been limited. We describe three open-source models well known in their respective domains (i.e. TerrSysMP, WOFOST and SWAT) where components of each if combined could help decision-makers address the nexus issue. We propose as a first step the development of simple workflows utilizing essential variables and addressing components of the above-mentioned models which can act as building-blocks to be used ultimately in a comprehensive nexus model framework. The outputs of the workflows and the model framework are designed to address the SDGs.
Ian McCallum; Carsten Montzka; Bagher Bayat; Stefan Kollet; Andrii Kolotii; Nataliia Kussul; Mykola Lavreniuk; Anthony Lehmann; Joan Maso; Paolo Mazzetti; Aline Mosnier; Emma Perracchione; Mario Putti; Mattia Santoro; Ivette Serral; Leonid Shumilo; Daniel Spengler; Steffen Fritz. Developing food, water and energy nexus workflows. International Journal of Digital Earth 2019, 13, 299 -308.
AMA StyleIan McCallum, Carsten Montzka, Bagher Bayat, Stefan Kollet, Andrii Kolotii, Nataliia Kussul, Mykola Lavreniuk, Anthony Lehmann, Joan Maso, Paolo Mazzetti, Aline Mosnier, Emma Perracchione, Mario Putti, Mattia Santoro, Ivette Serral, Leonid Shumilo, Daniel Spengler, Steffen Fritz. Developing food, water and energy nexus workflows. International Journal of Digital Earth. 2019; 13 (2):299-308.
Chicago/Turabian StyleIan McCallum; Carsten Montzka; Bagher Bayat; Stefan Kollet; Andrii Kolotii; Nataliia Kussul; Mykola Lavreniuk; Anthony Lehmann; Joan Maso; Paolo Mazzetti; Aline Mosnier; Emma Perracchione; Mario Putti; Mattia Santoro; Ivette Serral; Leonid Shumilo; Daniel Spengler; Steffen Fritz. 2019. "Developing food, water and energy nexus workflows." International Journal of Digital Earth 13, no. 2: 299-308.
For evaluating the progresses towards achieving the Sustainable Development Goals (SDGs), a global indicator framework was developed by the UN Inter-Agency and Expert Group on Sustainable Development Goals Indicators. In this paper, we propose an improved methodology and a set of workflows for calculating SDGs indicators. The main improvements consist of using moderate and high spatial resolution satellite data and state-of-the-art deep learning methodology for land cover classification and for assessing land productivity. Within the European Network for Observing our Changing Planet (ERA-PLANET), three SDGs indicators are calculated. In this research, harmonized Landsat and Sentinel-2 data are analyzed and used for land productivity analysis and yield assessment, as well as Landsat 8, Sentinel-2 and Sentinel-1 time series are utilized for crop mapping. We calculate for the whole territory of Ukraine SDG indicators: 15.1.1 – ‘Forest area as proportion of total land area’; 15.3.1 – ‘Proportion of land that is degraded over total land area’; and 2.4.1 – ‘Proportion of agricultural area under productive and sustainable agriculture’. Workflows for calculating these indicators were implemented in a Virtual Laboratory Platform. We conclude that newly available high-resolution remote sensing products can significantly improve our capacity to assess several SDGs indicators through dedicated workflows.
Nataliia Kussul; Mykola Lavreniuk; Andrii Kolotii; Sergii Skakun; Olena Rakoid; Leonid Shumilo. A workflow for Sustainable Development Goals indicators assessment based on high-resolution satellite data. International Journal of Digital Earth 2019, 13, 309 -321.
AMA StyleNataliia Kussul, Mykola Lavreniuk, Andrii Kolotii, Sergii Skakun, Olena Rakoid, Leonid Shumilo. A workflow for Sustainable Development Goals indicators assessment based on high-resolution satellite data. International Journal of Digital Earth. 2019; 13 (2):309-321.
Chicago/Turabian StyleNataliia Kussul; Mykola Lavreniuk; Andrii Kolotii; Sergii Skakun; Olena Rakoid; Leonid Shumilo. 2019. "A workflow for Sustainable Development Goals indicators assessment based on high-resolution satellite data." International Journal of Digital Earth 13, no. 2: 309-321.
