Analyzing the Adoption, Cropping Rotation, and Impact of Winter Cover Crops in the Mississippi Alluvial Plain (MAP) Region through Remote Sensing Technologies

This dissertation explores the application of remote sensing technologies in conservation agriculture, specifically focusing on identifying and mapping winter cover crops and assessing voluntary cover crop adoption and cropping patterns in the Arkansas portion of the Mississippi Alluvial Plain (MAP). In the first chapter, a systematic review using the PRISMA methodology examines the last 30 years of thematic research, development, and trends in remote sensing applied to conservation agriculture from a global perspective. The review uncovers a growing interest in remote sensing-based research in conservation agriculture and emphasizes the necessity for further studies dedicated to conservation practices. Among the 68 articles examined, 94% of studies utilized a pixel-based classification method, while only 6% employed an object-based approach. The analysis also revealed a thematic shift over time, with tillage practices being extensively studied before 2005, followed by a focus on crop residue from 2004 to 2012. From 2012 to 2020, there was a renewed emphasis on cover crops research. These findings highlight the evolving research landscape and provide insights into the trends within remote sensing-based conservation agriculture studies. The second chapter presents a methodological framework for identifying and mapping winter cover crops. The framework utilizes the Google Earth Engine (GEE) and a Random Forest (RF) classifier with time series data from Landsat 8 satellite. Results demonstrate a high classification accuracy (97.7%) and a significant increase (34%) in model-predicted cover crop adoption over the study period between 2013 and 2019. Additionally, the study showcases the use of multi-year datasets to efficiently map the growing season\u27s length and cover crops\u27 phenological characteristics. The third chapter assesses the voluntary adoption of winter cover crops and cropping patterns in the MAP region. Remote sensing technologies, USDA-NRCS government cover crop data sources, and the USDA Cropland Data Layer (CDL) are employed to identify cover crop locations, analyze county-wide voluntary adoption, and cropping rotations. The result showed a 5.33% increase in the overall voluntary adoption of cover crops in the study region between 2013 and 2019. The findings also indicate a growing trend in cover crop adoption, with soybean-cover crop rotations being prominent. This dissertation enhances our understanding of the role of remote sensing in conservation agriculture with a particular focus on winter cover crops. These insights are valuable for policymakers, stakeholders, and researchers seeking to promote sustainable agricultural practices and increased cover crop adoption. The study also underscores the significance of integrating remote sensing technologies into agricultural decision-making processes and highlights the importance of collaboration among policymakers, researchers, and producers. By leveraging the capabilities of remote sensing, it will enhance conservation agriculture contribution to long-term environmental sustainability and agricultural resilience. Keywords: Remote sensing technologies, Conservation agriculture, Winter cover crops, Voluntary adoption, Cropping patterns, Sustainable agricultural practice

Analyzing the Adoption, Cropping Rotation, and Impact of Winter Cover Crops in the Mississippi Alluvial Plain (MAP) Region through Remote Sensing Technologies

This dissertation explores the application of remote sensing technologies in conservation agriculture, specifically focusing on identifying and mapping winter cover crops and assessing voluntary cover crop adoption and cropping patterns in the Arkansas portion of the Mississippi Alluvial Plain (MAP). In the first chapter, a systematic review using the PRISMA methodology examines the last 30 years of thematic research, development, and trends in remote sensing applied to conservation agriculture from a global perspective. The review uncovers a growing interest in remote sensing-based research in conservation agriculture and emphasizes the necessity for further studies dedicated to conservation practices. Among the 68 articles examined, 94% of studies utilized a pixel-based classification method, while only 6% employed an object-based approach. The analysis also revealed a thematic shift over time, with tillage practices being extensively studied before 2005, followed by a focus on crop residue from 2004 to 2012. From 2012 to 2020, there was a renewed emphasis on cover crops research. These findings highlight the evolving research landscape and provide insights into the trends within remote sensing-based conservation agriculture studies. The second chapter presents a methodological framework for identifying and mapping winter cover crops. The framework utilizes the Google Earth Engine (GEE) and a Random Forest (RF) classifier with time series data from Landsat 8 satellite. Results demonstrate a high classification accuracy (97.7%) and a significant increase (34%) in model-predicted cover crop adoption over the study period between 2013 and 2019. Additionally, the study showcases the use of multi-year datasets to efficiently map the growing season\u27s length and cover crops\u27 phenological characteristics. The third chapter assesses the voluntary adoption of winter cover crops and cropping patterns in the MAP region. Remote sensing technologies, USDA-NRCS government cover crop data sources, and the USDA Cropland Data Layer (CDL) are employed to identify cover crop locations, analyze county-wide voluntary adoption, and cropping rotations. The result showed a 5.33% increase in the overall voluntary adoption of cover crops in the study region between 2013 and 2019. The findings also indicate a growing trend in cover crop adoption, with soybean-cover crop rotations being prominent. This dissertation enhances our understanding of the role of remote sensing in conservation agriculture with a particular focus on winter cover crops. These insights are valuable for policymakers, stakeholders, and researchers seeking to promote sustainable agricultural practices and increased cover crop adoption. The study also underscores the significance of integrating remote sensing technologies into agricultural decision-making processes and highlights the importance of collaboration among policymakers, researchers, and producers. By leveraging the capabilities of remote sensing, it will enhance conservation agriculture contribution to long-term environmental sustainability and agricultural resilience. Keywords: Remote sensing technologies, Conservation agriculture, Winter cover crops, Voluntary adoption, Cropping patterns, Sustainable agricultural practice

