Validation of sentinel-2 leaf area index (LAI) product derived from SNAP toolbox and its comparison with global LAI products in an African semi-arid agricultural landscape

This study validated SNAP-derived LAI from Sentinel-2 and its consistency with existing global LAI products. The validation and intercomparison experiments were performed on two processing levels, i.e., Top-of-Atmosphere and Bottom-of-Atmosphere reflectances and two spatial resolutions, i.e., 10 m, and 20 m. These were chosen to determine their effect on retrieved LAI accuracy and consistency. The results showed moderate R2, i.e., ~0.6 to ~0.7 between SNAPderived LAI and in-situ LAI, but with high errors, i.e., RMSE, BIAS, and MAE >2 m2 m–2 with marked differences between processing levels and insignificant differences between spatial resolutions. In contrast, inter-comparison of SNAP-derived LAI with MODIS and Proba-V LAI products revealed moderate to high consistencies, i. e., R2 of ~0.55 and ~0.8 respectively, and RMSE of ~0.5 m2 m–2 and ~0.6 m2 m–2, respectively. The results in this study have implications for future use of SNAP-derived LAI from Sentinel-2 in agricultural landscapes, suggesting its global applicability that is essential for large-scale agricultural monitoring. However, enormous errors in characterizing field-level LAI variability indicate that SNAP-derived LAI is not suitable for precision farming. In fact, from the study, the need for further improvement of LAI retrieval arises, especially to support farm-level agricultural management decisions

Earth Observation Strategies For Degradation Monitoring In South Africa With The Sentinels - Results From The Spaces II Saldi-Project

The overarching goal of SALDi (South African Land Degradation MonItor) is to implement novel, adaptive, and sustainable tools for assessing land degradation in multi-use landscapes in South Africa. This presentationdemonstrates results from hyper-temporal Sentinel-1 and -2 timeseries concerning woody cover mapping in complex savanna systems, invasive slangbos bushencroachment in grassland areas and regional soil moisture retrievals. Validation has been performed by cross-comparisons, field trips and permanently installed soil moisture networks

Estimating Crop Biophysical Parameters Using Machine Learning Algorithms and Sentinel-2 Imagery

Global food security is critical to eliminating hunger and malnutrition. In the changing climate, farmers in developing countries must adopt technologies and farming practices such as precision agriculture (PA). PA-based approaches enable farmers to cope with frequent and intensified droughts and heatwaves, optimising yields, increasing efficiencies, and reducing operational costs. Biophysical parameters such as Leaf Area Index (LAI), Leaf Chlorophyll Content (LCab), and Canopy Chlorophyll Content (CCC) are essential for characterising field-level spatial variability and thus are necessary for enabling variable rate application technologies, precision irrigation, and crop monitoring. Moreover, robust machine learning algorithms offer prospects for improving the estimation of biophysical parameters due to their capability to deal with non-linear data, small samples, and noisy variables. This study compared the predictive performance of sparse Partial Least Squares (sPLS), Random Forest (RF), and Gradient Boosting Machines (GBM) for estimating LAI, LCab, and CCC with Sentinel-2 imagery in Bothaville, South Africa and identified, using variable importance measures, the most influential bands for estimating crop biophysical parameters. The results showed that RF was superior in estimating all three biophysical parameters, followed by GBM which was better in estimating LAI and CCC, but not LCab, where sPLS was relatively better. Since all biophysical parameters could be achieved with RF, it can be considered a good contender for operationalisation. Overall, the findings in this study are significant for future biophysical product development using RF to reduce reliance on many algorithms for specific parameters, thus facilitating the rapid extraction of actionable information to support PA and crop monitoring activities

Assessment of the Characteristics of Recent Major Wildfires in the USA, Australia and Brazil in 2018–2019 Using Multi-Source Satellite Products

This study analysed the characteristics of the recent (2018–2019) wildfires that occurred in the USA, Brazil, and Australia using Moderate Resolution Imaging Spectroradiometer (MODIS) active fires (AF), fire radiative power (FRP, MW) and burned area (BA) products. Meteorological and environmental parameters were also analysed. The study found various patterns in the spatial distribution of fires, FRP and BA at the three sites, associated with various vegetation compositions, prevailing meteorological and environmental conditions and anthropogenic activities. We found significant fire clusters along the western and eastern coasts of the USA and Australia, respectively, while vastly distributed clusters were found in Brazil. Across all sites, significant fire intensity was recorded over forest cover (FC) and shrublands (SL), attributed to highly combustible tree crown fuel load characterised by leafy canopies and thin branches. In agreement, BA over FC was the highest in the USA and Australia, while Brazil was dominated by the burning of SL, characteristic of fire-tolerant Cerrado. The relatively lower BA over FC in Brazil can be attributed to fuel availability and proximity to highly flammable cover types such as cropland, SL and grasslands rather than fuel flammability. Overall, this study contributes to a better understanding of wildfires in various regions and the underlying environmental and meteorological causal factors, towards better wildfire disaster management strategies and habitat-specific firefighting