Cropland maps derived from satellite imagery have become a common source of information to estimate food production, support land use policies, and measure the environmental impacts of agriculture. Cropland classification models are typically calibrated with data collected from roadside surveys which enable the sampling of large areas at a relatively low cost. However, there is a risk of providing biased data as environmental and management gradients may not be fully captured from road networks, thereby violating the assumption of representativeness of calibration data. Despite being widely adopted, the potential biases of roadside sampling have so far not been thoroughly addressed. In this study, we looked for evidence of these biases by comparing three sampling strategies: Random sampling, Roadside sampling, and Transect sampling – a spatially constrained variant of Roadside sampling. In these three strategies, non-cropland data are randomly distributed as they can be photo-interpreted. Based on reference maps at 30 m in four study sites, we followed a Monte Carlo approach to generate multiple realizations of each sampling strategy for ten sample sizes. The effect of the sampling strategy was then assessed in terms of representativeness of the data set collected and accuracy of the resulting maps. Results showed that data sets obtained from Roadside sampling were significantly less representative than those obtained from Random sampling but the resulting maps were only marginally less accurate (2% difference). Transect sampling captured systematically less variability than Random or Roadside sampling which led to differences in accuracy as large as 15%. The effect of sample size on accuracy varied across sites but generally leveled off after reaching 3000 pixels. Augmenting the size of Transect samples improved the classification accuracy but not sufficiently to match the performance of the other sampling strategies. Finally, we found that Random and Roadside training sets with similar representativeness yield comparable accuracy. Therefore, we conclude that roadside sampling can be a viable source of training data for cropland mapping if the range of environmental and management gradients is surveyed. This underlines the importance of survey planning to identify those routes that capture most variability.
François Waldner; Nicolas Bellemans; Zvi Hochman; Terence Newby; Diego de Abelleyra; Santiago R. Verón; Sergey Bartalev; Mykola Lavreniuk; Nataliia Kussul; Guerric Le Maire; Margareth Simoes; Sergii Skakun; Pierre Defourny. Roadside collection of training data for cropland mapping is viable when environmental and management gradients are surveyed. International Journal of Applied Earth Observation and Geoinformation 2019, 80, 82 -93.
AMA StyleFrançois Waldner, Nicolas Bellemans, Zvi Hochman, Terence Newby, Diego de Abelleyra, Santiago R. Verón, Sergey Bartalev, Mykola Lavreniuk, Nataliia Kussul, Guerric Le Maire, Margareth Simoes, Sergii Skakun, Pierre Defourny. Roadside collection of training data for cropland mapping is viable when environmental and management gradients are surveyed. International Journal of Applied Earth Observation and Geoinformation. 2019; 80 ():82-93.
Chicago/Turabian StyleFrançois Waldner; Nicolas Bellemans; Zvi Hochman; Terence Newby; Diego de Abelleyra; Santiago R. Verón; Sergey Bartalev; Mykola Lavreniuk; Nataliia Kussul; Guerric Le Maire; Margareth Simoes; Sergii Skakun; Pierre Defourny. 2019. "Roadside collection of training data for cropland mapping is viable when environmental and management gradients are surveyed." International Journal of Applied Earth Observation and Geoinformation 80, no. : 82-93.
In this paper, we propose methodology for calculating indicators of sustainable development goals within the GEOEssential project, that is a part of ERA-PLANET Horizon 2020 project. We consider indicators 15.1.1 Forest area as proportion of total land area, 15.3.1 Proportion of land that is degraded over total land area, and 2.4.1. Proportion of agricultural area under productive and sustainable agriculture. For this, we used remote sensing data, weather and climatic models’ data and in-situ data. Accurate land cover maps are important for precisely land cover changes assessment. To improve the resolution and quality of existing global land cover maps, we proposed our own deep learning methodology for country level land cover providing. For calculating essential variables, that are vital for achieving indicators, NEXUS approach based on idea of fusion food, energy, and water was applied. Long-term land cover change maps connected with land productivity maps are essential for determining environment changes and estimation of consequences of anthropogenic activity.
