Personalizing Sustainable Agriculture with Causal Machine Learning

To fight climate change and accommodate the increasing population, global crop production has to be strengthened. To achieve the "sustainable intensification" of agriculture, transforming it from carbon emitter to carbon sink is a priority, and understanding the environmental impact of agricultural management practices is a fundamental prerequisite to that. At the same time, the global agricultural landscape is deeply heterogeneous, with differences in climate, soil, and land use inducing variations in how agricultural systems respond to farmer actions. The "personalization" of sustainable agriculture with the provision of locally adapted management advice is thus a necessary condition for the efficient uplift of green metrics, and an integral development in imminent policies. Here, we formulate personalized sustainable agriculture as a Conditional Average Treatment Effect estimation task and use Causal Machine Learning for tackling it. Leveraging climate data, land use information and employing Double Machine Learning, we estimate the heterogeneous effect of sustainable practices on the field-level Soil Organic Carbon content in Lithuania. We thus provide a data-driven perspective for targeting sustainable practices and effectively expanding the global carbon sink.Comment: Accepted for publication and spotlight presentation at Tackling Climate Change with Machine Learning: workshop at NeurIPS 202

Cloud gap-filling with deep learning for improved grassland monitoring

Uninterrupted optical image time series are crucial for the timely monitoring of agricultural land changes. However, the continuity of such time series is often disrupted by clouds. In response to this challenge, we propose a deep learning method that integrates cloud-free optical (Sentinel-2) observations and weather-independent (Sentinel-1) Synthetic Aperture Radar (SAR) data, using a combined Convolutional Neural Network (CNN)-Recurrent Neural Network (RNN) architecture to generate continuous Normalized Difference Vegetation Index (NDVI) time series. We emphasize the significance of observation continuity by assessing the impact of the generated time series on the detection of grassland mowing events. We focus on Lithuania, a country characterized by extensive cloud coverage, and compare our approach with alternative interpolation techniques (i.e., linear, Akima, quadratic). Our method surpasses these techniques, with an average MAE of 0.024 and R^2 of 0.92. It not only improves the accuracy of event detection tasks by employing a continuous time series, but also effectively filters out sudden shifts and noise originating from cloudy observations that cloud masks often fail to detect

Evaluating Digital Tools for Sustainable Agriculture using Causal Inference

In contrast to the rapid digitalization of several industries, agriculture suffers from low adoption of climate-smart farming tools. Even though AI-driven digital agriculture can offer high-performing predictive functionalities, it lacks tangible quantitative evidence on its benefits to the farmers. Field experiments can derive such evidence, but are often costly and time consuming. To this end, we propose an observational causal inference framework for the empirical evaluation of the impact of digital tools on target farm performance indicators. This way, we can increase farmers' trust by enhancing the transparency of the digital agriculture market, and in turn accelerate the adoption of technologies that aim to increase productivity and secure a sustainable and resilient agriculture against a changing climate. As a case study, we perform an empirical evaluation of a recommendation system for optimal cotton sowing, which was used by a farmers' cooperative during the growing season of 2021. We leverage agricultural knowledge to develop a causal graph of the farm system, we use the back-door criterion to identify the impact of recommendations on the yield and subsequently estimate it using several methods on observational data. The results show that a field sown according to our recommendations enjoyed a significant increase in yield (12% to 17%).Comment: Accepted for publication and spotlight presentation at Tackling Climate Change with Machine Learning: workshop at NeurIPS 202

InSAR Campaign Reveals Ongoing Displacement Trends at High Impact Sites of Thessaloniki and Chalkidiki, Greece

We studied the broader area of Thessaloniki in northern Greece and Chalkidiki and performed an InSAR campaign to study the surface deformation phenomena that have been known to exist for at least two decades. Sentinel-1 data (2015–2019) together with drill measurements were exploited to focus on specific sites of interest. Our results indicate an ongoing displacement field. At the region of Kalochori and Sindos—where intense subsidence in the 1990s was previously found to have had a natural surface rebound in the 2000s—a new period of subsidence, caused by the enlivenment of the groundwater overexploitation, was reported. The uplifting trend of Oreokastro is still active and subsidence in Anthemountas graben is ongoing; special focus was set on the Makedonia Airport, where significant displacement is occurring. The study also reveals a new area at Nea Moudania, that was not known previously to deform; another case corresponding to anthropogenic-induced surface displacement. Thessaloniki is surrounded by different persistent displacement phenomena, whose main driving mechanisms are anthropogenic. The sensitivity of the surface displacements to the water trends is highlighted in parts of the study area. Results highlight the plan of a water resources management as a high priority for the area

Causality and Explainability for Trustworthy Integrated Pest Management

Pesticides serve as a common tool in agricultural pest control but significantly contribute to the climate crisis. To combat this, Integrated Pest Management (IPM) stands as a climate-smart alternative. Despite its potential, IPM faces low adoption rates due to farmers' skepticism about its effectiveness. To address this challenge, we introduce an advanced data analysis framework tailored to enhance IPM adoption. Our framework provides i) robust pest population predictions across diverse environments with invariant and causal learning, ii) interpretable pest presence predictions using transparent models, iii) actionable advice through counterfactual explanations for in-season IPM interventions, iv) field-specific treatment effect estimations, and v) assessments of the effectiveness of our advice using causal inference. By incorporating these features, our framework aims to alleviate skepticism and encourage wider adoption of IPM practices among farmers.Comment: Accepted at NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning: Blending New and Existing Knowledge System

Earth-observation-based estimation and forecasting of particulate matter impact on solar energy in Egypt