Observations of Emissions and the Influence of Meteorological Conditions during Wildfires: A Case Study in the USA, Brazil, and Australia during the 2018/19 Period

Wildfires can have rapid and long-term effects on air quality, human health, climate change, and the environment. Smoke from large wildfires can travel long distances and have a harmful effect on human health, the environment, and climate in other areas. More recently, in 2018–2019 there have been many large fires. This study focused on the wildfires that occurred in the United States of America (USA), Brazil, and Australia using Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP) and a TROPOspheric Monitoring Instrument (TROPOMI). Specifically, we analyzed the spatial-temporal distribution of black carbon (BC) and carbon monoxide (CO) and the vertical distribution of smoke. Based on the results, the highest detection of smoke (~14 km) was observed in Brazil; meanwhile, Australia showed the largest BC column burden of ~1.5 mg/m2. The meteorological conditions were similar for all sites during the fires. Moderate temperatures (between 32 and 42 °C) and relative humidity (30–50%) were observed, which resulted in drier conditions favorable for the burning of fires. However, the number of active fires was different for each site, with Brazil having 13 times more active fires than the USA and five times more than the number of active fires in Australia. However, the high number of active fires did not translate to higher atmospheric constituent emissions. Overall, this work provides a better understanding of wildfire behavior and the role of meteorological conditions in emissions at various sites

The Role of African Emerging Space Agencies in Earth Observation Capacity Building for Facilitating the Implementation and Monitoring of the African Development Agenda: The Case of African Earth Observation Program

AU-Agenda 2063 was adopted at the 24th Ordinary Session of the African Heads of State and Government in 2015 as the blueprint for the future development of the continent. Built upon the continent’s past experiences, challenges, and successes, AU-Agenda 2063 comprehensively describes the strategic path for Africa’s future development in the next 50 years. Thus, the monitoring of its implementation in various African states is critical for ensuring sustainable development and track progress. However, the higher cost of collecting data for accurately and reliably monitoring the implementation of Agenda 2063 may hinder the progress towards achieving these goals. Satellite Earth observation provides ample data, and thus has provided opportunities for the development of novel products and services with the potential to support implementation, monitoring and reporting for AU-Agenda 2063 development imperatives. However, it has been limitedly exploited in Africa, as evidenced by lower research outputs and investments. This calls for increased capacity building in the use of available EO data and products for various users including decision makers to advance national, regional and continental priorities. The use of such data products is often hampered by the capability to understand the products and thus their value for addressing socio-economic challenges. This paper discusses the potential of Earth observation capacity building for supporting the implementation, monitoring of, and reporting towards achieving AU-Agenda 2063 development imperatives. Specifically, this paper identifies existing capacity building resources, including the role of open and free Earth observation data, open-source software, and product dissemination platforms that can be leveraged for supporting national development, service delivery and the achievement of AU-Agenda 2063 targets. Furthermore, the paper recognizes the importance of bilateral and multilateral partnerships in leveraging existing know-how, technology and other resources for advancing strategic goals of African emerging space agencies and promoting sustainable development, with examples from South African National Space Agency (SANSA). Then, the challenges and opportunities for capacity building and the wide adoption of EO in Africa are discussed in the context of AU-Agenda 2063. The paper thus concludes that EO capacity building is essential to address the skills and data gaps and increase the use of EO-based solutions for decision making in various sectors, critical for achieving AU-A2063

Assessing the Responses of Aviation-Related SO2 and NO2 Emissions to COVID-19 Lockdown Regulations in South Africa

Aircraft emit harmful substances, such as carbon dioxide (CO2), water vapour (H2O), nitrogen oxides (NOx), sulphur oxides (SOx), particulates, and other trace compounds. These emissions degrade air quality and can deteriorate human health and negatively impact climate change. Airports are the nucleus of the ground and low-altitude emissions from aircraft during approach, landing, take-off, and taxi. During the global lockdown due to the COVID-19 pandemic, tight restrictions of the movement were imposed, leading to temporary closures of airports globally. In this study, we look at the variability of emissions at two major airports in South Africa, namely the OR Tambo international airport (FAOR) and the Cape Town international airport (FACT). Trend analysis of aircraft movements, i.e., departures and arrivals, showed a sharp decline at the two airports coinciding with the lockdowns to prevent the spread of the COVID-19. Consequently, a decrease in NO2 emissions by 70.45% (12.6 × 10−5 mol/m2) and 64.58% (11.6 × 10−5 mol/m2) at FAOR and FACT were observed, respectively. A noticeable SO2 emission decline was also observed, particularly over FAOR during the lockdown period in South Africa. Overall, this study observed that the global lockdown regulations had a positive impact on the air quality, causing a brief decline in emissions from commercial aviation at the South African major airports