Nataliia Kussul; Mykola Lavreniuk; Leonid Sumilo; Andrii Kolotii; Olena Rakoid; Bohdan Yailymov; Andrii Shelestov; Vladimir Vasiliev. Assessment of Sustainable Development Goals Achieving with Use of NEXUS Approach in the Framework of GEOEssential ERA-PLANET Project. Advances in Intelligent Systems and Computing 2018, 146 -155.
AMA StyleNataliia Kussul, Mykola Lavreniuk, Leonid Sumilo, Andrii Kolotii, Olena Rakoid, Bohdan Yailymov, Andrii Shelestov, Vladimir Vasiliev. Assessment of Sustainable Development Goals Achieving with Use of NEXUS Approach in the Framework of GEOEssential ERA-PLANET Project. Advances in Intelligent Systems and Computing. 2018; ():146-155.
Chicago/Turabian StyleNataliia Kussul; Mykola Lavreniuk; Leonid Sumilo; Andrii Kolotii; Olena Rakoid; Bohdan Yailymov; Andrii Shelestov; Vladimir Vasiliev. 2018. "Assessment of Sustainable Development Goals Achieving with Use of NEXUS Approach in the Framework of GEOEssential ERA-PLANET Project." Advances in Intelligent Systems and Computing , no. : 146-155.
In this paper, we propose a novel method for an object-based post-classification filtering, specifically tailored to improve agricultural land use maps. That has significant impact on the solving other applied tasks like detection of land cover changes and crop rotation violation, area estimation and crop yield forecasting. The main idea of this method is to divide classification map into separate objects (group of pixels with the same class value) and investigate the properties of them, taking into account the specificity of each class, independently. The most challenging task in post-classification filtering is preserving edges and boundaries between different fields. Often these boundaries are narrow and some traditional filters tend to treat this like noise and remove them. To deal with this, our method identifies boundaries of objects like crop fields, based on a modified version of the Sobel algorithm. The accuracy and effectiveness of our method has been tested and compared with other methods, based on accuracy assessments and visual comparison.
Mykola Lavreniuk; Nataliia Kussul; Andrii Shelestov; Olena Dubovyk; Fabian Low. Object-Based Postprocessing Method for Crop Classification MAPS. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 7058 -7061.
AMA StyleMykola Lavreniuk, Nataliia Kussul, Andrii Shelestov, Olena Dubovyk, Fabian Low. Object-Based Postprocessing Method for Crop Classification MAPS. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():7058-7061.
Chicago/Turabian StyleMykola Lavreniuk; Nataliia Kussul; Andrii Shelestov; Olena Dubovyk; Fabian Low. 2018. "Object-Based Postprocessing Method for Crop Classification MAPS." IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 7058-7061.
Crop classification maps based on high resolution remote sensing data are essential for supporting sustainable land management. The most challenging problems for their producing are collecting of ground based training and validation datasets, non-regular satellite data acquisition and cloudiness. To increase the efficiency of ground data utilization it is important to develop classifiers able to be trained on the data collected in the previous year. In this study, we propose new deep learning method for providing crop classification maps using in-situ data that has been collected in the previous year. Main idea of the study is to utilize deep learning approach based on sparse autoencoder. At the first stage it is trained on satellite data only and then neural network fine-tuning is conducted based on in-situ data form the previous year. Taking into account that collecting ground truth data is very time consuming and challenging task, the proposed approach allows us to avoid necessity for annual collecting in-situ data for the same territory. Experimental results for the territory of Ukraine show that this technique is rather efficient and provides reliable crop classification maps with overall accuracy higher than 85.9%.
Mykola Lavreniuk; Nataliia Kussul; Alexei Novikov. Deep Learning Crop Classification Approach Based on Sparse Coding of Time Series of Satellite Data. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 4812 -4815.
AMA StyleMykola Lavreniuk, Nataliia Kussul, Alexei Novikov. Deep Learning Crop Classification Approach Based on Sparse Coding of Time Series of Satellite Data. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():4812-4815.