Transfer Learning of Deep Learning Models for Cloud Masking in Optical Satellite Images

Los satélites de observación de la Tierra proporcionan una oportunidad sin precedentes para monitorizar nuestro planeta a alta resolución tanto espacial como temporal. Sin embargo, para procesar toda esta cantidad creciente de datos, necesitamos desarrollar modelos rápidos y precisos adaptados a las características específicas de los datos de cada sensor. Para los sensores ópticos, detectar las nubes en la imagen es un primer paso inevitable en la mayoría de aplicaciones tanto terrestres como oceánicas. Aunque detectar nubes brillantes y opacas es relativamente fácil, identificar automáticamente nubes delgadas semitransparentes o diferenciar nubes de nieve o superficies brillantes es mucho más difícil. Además, en el escenario actual, donde el número de sensores en el espacio crece constantemente, desarrollar metodologías para transferir modelos que funcionen con datos de nuevos satélites es una necesidad urgente. Por tanto, los objetivos de esta tesis son desarrollar modelos precisos de detección de nubes que exploten las diferentes propiedades de las imágenes de satélite y desarrollar metodologías para transferir esos modelos a otros sensores. La tesis está basada en cuatro trabajos los cuales proponen soluciones a estos problemas. En la primera contribución, "Multitemporal cloud masking in the Google Earth Engine", implementamos un modelo de detección de nubes multitemporal que se ejecuta en la plataforma Google Earth Engine y que supera los modelos operativos de Landsat-8. La segunda contribución, "Transferring deep learning models for Cloud Detection between Landsat-8 and Proba-V", es un caso de estudio de transferencia de un algoritmo de detección de nubes basado en aprendizaje profundo de Landsat-8 (resolución 30m, 12 bandas espectrales y muy buena calidad radiométrica) a Proba-V, que tiene una resolución de 333m, solo cuatro bandas y una calidad radiométrica peor. El tercer artículo, "Cross sensor adversarial domain adaptation of Landsat-8 and Proba-V images for cloud detection", propone aprender una transformación de adaptación de dominios que haga que las imágenes de Proba-V se parezcan a las tomadas por Landsat-8 con el objetivo de transferir productos diseñados con datos de Landsat-8 a Proba-V. Finalmente, la cuarta contribución, "Towards global flood mapping onboard low cost satellites with machine learning", aborda simultáneamente la detección de inundaciones y nubes con un único modelo de aprendizaje profundo, implementado para que pueda ejecutarse a bordo de un CubeSat (ϕSat-I) con un chip acelerador de aplicaciones de inteligencia artificial. El modelo está entrenado en imágenes Sentinel-2 y demostramos cómo transferir este modelo a la cámara del ϕSat-I. Este modelo se lanzó en junio de 2021 a bordo de la misión WildRide de D-Orbit para probar su funcionamiento en el espacio.Remote sensing sensors onboard Earth observation satellites provide a great opportunity to monitor our planet at high spatial and temporal resolutions. Nevertheless, to process all this ever-growing amount of data, we need to develop fast and accurate models adapted to the specific characteristics of the data acquired by each sensor. For optical sensors, detecting the clouds present in the image is an unavoidable first step for most of the land and ocean applications. Although detecting bright and opaque clouds is relatively easy, automatically identifying thin semi-transparent clouds or distinguishing clouds from snow or bright surfaces is much more challenging. In addition, in the current scenario where the number of sensors in orbit is constantly growing, developing methodologies to transfer models across different satellite data is a pressing need. Henceforth, the overreaching goal of this Thesis is to develop accurate cloud detection models that exploit the different properties of the satellite images, and to develop methodologies to transfer those models across different sensors. The four contributions of this Thesis are stepping stones in that direction. In the first contribution,"Multitemporal cloud masking in the Google Earth Engine", we implemented a lightweight multitemporal cloud detection model that runs on the Google Earth Engine platform and which outperforms the operational models for Landsat-8. The second contribution, "Transferring deep learning models for Cloud Detection between Landsat-8 and Proba-V", is a case-study of transferring a deep learning based cloud detection algorithm from Landsat-8 (30m resolution, 12 spectral bands and very good radiometric quality) to Proba-V, which has a lower{333m resolution, only four bands and a less accurate radiometric quality. The third paper, "Cross sensor adversarial domain adaptation of Landsat-8 and Proba-V images for cloud detection", proposes a learning-based domain adaptation transformation of Proba-V images to resemble those taken by Landsat-8, with the objective of transferring products designed on Landsat-8 to Proba-V. Finally, the fourth contribution, "Towards global flood mapping onboard low cost satellites with machine learning", tackles simultaneously cloud and flood water detection with a single deep learning model, which was implemented to run onboard a CubeSat (ϕSat-I) with an AI accelerator chip. In this case, the model is trained on Sentinel-2 and transferred to theϕSat-I camera. This model was launched in June 2021 onboard the Wild Ride D-Orbit mission in order to test its performance in space