Assessing the Relationship between Economic Growth and Emissions Levels in South Africa between 1994 and 2019

The objective of this study is to establish whether there is any relationship between economic growth and emission levels for pollutants (namely carbon dioxide (CO2), black carbon (BC), sulfur dioxide (SO2), and carbon monoxide (CO)) in South Africa, for the period from 1994 to 2019. Data from the world bank, namely gross domestic product (GDP) and CO2 emissions, were used. BC, SO2, and CO data were obtained from Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). The linear correlation coefficient and the environmental Kuznets curve (EKC) hypothesis test were used to determine the relationships. The sequential Mann–Kendall (SQMK) test was further used to study the trends. A correlation coefficient of 0.84, which indicates a strong positive linear correlation, between GDP and CO2 emission was observed. However, the relationship between GDP and CO concentration showed a correlation coefficient of −0.05, indicating no linear relationship between the two variables. The EKC hypothesis showed an N-shape for SO2 and CO. Overall, the results of this study indicate that emissions levels are generally correlated with economic growth. Therefore, a stringent regulatory system is needed to curtail the high emissions levels observed in this study, given the devastating impacts of global warming already ravaging the world

Assessing Spatio-Temporal Variability of Wildfires and their Impact on Sub-Saharan Ecosystems and Air Quality Using Multisource Remotely Sensed Data and Trend Analysis

Globally, wildfires are considered the most commonly occurring disasters, resulting from natural and anthropogenic ignition sources. Wildfires consist of burning standing biomass at erratic degrees of intensity, severity, and frequency. Consequently, wildfires generate large amounts of smoke and other toxic pollutants that have devastating impacts on ambient air quality and human health. There is, therefore, a need for a comprehensive study that characterizes land–atmosphere interactions with regard to wildfires, critical for understanding the interrelated and multidimensional impacts of wildfires. Current studies have a limited scope and a narrow focus, usually only focusing on one aspect of wildfire impacts, such as air quality without simultaneously considering the impacts on land surface changes and vice versa. In this study, we use several multisource data to determine the spatial distribution, frequency, disturbance characteristics of and variability and distribution of pollutants emitted by wildfires. The specific objectives were to (1) study the sources of wildfires and the period they are prevalent in sub-Saharan Africa over a 9 year period, i.e., 2007–2016, (2) estimate the seasonal disturbance of wildfires on various vegetation types, (3) determine the spatial distribution of black carbon (BC), carbon monoxide (CO) and smoke, and (4) determine the vertical height distribution of smoke. The results show largest burned areas in December–January–February (DJF), June–July–August (JJA) and September–October–November (SON) seasons, and reciprocal high emissions of BC, CO, and smoke, as observed by Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). In addition, the results reveal an increasing trend in the magnitude of BC, and CO concentration driven by meteorological conditions such as low precipitation, low relative humidity, and low latent heat flux. Overall, this study demonstrates the value of multisource remotely sensed data in characterising long-term wildfire patterns and associated emissions. The results in this study are critical for informing better regional fire management and air quality control strategies to preserve endangered species and habitats, promote sustainable land management, and reduce greenhouse gases (GHG) emissions

Seasonal Comparison of the Wildfire Emissions in Southern African Region during the Strong ENSO Events of 2010/11 and 2015/16 Using Trend Analysis and Anomaly Detection

This study investigates the wildfire biomass-burning emission levels during strong El Niño–southern oscillation (ENSO) events of 2010–2011 (characterized by a strong La Niña event) and 2015–2016 (characterized by a strong El Niño event) over the southern African region. Specifically, the biomass-burning parameters of black carbon (BC), carbon monoxide (CO) and sulfur dioxide (SO2) were investigated. Of interest in the current study was the strong El Niño (2015–2016) and La Niña (2010–2011) events during the main fire seasons in southern Africa, i.e., June–July–August (JJA) and September–October–November (SON). Furthermore, the study looks at how meteorological parameters (temperature and precipitation) are influenced by the two strong ENSO events. The sequential Mann–Kendall (SQMK) test is used to study the long-term trends of the emission and meteorological parameters. Anomaly detection on the long-term emission trends and meteorological parameters are performed using the seasonal and trend decomposition loess (STL) and generalized extreme studentized deviate (GESD). Overall, the results show higher emission levels of SO2, CO, and BC during the JJA season compared to the SON season. The SQMK results show an increasing trend of SO2, CO, and BC over time, indicating an increase in the amount of biomass burning. The GESD showed significant anomalies for BC, SO2, and CO emanating from the two strong El Niño and La Niña events. On the other hand, no significant anomalies were detected for temperature and precipitation. The results in this study highlight the significant effect of strong ENSO events on wildfire emissions, thus retrospectively showing the potential effect of future events, especially in the context of climate change