Chicago/Turabian StyleMykola Lavreniuk; Nataliia Kussul; Alexei Novikov. 2018. "Deep Learning Crop Classification Approach Based on Sparse Coding of Time Series of Satellite Data." IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 4812-4815.
To provide reliable crop maps for the same territory each year, it is necessary to collect in-situ data for each year independently. Collecting ground truth data is a very time consuming and challenging task. At present, unfortunately, there is no an adopted approach, how to utilize in-situ and satellite data from previous years for crop mapping in the subsequent years. In this paper, we propose a new deep learning approach using sparse autoencoder based on only satellite data, and a further procedure of neural network fine-tuning based on in-situ data. The possibility of utilizing this deep learning architecture based on translating all available satellite data into the unified hyperspace. The study is carried out for the central part of Ukraine. Obtained results show that this technique is feasible and provides reliable crop classification maps with overall accuracy (OA) of 91.0% and 85.9% for two different experiments. The use of the proposed approach makes it possible to avoid, or decrease, the necessity for collecting in-situ data for each year and for each part of large territory.
Mykola Lavreniuk; Nataliia Kussul; Alexei Novikov. Deep Learning Crop Classification Approach Based on Coding Input Satellite Data Into the Unified Hyperspace. 2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO) 2018, 239 -244.
AMA StyleMykola Lavreniuk, Nataliia Kussul, Alexei Novikov. Deep Learning Crop Classification Approach Based on Coding Input Satellite Data Into the Unified Hyperspace. 2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO). 2018; ():239-244.
Chicago/Turabian StyleMykola Lavreniuk; Nataliia Kussul; Alexei Novikov. 2018. "Deep Learning Crop Classification Approach Based on Coding Input Satellite Data Into the Unified Hyperspace." 2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO) , no. : 239-244.
Along the season crop classification maps based on satellite data is a challenging task for countries with large diversity of agricultural crops with different phenology (crop calendars). In this paper, we investigate feasibility of delivering early and along the season crop specific maps using available free satellite data over multiple years, including Landsat-8, Sentinel-1 and Sentinel-2. For this study, a test site in Kyiv region (Ukraine) is selected, for which we have been collecting ground data on crop types every year since 2011. Crop type maps are generated through a supervised classification of multi-temporal multi-source satellite data using previously developed artificial neural network algorithms. It is shown, how multi-year crop classification maps are used for crop rotation violation detection. The study shows that in case of considerable cloud cover, synthetic aperture radar (SAR) data, for example acquired by Sentinel-1 satellite, can be interchangeably used with optical imagery to achieve the target 85% accuracy for crop classification.
Nataliia Kussul; Lavreniuk Mykola; Andrii Shelestov; Sergii Skakun. Crop inventory at regional scale in Ukraine: developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery. European Journal of Remote Sensing 2018, 51, 627 -636.
AMA StyleNataliia Kussul, Lavreniuk Mykola, Andrii Shelestov, Sergii Skakun. Crop inventory at regional scale in Ukraine: developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery. European Journal of Remote Sensing. 2018; 51 (1):627-636.
Chicago/Turabian StyleNataliia Kussul; Lavreniuk Mykola; Andrii Shelestov; Sergii Skakun. 2018. "Crop inventory at regional scale in Ukraine: developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery." European Journal of Remote Sensing 51, no. 1: 627-636.
Accurate classification and mapping of crops is essential for supporting sustainable land management. Such maps can be created based on satellite remote sensing; however, the selection of input data and optimal classifier algorithm still needs to be addressed especially for areas where field data is scarce. We exploited the intra-annual variation of temporal signatures of remotely sensed observations and used prior knowledge of crop calendars for the development of a two-step processing chain for crop classification. First, Landsat-based time-series metrics capturing within-season phenological variation were preprocessed and analyzed using Google Earth Engine cloud computing platform. The developmental stage of each crop was modeled by fitting harmonic function. The model’s output was further used for the automatic generation of training samples. Second, several classification methods (support vector machines, random forest, decision fusion) were tested. As input data for crop classification, composites based on Sentinel-1 and Landsat images were used. Overall classification accuracies exceeded 80% when the seasonal composites were used. Winter cereals were the most accurately classified, while we observed misclassifications among summer crops. The proposed approach offers a potential to accurately map crops in the areas where in situ field data are scarce or unavailable.