This study estimates the impact of dust aerosols on surface solar radiation and solar energy in Egypt based on Earth Observation (EO) related techniques. For this purpose, we exploited the synergy of monthly mean and daily post processed satellite remote sensing observations from the MODerate resolution Imaging Spectroradiometer (MODIS), radiative transfer model (RTM) simulations utilizing machine learning, in conjunction with 1-day forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). As cloudy conditions in this region are rare, aerosols in particular dust, are the most common sources of solar irradiance attenuation, causing performance issues in the photovoltaic (PV) and concentrated solar power (CSP) plant installations. The proposed EO-based methodology is based on the solar energy nowcasting system (SENSE) that quantifies the impact of aerosol and dust on solar energy potential by using the aerosol optical depth (AOD) in terms of climatological values and day-to-day monitoring and forecasting variability from MODIS and CAMS, respectively. The forecast accuracy was evaluated at various locations in Egypt with substantial PV and CSP capacity installed and found to be within 5–12% of that obtained from the satellite observations, highlighting the ability to use such modelling approaches for solar energy management and planning (M&P). Particulate matter resulted in attenuation by up to 64–107 kWh/m2 for global horizontal irradiance (GHI) and 192–329 kWh/m2 for direct normal irradiance (DNI) annually. This energy reduction is climatologically distributed between 0.7% and 12.9% in GHI and 2.9% to 41% in DNI with the maximum values observed in spring following the frequent dust activity of Khamaseen. Under extreme dust conditions the AOD is able to exceed 3.5 resulting in daily energy losses of more than 4 kWh/m2 for a 10 MW system. Such reductions are able to cause financial losses that exceed the daily revenue values. This work aims to show EO capabilities and techniques to be incorporated and utilized in solar energy studies and applications in sun-privileged locations with permanent aerosol sources such as Egypt

Wildfire monitoring using satellite images, ontologies and linked geospatial data

Advances in remote sensing technologies have allowed us to send an ever-increasing number of satellites in orbit around Earth. As a result, Earth Observation data archives have been constantly increasing in size in the last few years, and have become a valuable source of data for many scientific and application domains. When Earth Observation data is coupled with other data sources many pioneering applications can be developed. In this paper we show how Earth Observation data, ontologies, and linked geospatial data can be combined for the development of a wildfire monitoring service that goes beyond applications currently deployed in various Earth Observation data centers. The service has been developed in the context of European project TELEIOS that faces the challenges of extracting knowledge from Earth Observation data head-on, capturing this knowledge by semantic annotation encoded using Earth Observation ontologies, and combining these annotations with linked geospatial data to allow the development of interesting applications

Satellite Earth Observation Data in Epidemiological Modeling of Malaria, Dengue and West Nile Virus: A Scoping Review

Earth Observation (EO) data can be leveraged to estimate environmental variables that influence the transmission cycle of the pathogens that lead to mosquito-borne diseases (MBDs). The aim of this scoping review is to examine the state-of-the-art and identify knowledge gaps on the latest methods that used satellite EO data in their epidemiological models focusing on malaria, dengue and West Nile Virus (WNV). In total, 43 scientific papers met the inclusion criteria and were considered in this review. Researchers have examined a wide variety of methodologies ranging from statistical to machine learning algorithms. A number of studies used models and EO data that seemed promising and claimed to be easily replicated in different geographic contexts, enabling the realization of systems on regional and national scales. The need has emerged to leverage furthermore new powerful modeling approaches, like artificial intelligence and ensemble modeling and explore new and enhanced EO sensors towards the analysis of big satellite data, in order to develop accurate epidemiological models and contribute to the reduction of the burden of MBDs

Application of Level Set Methods for burned area mapping and evaluation against DLR's TET-1 hotspot data - a case study in Portugal

Automated extraction of fire-affected areas from satellite images is a crucial task for large scale, near real-time damage assessment. The discrimination between burned and unburned pixels is usually done by differentiating pre-/post scenes and categorizing the change rate, or by using empirically derived thresholds on a single scene. These approaches, however, require the setting of a threshold value for the discrimination, which has to be derived empirically for constricted region of interest. It is therefore not well adoptable to different regions of interest, turning this approach inappropriate for automatic extraction of burned areas in large, heterogeneous study areas. The aim of this paper is to test the Active Contour Level Set method, which does not rely on any thresholds, regarding geometric accuracy of burned area extraction

Earth observation : An integral part of a smart and sustainable city

Over the course of the 21st century, a century in which the urbanization process of the previous one is ever on the rise, the novel smart city concept has rapidly evolved and now encompasses the broader aspect of sustainability. Concurrently, there has been a sea change in the domain of Earth observation (EO) where scientific and technological breakthroughs are accompanied by a paradigm shift in the provision of open and free data. While the urban and EO communities share the end goal of achieving sustainability, cities still lack an understanding of the value EO can bring in this direction, an next a consolidated framework for tapping the full potential of EO and integrating it in their operational modus operandi. The “SMart URBan Solutions for air quality, disasters and city growth” H2020 project (SMURBS/ERA-PLANET) sits at this scientific and policy crossroad, and, by creating bottom-up EO-driven solutions against an array of environmental urban pressures, and by expanding the network of engaged and exemplary smart cities that push the state-of-the-art in EO uptake, brings the international ongoing discussion of EO for sustainable cities closer to home and contributes in this discussion. This paper advocates for EO as an integral part of a smart and sustainable city and aspires to lead by example. To this end, it documents the project's impacts, ranging from the grander policy fields to an evolving portfolio of smart urban solutions and everyday city operations, as well as the cornerstones for successful EO integration. Drawing a parallel with the utilization of EO in supporting several aspects of the 2030 Agenda for Sustainable Development, it aspires to be a point of reference for upcoming endeavors of city stakeholders and the EO community alike, to tread together, beyond traditional monitoring or urban planning, and to lay the foundations for urban sustainability.Peer reviewe