Examining spatiotemporal changes in the phenology of Australian mangroves using satellite imagery

Nicolás Younes investigated the phenology of Australian mangroves using satellite imagery, field data, and generalized additive models. He found that satellite-derived phenology changes with location, frequency of observation, and spatial resolution. Nicolás challenges the common methods for detecting phenology and proposes a data-driven approach

MULTI-SCALE REMOTE SENSING OF OPEN SURFACE WATER BODY AREA AND QUALITY

Water is one of the most important resources for life. Climate change and climate variability have caused dramatic variations and significant trends in surface water resources, while global population growth and increased food demand have greatly stressed and modified global surface water systems. These changes in surface water resources have huge consequences to human society, natural environment, and global biodiversity. Landsat satellites have scanned the entire earth in every 16 days since the 1980s. The historical information of surface water body spatial distribution, temporal variation, and multi-decadal trends documented in remote sensing images can aid in water resource research, planning, and management, yet it is not well explored. This dissertation aims to develop algorithms and generate open surface water body maps at state, national, and global scales. Based on these maps, the interannual variations and long-term trends of surface water body area were analyzed while their climatic and anthropogenic drivers were examined. The joint analysis of both surface water body area and land water storage was carried out to explore the consistency and divergence between surface and land water resource dynamics. The potential of satellite images in water chlorophyll-a concentration estimation was also evaluated. Chapter 2 used ~16,000 Landsat images to analyze surface water body dynamics in Oklahoma and found significant decreasing trends in both surface water body area (the maximum, year-long, seasonal, and average water body area) and water body number (maximum and year-long water body numbers) during 1984–2015. The decrease of water body area was mainly attributed to the shrinking of large water bodies (>1 km2) while the decrease in water body number was mainly caused by the vanishing of some small water bodies. Smaller water bodies have a higher risk of drying up under climate-warming scenarios. Chapter 3 used ~ 370,000 Landsat images and the Gravity Recovery and Climate Experiment (GRACE) land water storage data to analyze changes in surface water area and groundwater across the contiguous US (CONUS) during 1984–2016. Divergent trends of surface water area were found across the CONUS with water-poor regions of the Southwest and Northwest US getting poorer, while the water-rich regions of the Southeast US and far north Great Plains getting richer. In the 2012-2014 prolonged droughts, surface water body shrinkage had led to massive groundwater mining and the rapid decline of land water storage in California and the Southern Great Plains. Chapter 4 used ~3.8 million Landsat images and GRACE land water storage data to analyze surface water area and land water storage jointly during 1984–2017 at 0.01° grid cells, 0.5° grid cells, and 5° tiles across the globe. About 8.5 million 0.01° grid cells had significant increasing or decreasing trends in surface water area over the past decades, forming interesting spatial patterns in northern Greenland, Tibetan Plateau, western US, the Great Lakes, Gulf of Bothnia, central South America, etc. The interannual variations, magnitude of variability, and the multi-decadal trends of regional surface water area were analyzed and visualized at 5° tiles. Divergent trends between land water storage and regional surface water area occurred in Greenland, China, the Indus Basin, and central Africa, mainly driven by climate and anthropogenic activities. Chapter 5 used ~10,000 chlorophyll-a field measurements to evaluate the potential of Landsat 5/7/8 and Sentinel 2 satellite images in water chlorophyll-a content estimation. Regression models of Landsat data have different performance in various water sampling sites and water bodies across Oklahoma, with relatively good performance in Eufaula Lake, Keystone Lake, Copan Lake, Hugo Lake, Foss Reservoir, and Atoka Reservoir. The brightness temperature band of Landsat satellites showed great potential in chlorophyll-a estimation, which indicated that temperature was among the most important factors of algal blooms in Oklahoma. The Red Edge 2 band of Sentinel 2 Satellite also showed great potential in chlorophyll-a estimation among different water sampling sites and water bodies across Oklahoma. The findings in this dissertation can be used in water resource research, planning, and management in coping with water scarcity and food security associated with climate change and population growth