Gohar Ghazaryan; Olena Dubovyk; Fabian Löw; Mykola Lavreniuk; Andrii Kolotii; Jürgen Schellberg; Nataliia Kussul. A rule-based approach for crop identification using multi-temporal and multi-sensor phenological metrics. European Journal of Remote Sensing 2018, 51, 511 -524.
AMA StyleGohar Ghazaryan, Olena Dubovyk, Fabian Löw, Mykola Lavreniuk, Andrii Kolotii, Jürgen Schellberg, Nataliia Kussul. A rule-based approach for crop identification using multi-temporal and multi-sensor phenological metrics. European Journal of Remote Sensing. 2018; 51 (1):511-524.
Chicago/Turabian StyleGohar Ghazaryan; Olena Dubovyk; Fabian Löw; Mykola Lavreniuk; Andrii Kolotii; Jürgen Schellberg; Nataliia Kussul. 2018. "A rule-based approach for crop identification using multi-temporal and multi-sensor phenological metrics." European Journal of Remote Sensing 51, no. 1: 511-524.
In this paper, we investigate global and national level datasets, used to estimate trends in land cover and in land productivity in Ukraine within Land Degradation Neutrality (LDN) Target Setting Programme of United Nations Convention to Combat Desertification (UNCCD). To assess land cover changes, the ESA CCI-LC 2000 and 2010 epochs are used, focusing on changes between the 6 main land cover categories. The JRC's Land Productivity Dynamics (LPD) dataset is used as default source for land productivity data. The LPD dataset is derived from a 15-year time series (1999 to 2013) of global NDVI observations composited in 10-day intervals at a spatial resolution of 1 km. To improve the resolution of satellite based products at national level we use regional land cover datasets for Ukraine developed by Space Research Institute (SRI) and available for 1990, 2000 and 2010 respectively. They are developed according to international standards (LUCAS classification) and are comparable with ESA CCI-LC in terms of class nomenclature and notations. These land cover maps at 30 m spatial resolution have been developed for the whole territory of Ukraine based on the Landsat-4/5/7 images for three decades, namely 1990s, 2000s and 2010s within FP-7 SIGMA Project. They are based on our Machine Learning approach for classification of time series of satellite data.
Nataliia Kussul; Andrii Kolotii; Andrii Shelestov; Bohdan Yailymov; Mykola Lavreniuk. Land degradation estimation from global and national satellite based datasets within UN program. 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) 2017, 1, 383 -386.
AMA StyleNataliia Kussul, Andrii Kolotii, Andrii Shelestov, Bohdan Yailymov, Mykola Lavreniuk. Land degradation estimation from global and national satellite based datasets within UN program. 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). 2017; 1 ():383-386.
Chicago/Turabian StyleNataliia Kussul; Andrii Kolotii; Andrii Shelestov; Bohdan Yailymov; Mykola Lavreniuk. 2017. "Land degradation estimation from global and national satellite based datasets within UN program." 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) 1, no. : 383-386.
Ukraine is a large agricultural country situated in Eastern Europe (603,500 km2). Nowadays in Ukraine, there is no any land market due to the moratorium on land sales. Nevertheless, in all areas preparation for land market is undergoing. Cropland productivity assessment based on satellite data is a challenging task for Ukraine because of a large territory and big diversity of agricultural crops. Cropland productivity is one of the major factors for forming the land price. In this paper, we aim to provide land productivity maps based on analysis of MODIS and Landsat-8 data due to availability long term time-series of Normalized Difference Vegetation index (NDVI) from sensors aboard those remote sensing satellites. Taking into account the huge amount of satellite products to be analyzed, in the study we propose to exploit the Google Earth Engine (GEE) cloud platform. It was found that land productivity maps provided from MODIS data for different time periods are strongly correlated. The experiment shows that land productivity maps should have high resolution. That is why, Landsat-8 data is more appropriate for land market purpose, despite of some bias in values comparing to results based on MODIS data. Comparing crop mask from ESA Sen2Agri project and obtained results it was found the dependence of land productivity value and crop/non-crop cover. It was found that irrigated fields from the south part of the study area are the most productive lands in Ukraine.
