A review of satellite-based global agricultural monitoring systems available for Africa

Abstract The increasing frequency and severity of extreme climatic events and their impacts are being realized in many regions of the world, particularly in smallholder crop and livestock production systems in Sub-Saharan Africa (SSA). These events underscore the need for timely early warning. Satellite Earth Observation (EO) availability, rapid developments in methodology to archive and process them through cloud services and advanced computational capabilities, continue to generate new opportunities for providing accurate, reliable, and timely information for decision-makers across multiple cropping systems and for resource-constrained institutions. Today, systems and tools that leverage these developments to provide open access actionable early warning information exist. Some have already been employed by early adopters and are currently operational in selecting national monitoring programs in Angola, Kenya, Rwanda, Tanzania, and Uganda. Despite these capabilities, many governments in SSA still rely on traditional crop monitoring systems, which mainly rely on sparse and long latency in situ reports with little to no integration of EO-derived crop conditions and yield models. This study reviews open-access operational agricultural monitoring systems available for Africa. These systems provide the best-available open-access EO data that countries can readily take advantage of, adapt, adopt, and leverage to augment national systems and make significant leaps (timeliness, spatial coverage and accuracy) of their monitoring programs. Data accessible (vegetation indices, crop masks) in these systems are described showing typical outputs. Examples are provided including crop conditions maps, and damage assessments and how these have integrated into reporting and decision-making. The discussion compares and contrasts the types of data, assessments and products can expect from using these systems. This paper is intended for individuals and organizations seeking to access and use EO to assess crop conditions who might not have the technical skill or computing facilities to process raw data into informational products

strengthening agricultural decisions in countries at risk of food insecurity the geoglam crop monitor for early warning

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Earth observations into action: the systemic integration of earth observation applications into national risk reduction decision structures

Purpose - As stated in the United Nations Global Assessment Report 2022 Concept Note, decision-makers everywhere need data and statistics that are accurate, timely, sufficiently disaggregated, relevant, accessible and easy to use. The purpose of this paper is to demonstrate scalable and replicable methods to advance and integrate the use of earth observation (EO), specifically ongoing efforts within the Group on Earth Observations (GEO) Work Programme and the Committee on Earth Observation Satellites (CEOS) Work Plan, to support risk-informed decision-making, based on documented national and subnational needs and requirements. Design/methodology/approach - Promotion of open data sharing and geospatial technology solutions at national and subnational scales encourages the accelerated implementation of successful EO applications. These solutions may also be linked to specific Sendai Framework for Disaster Risk Reduction (DRR) 2015–2030 Global Targets that provide trusted answers to risk-oriented decision frameworks, as well as critical synergies between the Sendai Framework and the 2030 Agenda for Sustainable Development. This paper provides examples of these efforts in the form of platforms and knowledge hubs that leverage latest developments in analysis ready data and support evidence-based DRR measures. Findings - The climate crisis is forcing countries to face unprecedented frequency and severity of disasters. At the same time, there are growing demands to respond to policy at the national and international level. EOs offer insights and intelligence for evidence-based policy development and decision-making to support key aspects of the Sendai Framework. The GEO DRR Working Group and CEOS Working Group Disasters are ideally placed to help national government agencies, particularly national Sendai focal points to learn more about EOs and understand their role in supporting DRR. Originality/value - The unique perspective of EOs provide unrealized value to decision-makers addressing DRR. This paper highlights tangible methods and practices that leverage free and open source EO insights that can benefit all DRR practitioners