Multi-sensor Evolution Analysis: an advanced GIS for interactive time series analysis and modelling based on satellite data

Archives of Earth remote sensing data, acquired from orbiting satellites, contain large amounts of information that can be used both for research activities and decision support. Thematic categorization is one method to extract from satellite data meaningful information that humans can directly comprehend. An interactive system that permits to analyse geo-referenced thematic data and its evolution over time is proposed as a tool to efficiently exploit such vast and growing amount of data. This thesis describes the approach used in building the system, the data processing methodology, details architectural elements and graphical interfaces. Finally, this thesis provides an evaluation of potential uses of the features provided, performance levels and usability of an implementation hosting an archive of 15 years moderate resolution (1 Km, from the ATSR instrument) thematic data

Evaluation of Sentinel-1, SMAP and SMOS surface soil moisture products for distributed eco-hydrological modelling in Mediterranean forest basins

[EN] Reliable distributed hydrological modeling, especially in semi-arid areas, must consider the inclusion of surface soil moisture (SSM) spatial information during the calibration process. This variable plays a key role in the evapotranspiration processes that determine the hydrological cycle. The coarse resolution of the SSM estimates by satellite remote sensing has restricted the application of this approach to only large basins, focusing most of the studies in the consideration of simply the temporal dynamics of this variable. The growing efforts in providing higher spatial resolution through disaggregating methodologies or new sensor estimates facilitates the application of this spatial approach to small basins. This paper explores the applicability of the currently available satellite surface soil moisture estimates for distributed eco-hydrological modelling in Mediterranean forest basins. On one hand, this study contributes to fill the existing research gap on the use of remote sensing SSM spatial patterns within the distributed hydrological modelling framework in small basins. On the other hand, it serves as an indirect validation method for the spatial performance of satellite SSM products. To achieve this goal, we implemented the eco-hydrological model TETIS in three case studies named: Hozgarganta (southern Spain), Ceira (western Portugal) and Carraixet (eastern Spain). The SSM estimates selected for comparison were Sentinel-1 SSM provided by the Copernicus Global Land Services (CGLS), SMAP SSM disaggregated using Sentinel-1 (SPL2SMAP_S) provided by the National Aeronautics and Space Administration (NASA), SMOS SSM provided by the Barcelona Expert Center (BEC), and SMOS and SMAP SSM disaggregated using the DISPATCH algorithm provided by Lobelia Earth. The methodology employed involved a multi-objective and multi-variable calibration in terms of remote sensing SSM spatial patterns and in-situ streamflow, using the Spatial Efficiency Metric (SPAEF) and the Nash-Sutcliffe efficiency index (NSE) respectively. Before model calibration a sensitivity analysis of the most influent variables was performed. The temporal and spatial comparison of the reference SSM products revealed inconsistencies amongst products. The disaggregating methodology determined the spatial agreement to a greater degree than the sensor itself (i.e. SMAP, SMOS). In spite of the differences amongst products, the multi-objective calibration approach proposed increased the robustness of the hydrological modelling.This study was founded by the Spanish AEI within the program WaterJPI through the project iAqueduct (PCI2019-103729) , by the EC Life project ResilientForests (LIFE17 CCA/ES/000063) , and by the project Water4Cast funded by Generalitat Valenciana (PROMETEO/2021/074) . We also acknowledge the following hydrometeorological data providers institutions: SiAR, SAIH-HIDROSUR, SAIH Jucar and SNIRH.Gomis-Cebolla, J.; Garcia-Arias, A.; Perpinyà-Vallès, M.; Francés, F. (2022). Evaluation of Sentinel-1, SMAP and SMOS surface soil moisture products for distributed eco-hydrological modelling in Mediterranean forest basins. Journal of Hydrology. 608:1-19. https://doi.org/10.1016/j.jhydrol.2022.12756911960