Nataliia Kussul; Mykola Lavreniuk; Sergii Skakun; Andrii Shelestov. Cropland productivity assessment for Ukraine based on time series of optical satellite images. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2017, 5007 -5010.
AMA StyleNataliia Kussul, Mykola Lavreniuk, Sergii Skakun, Andrii Shelestov. Cropland productivity assessment for Ukraine based on time series of optical satellite images. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2017; ():5007-5010.
Chicago/Turabian StyleNataliia Kussul; Mykola Lavreniuk; Sergii Skakun; Andrii Shelestov. 2017. "Cropland productivity assessment for Ukraine based on time series of optical satellite images." 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , no. : 5007-5010.
For many applied problems in agricultural monitoring and food security it is important to provide reliable crop classification maps in national or global scale. Large amount of satellite data for large scale crop mapping generate a “Big Data” problem. The main idea of this paper was comparison of pixel-based approaches to crop mapping in Ukraine and exploring efficiency of the Google Earth Engine (GEE) cloud platform for solving “Big Data” problem and providing high resolution crop classification map for large territory. The study is carried out for the Joint Experiment of Crop Assessment and Monitoring (JECAM) test site in Ukraine covering the Kyiv region (North of Ukraine) in 2013. We found that Google Earth Engine (GEE) provided very good performance in enabling access to remote sensing products through the cloud platform, but our own approach based on ensemble of neural networks outperformed SVM, decision tree and random forest classifiers that are available in GEE.
Andrii Shelestov; Mykola Lavreniuk; Nataliia Kussul; Alexei Novikov; Sergii Skakun. Large scale crop classification using Google earth engine platform. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2017, 3696 -3699.
AMA StyleAndrii Shelestov, Mykola Lavreniuk, Nataliia Kussul, Alexei Novikov, Sergii Skakun. Large scale crop classification using Google earth engine platform. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2017; ():3696-3699.
Chicago/Turabian StyleAndrii Shelestov; Mykola Lavreniuk; Nataliia Kussul; Alexei Novikov; Sergii Skakun. 2017. "Large scale crop classification using Google earth engine platform." 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , no. : 3696-3699.
Data provided by synthetic aperture radar (SAR) of Sentinel satellite can be useful for many applications. However, as for any SAR image, speckle noise is present in acquired images. Speckle properties are important for different operations of SAR image processing as filtering, edge detection, segmentation, classification. Thus, we first carry out preliminary analysis of speckle statistics and show that speckle PDF is quite close to Gaussian whilst noise is of practically multiplicative nature. Second, spatial correlation properties of speckle are analyzed. The study is performed in local DCT domain. This is done since then the obtained 2D spectrum is employed in image despeckling based on DCT. Peculiarities of several possible approaches to despeckling are discussed. Several examples for one component and dual polarization data are presented.
Sergey Abramov; Oleksii Rubel; Vladimir Lukin; Ruslan Kozhemiakin; Nataliia Kussul; Andrii Shelestov; Mykola Lavreniuk. Speckle reducing for Sentinel-1 SAR data. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2017, 2353 -2356.
AMA StyleSergey Abramov, Oleksii Rubel, Vladimir Lukin, Ruslan Kozhemiakin, Nataliia Kussul, Andrii Shelestov, Mykola Lavreniuk. Speckle reducing for Sentinel-1 SAR data. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2017; ():2353-2356.
Chicago/Turabian StyleSergey Abramov; Oleksii Rubel; Vladimir Lukin; Ruslan Kozhemiakin; Nataliia Kussul; Andrii Shelestov; Mykola Lavreniuk. 2017. "Speckle reducing for Sentinel-1 SAR data." 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , no. : 2353-2356.
