CubeSat constellations provide enhanced crop phenology and digital agricultural insights using daily leaf area index retrievals

Satellite remote sensing has great potential to deliver on the promise of a data-driven agricultural revolution, with emerging space-based platforms providing spatiotemporal insights into precisionlevel attributes such as crop water use, vegetation health and condition and crop response to management practices. Using a harmonized collection of high-resolution Planet CubeSat, Sentinel-2, Landsat-8 and additional coarser resolution imagery from MODIS and VIIRS, we exploit a multisatellite data fusion and machine learning approach to deliver a radiometrically calibrated and gap-filled time-series of daily leaf area index (LAI) at an unprecedented spatial resolution of 3 m. The insights available from such high-resolution CubeSat-based LAI data are demonstrated through tracking the growth cycle of a maize crop and identifying observable within-field spatial and temporal variations across key phenological stages. Daily LAI retrievals peaked at the tasseling stage, demonstrating their value for fertilizer and irrigation scheduling. An evaluation of satellite-based retrievals against field-measured LAI data collected from both rain-fed and irrigated fields shows high correlation and captures the spatiotemporal development of intra- and inter-field variations. Novel agricultural insights related to individual vegetative and reproductive growth stages were obtained, showcasing the capacity for new high-resolution CubeSat platforms to deliver actionable intelligence for precision agricultural and related applications

Detecting and mapping forest nutrient deficiencies: eucalyptus variety (Eucalyptus grandis x and Eucalyptus urophylla) trees in KwaZulu-Natal, South Africa.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Abstract available in PDF

Use of remote sensing approaches for agricultural applications

Doctor of PhilosophyDepartment of AgronomyIgnacio CiampittiRemote sensing is a technology that has been utilized extensively in agriculture due to its capacity to provide precise and detailed information on various aspects of agricultural production. Farmers and researchers have utilized this technology to gain valuable insights regarding, among other things, crop phenology, yield prediction, land classification, soil quality, water management, and environmental monitoring. The present dissertation is structured into six chapters, with the first serving as an introduction to remote sensing technology in agriculture and the last chapter offering concluding remarks. The remaining chapters delve into various applications of remote sensing technology in agriculture. The second and third chapters examine the potential of remote sensing to classify maize phenology in Kansas, utilizing three distinct image resolutions. The second chapter identifies the optimal combination of spectral bands, vegetation indices, and weather data for phenology classification using Landsat 8 as a source of spectral information.  In chapter three, greater temporal and spatial resolution was  tested using Sentinel-2 and Planet Fusion, and the classification performance of both sources was compared. The model was tested in different areas, and the results emphasized the significance of temporal and spatial resolution for traits like phenology that can change rapidly. Chapter four explores the use of remote sensing technology to identify areas in Cambodia with traditional management practices where conservation agriculture could play a critical role. The study employs 3-meter daily imagery from Planet Fusion and image segmentation tools to distinguish between burned patches and bare soil after ploughing. The results demonstrate that these images, in conjunction with image segmentation tools, have the potential to identify management practices in areas where obtaining ground-truth data could be challenging.  Finally, chapter five discusses the current state of the art and the necessary changes to integrate soil science methods and remote sensing for determining soil organic carbon. This chapter examines the challenges and opportunities associated with using remote sensing to monitor soil properties and offers viable solutions for bridging these two domains

Multi-temporal assessment of diversity and condition in UK semi-natural grasslands using optical reflectance

With 40% of the world’s plants estimated to be under threat of extinction and ever lowering levels of ecological intactness of biological systems, the requirement to effectively monitor plant species and diversity has never been more pressing. Globally, natural, and semi-natural grassland ecosystems are at particular risk of degradation and conversion. Semi-natural grasslands in the UK currently make up about 1-2% of the permanent lowland grassland cover. Once degraded due to agricultural additions or inappropriate management, they can be difficult and costly to restore. As these systems display high levels of plant and invertebrate diversity, there is a need to safeguard their decline. However, there are currently significant challenges to providing the data needed to assess the condition of these systems. Remote sensing could contribute by providing information on herbaceous plant diversity and vegetation state across a wide range of spatial scales and time. Optical traits are a subset of plant traits that are detectable using reflectance data from leaf to canopy scales, dependent on the configuration of the sensor employed and can be linked to taxonomic diversity and condition of vegetation. Very high spatial resolution hyperspectral imaging technologies are, for the first time, enabling in-situ grassland plant phenotyping at the leaf, individual and high-resolution canopy scale. Analyses of these spectra have demonstrated promising results in application of mapping of taxonomic units and diversity metrics. However there is little evidence of the temporal stability of these observations. At the landscape scale, openly available, higher spatial resolution satellite data is also enabling examination of smaller field parcels, which are typical of UK fragmented landscapes. In this context, spectral time-series have the potential to be used to predict the condition of vegetation communities of conservation interest. In this thesis, the use of optical remote sensing data to further our understanding of semi-natural grasslands and to safeguard their decline, is examined, with a particular focus on the exploitation of multi-temporal sampling. Firstly, spectral variation in space, as a surrogate measure for species or community type diversity (also known as the spectral variation hypothesis), is assessed via a meta-analysis of existing studies. The results of the synthesis reveal some promise for the approach, but a large amount of variation between study outcomes is observed, suggesting that methodological approaches are important in the effectiveness of the proxy. Secondly, spectral data is collected alongside botanical and phenological diversity data at high spatial resolution over a growing season to test the stability of the spectral variation hypothesis over time. The results of these experiments show that the ability to detect biodiversity using this method is seasonally, and possibly, site dependent. Next, the suitability of hyperspectral leaf reflectance for distinguishing 17 herbaceous species growing within a calcareous grassland is examined. The application of machine learning classification models to multi-temporal leaf spectra show that although species are distinguishable at most sampling times within the year, the transferability of these models is very limited between sampling dates. Finally satellite time-series of vegetation indices are used to predict favourable or unfavourable vegetation condition criteria in calcareous fields across two years. A number of indices were successful in distinguishing between the different condition criteria but there was variation in results found between the two years sampled, due to differences in intra-annual vegetation phenology. Overall the results of this thesis, show promise for remote sensing of grassland biodiversity and condition. Both high spatial resolution hyperspectral data, as well as coarser resolution multi-spectral data sets, can be useful in evaluation of these systems. However, the dynamic nature of leaves and canopies over time, will require a multi-temporal approach to model building, which should be an integral part of developing these methods in the future

Emerging Technologies

This monograph investigates a multitude of emerging technologies including 3D printing, 5G, blockchain, and many more to assess their potential for use to further humanity’s shared goal of sustainable development. Through case studies detailing how these technologies are already being used at companies worldwide, author Sinan Küfeoğlu explores how emerging technologies can be used to enhance progress toward each of the seventeen United Nations Sustainable Development Goals and to guarantee economic growth even in the face of challenges such as climate change. To assemble this book, the author explored the business models of 650 companies in order to demonstrate how innovations can be converted into value to support sustainable development. To ensure practical application, only technologies currently on the market and in use actual companies were investigated. This volume will be of great use to academics, policymakers, innovators at the forefront of green business, and anyone else who is interested in novel and innovative business models and how they could help to achieve the Sustainable Development Goals. This is an open access book