AGRICULTURAL LAND USE, DROUGHT IMPACTS AND VULNERABILITY: A REGIONAL CASE STUDY FOR KARAMOJA, UGANDA

The increasing frequency of extreme climate events brings into question the sustainability of agriculture in marginal lands, especially those already experiencing drought such as the Karamoja region in northeastern Uganda. A significant amount of research often qualitative has been conducted documenting drought and its impact on Karamoja. Taking a mixed methods approach, this study combined remotely-sensed satellite data, national agricultural surveys, census, and field data to expand on empirical knowledge on agricultural drought, land use and human perceptions of drought necessary for comprehensive drought forecasting, monitoring, and management. Results from this study showed that Karamoja is at least twice more vulnerable to drought than any other region in Uganda. This is because of its very low adaptive capacity in part due to high poverty rates and a higher dependency on the natural environment for livelihood. Analysis of satellite data quantified a 229 percent increase in cropland area in Karamoja between 2000 and 2011/12, driven largely by agricultural development programs. Underlying forces (e.g., cropland expansion programs and controlled grazing) originating from land use policy and development programs, more than proximate causes (direct local level actions) remain the major drivers of this expansion. Although the cultivated area has dramatically increased, there is no quantifiable overall increase in yield or per-capita production as evidenced by the recurrent poor food security. This status quo, (poor yields and dependence on food aid) is likely to continue as more land is put to crop cultivation by poor households and meager investments are made in livestock-based livelihood opportunities. The cropland area mask developed in this research facilitated the characterization of drought within agricultural areas. The drought information developed by this study is spatially and temporally explicit, showing differences in severity between years and between districts. Overall Abim District showed the least variation and is the least impacted while, Moroto District had the highest inter-annual variability and was often the most severely impacted. This research presents an approach to predict the number of people who would require food aid during the lean season in Karamoja (December to March) within a reasonable margin of error (less than 10\%) at the peak of the growing season (August/September), although the need for more extensive testing is recognized. The method takes advantage of readily available satellite data and can contribute to planning for a timely and appropriate response. A case study of farmer's perceptions of drought in Moroto District found that many farmers feel helpless and have no control of their future. For the majority of farmers in the district, past experiences of drought do not necessarily impact on future expectations of drought and many have no long-term adjustment plans. Quite often the majority of the population depends on emergency food assistance, building a culture of dependency. The analysis indicates that factors such as; conflict (insecurity) and interventions by government and international agencies intermingle with culture to have a profound direct influence on farmers' perception of drought amongst communities in Moroto district. This research shows that satellite data can provide the much-needed information to fill the gaps that inhibit long-term drought monitoring, at a significantly lower cost than traditional climate station-based monitoring in data scarce regions like Karamoja. It also points to a way forward for proactive assessment, planning, and response

Considerations for AI-EO for agriculture in Sub-Saharan Africa

Adapting to and mitigating climate change while addressing food insecurity are top priorities in SubSaharan Africa that require technologies to improve rural livelihoods with minimal environmental costs [1]. Artificial intelligence (AI) offers great promise for climate-smart solutions that improve food security outcomes. While precision agriculture is often the foremost use case for AI in agriculture (e.g. automation of farm equipment or nutrient application), precision agriculture is out of reach for most African farmers due to the required capital and infrastructure. AI solutions using satellite Earth observations (EOs), which we call AI-EO, are more accessible in the near term. EO enables agricultural analyses and insights at global scales, and many datasets are freely available, making EO-based solutions affordable [2]. AI-EO-derived products such as crop type maps and yield estimates are necessary to forecast food production surpluses or deficits, inform trade, and aid decisions. These products can support policies that accelerate the design and adoption of climate-smart agriculture and impact farmer livelihoods by increasing access to actionable early warning, risk financing or insurance [3], farm inputs, markets, and costreducing interventions [2, 4]. Despite their promise, AI-EO solutions for agriculture in Africa are still limited. Most techniques are not generalizable across heterogeneous landscapes. In this paper, we describe the principal sub-fields of research in AI-EO for agriculture in Africa and discuss examples and limitations of existing work. We also propose ten considerations for future work to help increase the impact of AI-EO research in Africa.https://doi.org/10.1088/1748-9326/acc47

OpenMapFlow: A Library for Rapid Map Creation with Machine Learning and Remote Sensing Data

The desired output for most real-world tasks using machine learning (ML) and remote sensing data is a set of dense predictions that form a predicted map for a geographic region. However, most prior work involving ML and remote sensing follows the traditional practice of reporting metrics on a set of independent, geographically-sparse samples and does not perform dense predictions. To reduce the labor of producing dense prediction maps, we present OpenMapFlow---an open-source python library for rapid map creation with ML and remote sensing data. OpenMapFlow provides 1) a data processing pipeline for users to create labeled datasets for any region, 2) code to train state-of-the-art deep learning models on custom or existing datasets, and 3) a cloud-based architecture to deploy models for efficient map prediction. We demonstrate the benefits of OpenMapFlow through experiments on three binary classification tasks: cropland, crop type (maize), and building mapping. We show that OpenMapFlow drastically reduces the time required for dense prediction compared to traditional workflows. We hope this library will stimulate novel research in areas such as domain shift, unsupervised learning, and societally-relevant applications and lessen the barrier to adopting research methods for real-world tasks