Agriculture is one of the key areas where Remote Sensing (RS) techniques can be efficiently implemented for solving wide range of tasks (crop mapping, crop monitoring, crop yield forecasting etc.) on regular basis. Sentinel mission represents really new opportunities in agricultural domain - free of charge for non-commercial use satellite images with 10-20 m spatial resolution, 5-day revisit frequency with global coverage and compatibility to the Landsat missions. In this paper we present the results of Sentinel-2 national demonstration project in Ukraine executed during vegetation period of 2016 and coordinated by Universite catholique de Louvain (UCL). Within this demonstration Ukraine was selected as one of three sites for national demonstration due to high variability of agroclimatic conditions, relatively big fields and wide range of major crops over the territory of the country.
Nataliia Kussul; Andrii Kolotii; Andrii Shelestov; Mykola Lavreniuk; Nicolas Bellemans; Sophie Bontemps; Pierre Defourny; Benjamin Koetz. Sentinel-2 for agriculture national demonstration in ukraine: Results and further steps. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2017, 5842 -5845.
AMA StyleNataliia Kussul, Andrii Kolotii, Andrii Shelestov, Mykola Lavreniuk, Nicolas Bellemans, Sophie Bontemps, Pierre Defourny, Benjamin Koetz. Sentinel-2 for agriculture national demonstration in ukraine: Results and further steps. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2017; ():5842-5845.
Chicago/Turabian StyleNataliia Kussul; Andrii Kolotii; Andrii Shelestov; Mykola Lavreniuk; Nicolas Bellemans; Sophie Bontemps; Pierre Defourny; Benjamin Koetz. 2017. "Sentinel-2 for agriculture national demonstration in ukraine: Results and further steps." 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , no. : 5842-5845.
Sergii Skakun; Belen Franch; Eric Vermote; Jean-Claude Roger; Inbal Becker-Reshef; Christopher Justice; Nataliia Kussul. Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information and a Gaussian mixture model. Remote Sensing of Environment 2017, 195, 244 -258.
AMA StyleSergii Skakun, Belen Franch, Eric Vermote, Jean-Claude Roger, Inbal Becker-Reshef, Christopher Justice, Nataliia Kussul. Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information and a Gaussian mixture model. Remote Sensing of Environment. 2017; 195 ():244-258.
Chicago/Turabian StyleSergii Skakun; Belen Franch; Eric Vermote; Jean-Claude Roger; Inbal Becker-Reshef; Christopher Justice; Nataliia Kussul. 2017. "Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information and a Gaussian mixture model." Remote Sensing of Environment 195, no. : 244-258.
Deep learning (DL) is a powerful state-of-the-art technique for image processing including remote sensing (RS) images. This letter describes a multilevel DL architecture that targets land cover and crop type classification from multitemporal multisource satellite imagery. The pillars of the architecture are unsupervised neural network (NN) that is used for optical imagery segmentation and missing data restoration due to clouds and shadows, and an ensemble of supervised NNs. As basic supervised NN architecture, we use a traditional fully connected multilayer perceptron (MLP) and the most commonly used approach in RS community random forest, and compare them with convolutional NNs (CNNs). Experiments are carried out for the joint experiment of crop assessment and monitoring test site in Ukraine for classification of crops in a heterogeneous environment using nineteen multitemporal scenes acquired by Landsat-8 and Sentinel-1A RS satellites. The architecture with an ensemble of CNNs outperforms the one with MLPs allowing us to better discriminate certain summer crop types, in particular maize and soybeans, and yielding the target accuracies more than 85% for all major crops (wheat, maize, sunflower, soybeans, and sugar beet).
Nataliia Kussul; Mykola Lavreniuk; Sergii Skakun; Andrii Shelestov. Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data. IEEE Geoscience and Remote Sensing Letters 2017, 14, 778 -782.
AMA StyleNataliia Kussul, Mykola Lavreniuk, Sergii Skakun, Andrii Shelestov. Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data. IEEE Geoscience and Remote Sensing Letters. 2017; 14 (5):778-782.
Chicago/Turabian StyleNataliia Kussul; Mykola Lavreniuk; Sergii Skakun; Andrii Shelestov. 2017. "Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data." IEEE Geoscience and Remote Sensing Letters 14, no. 5: 778-782.