Global Monitoring for Security and Stability (GMOSS) - Integrated Scientific and Technological Research Supporting Security Aspects of the European Union

This report is a collection of scientific activities and achievements of members of the GMOSS Network of Excellence during the period March 2004 to November 2007. Exceeding the horizon of classical remote-sensing-focused projects, GMOSS is characterized by the integration of political and social aspects of security with the assessment of remote sensing capabilities and end-users support opportunities. The report layout reflects the work breakdown structure of GMOSS and is divided into four parts. Part I Concepts and Integration addresses the political background of European Security Policy and possibilities for Earth Observation technologies for a contribution. Besides it illustrates integration activities just as the GMOSS Gender Action Plan or a description of the GMOSS testcases. Part II of this book presents various Application activities conducted by the network partners. The contributions vary from pipeline sabotage analysis in Iraq to GIS studies about groundwater vulnerability in Gaza Strip, from Population Monitoring in Zimbabwe to Post-Conflict Urban Reconstruction Assessments and many more. Part III focuses on the research and development of image processing methods and Tools. The themes range from SAR interferometry for the measurement of Surface Displacement to Robust Satellite Techniques for monitoring natural hazards like volcanoes and earthquakes. Further subjects are the 3D detection of buildings in VHR imagery or texture analysis techniques on time series of satellite images with variable illumination and many other more. The report closes with Part IV. In the chapter ¿The Way Forward¿ a review on four years of integrated work is done. Challenges and achievements during this period are depicted. It ends with an outlook about a possible way forward for integrated European security research.JRC.G.2-Support to external securit

A Deep Learning Framework in Selected Remote Sensing Applications

The main research topic is designing and implementing a deep learning framework applied to remote sensing. Remote sensing techniques and applications play a crucial role in observing the Earth evolution, especially nowadays, where the effects of climate change on our life is more and more evident. A considerable amount of data are daily acquired all over the Earth. Effective exploitation of this information requires the robustness, velocity and accuracy of deep learning. This emerging need inspired the choice of this topic. The conducted studies mainly focus on two European Space Agency (ESA) missions: Sentinel 1 and Sentinel 2. Images provided by the ESA Sentinel-2 mission are rapidly becoming the main source of information for the entire remote sensing community, thanks to their unprecedented combination of spatial, spectral and temporal resolution, as well as their open access policy. The increasing interest gained by these satellites in the research laboratory and applicative scenarios pushed us to utilize them in the considered framework. The combined use of Sentinel 1 and Sentinel 2 is crucial and very prominent in different contexts and different kinds of monitoring when the growing (or changing) dynamics are very rapid. Starting from this general framework, two specific research activities were identified and investigated, leading to the results presented in this dissertation. Both these studies can be placed in the context of data fusion. The first activity deals with a super-resolution framework to improve Sentinel 2 bands supplied at 20 meters up to 10 meters. Increasing the spatial resolution of these bands is of great interest in many remote sensing applications, particularly in monitoring vegetation, rivers, forests, and so on. The second topic of the deep learning framework has been applied to the multispectral Normalized Difference Vegetation Index (NDVI) extraction, and the semantic segmentation obtained fusing Sentinel 1 and S2 data. The S1 SAR data is of great importance for the quantity of information extracted in the context of monitoring wetlands, rivers and forests, and many other contexts. In both cases, the problem was addressed with deep learning techniques, and in both cases, very lean architectures were used, demonstrating that even without the availability of computing power, it is possible to obtain high-level results. The core of this framework is a Convolutional Neural Network (CNN). {CNNs have been successfully applied to many image processing problems, like super-resolution, pansharpening, classification, and others, because of several advantages such as (i) the capability to approximate complex non-linear functions, (ii) the ease of training that allows to avoid time-consuming handcraft filter design, (iii) the parallel computational architecture. Even if a large amount of "labelled" data is required for training, the CNN performances pushed me to this architectural choice.} In our S1 and S2 integration task, we have faced and overcome the problem of manually labelled data with an approach based on integrating these two different sensors. Therefore, apart from the investigation in Sentinel-1 and Sentinel-2 integration, the main contribution in both cases of these works is, in particular, the possibility of designing a CNN-based solution that can be distinguished by its lightness from a computational point of view and consequent substantial saving of time compared to more complex deep learning state-of-the-art solutions