Many applied problems arising in agricultural monitoring and food security require reliable crop maps at national or global scale. Large scale crop mapping requires processing and management of large amount of heterogeneous satellite imagery acquired by various sensors that consequently leads to a “Big Data” problem. The main objective of this study is to explore efficiency of using the Google Earth Engine (GEE) platform when classifying multi-temporal satellite imagery with potential to apply the platform for a larger scale (e.g. country level) and multiple sensors (e.g. Landsat-8 and Sentinel-2). In particular, multiple state-of-the-art classifiers available in the GEE platform are compared to produce a high resolution (30 m) crop classification map for a large territory (~28,100 km2 and 1.0 M ha of cropland). Though this study does not involve large volumes of data, it does address efficiency of the GEE platform to effectively execute complex workflows of satellite data processing required with large scale applications such as crop mapping. The study discusses strengths and weaknesses of classifiers, assesses accuracies that can be achieved with different classifiers for the Ukrainian landscape, and compares them to the benchmark classifier using a neural network approach that was developed in our previous studies. The study is carried out for the Joint Experiment of Crop Assessment and Monitoring (JECAM) test site in Ukraine covering the Kyiv region (North of Ukraine) in 2013. We found that Google Earth Engine (GEE) provides very good performance in terms of enabling access to the remote sensing products through the cloud platform and providing pre-processing; however, in terms of classification accuracy, the neural network based approach outperformed support vector machine (SVM), decision tree and random forest classifiers available in GEE.
Andrii Shelestov; Mykola Lavreniuk; Nataliia Kussul; Alexei Novikov; Sergii Skakun. Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping. Frontiers in Earth Science 2017, 5, 1 .
AMA StyleAndrii Shelestov, Mykola Lavreniuk, Nataliia Kussul, Alexei Novikov, Sergii Skakun. Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping. Frontiers in Earth Science. 2017; 5 ():1.
Chicago/Turabian StyleAndrii Shelestov; Mykola Lavreniuk; Nataliia Kussul; Alexei Novikov; Sergii Skakun. 2017. "Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping." Frontiers in Earth Science 5, no. : 1.
Along the season crop classification based on satellite data is challenging task for Ukraine because of a big diversity of different agricultural crops with different phenology (crop calendars). Taking into account the availability for free of high resolution (10 to 30 meter) optical and SAR data from different satellite, the most resource consuming task is ground data collecting. That is why the proper time of ground surveys and crop classification maps developing is very important. In the study we propose to build three crop classification maps for JECAM Ukraine test site in Kyiv region during the vegetation season. The first one is built in the middle of May to classify winter cereals and rapeseeds. The next crop classification map is developing in July to discriminate major summer crops (spring cereals, maize, soybeans, sunflowers). The final crop map is built in autumn to refine summer crops and sugar beet discrimination. Time series of multi-temporal satellite images with restored missing (clouded and shadowed) data are classified using neural network approach, in particular ensemble of multi-layer perceptrons (MLPs). It is shown, that addition of satellite data from the end of previous year to the spring imagery allows to significantly improve the accuracy of winter crops classification. In July it is possible to deliver the map with major summer crops with overall accuracy higher than 87%, and the overall accuracy of final map at the end of the season is 94%.
Nataliia Kussul; Mykola Lavreniuk; Andrii Shelestov; Bohdan Yailymov. Along the season crop classification in Ukraine based on time series of optical and SAR images using ensemble of neural network classifiers. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2016, 7145 -7148.
AMA StyleNataliia Kussul, Mykola Lavreniuk, Andrii Shelestov, Bohdan Yailymov. Along the season crop classification in Ukraine based on time series of optical and SAR images using ensemble of neural network classifiers. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2016; ():7145-7148.
Chicago/Turabian StyleNataliia Kussul; Mykola Lavreniuk; Andrii Shelestov; Bohdan Yailymov. 2016. "Along the season crop classification in Ukraine based on time series of optical and SAR images using ensemble of neural network classifiers." 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , no. : 7145-7148.