About This Course
Climate change is reshaping landscapes, water systems, and ecosystems at an unprecedented pace, and the ability to monitor these transformations in near-real time has become a critical competency for environmental professionals worldwide. Satellite imagery and remote sensing now provide the most comprehensive, scalable, and cost-effective means of tracking deforestation, glacier retreat, sea-level rise, land degradation, and extreme weather patterns across vast geographies — including regions that are otherwise inaccessible for ground-based monitoring.
This ten-day programme offered by TrainingComplex is designed to take participants from foundational concepts in remote sensing and Earth observation through to advanced analytical workflows that support climate adaptation planning, environmental reporting, and policy advocacy. Using widely adopted platforms such as Google Earth Engine, QGIS, and ESA's Copernicus datasets, participants will learn to acquire, process, analyse, and visualise satellite data with confidence and rigour.
The curriculum integrates theoretical grounding with intensive practical sessions, ensuring that every concept is immediately applied to real-world datasets drawn from African, Middle Eastern, and global contexts. From monitoring vegetation health and land-use change to assessing flood extents and urban heat islands, participants will leave with a portfolio of completed analyses and a toolkit they can deploy immediately within their organisations.
This course is particularly relevant in an era where international climate frameworks — including the Paris Agreement, the Sendai Framework, and national Nationally Determined Contributions (NDCs) — demand rigorous, spatially explicit evidence to demonstrate progress and accountability. Whether you work in government, conservation, agriculture, humanitarian response, or research, this programme will elevate your capacity to contribute meaningfully to the global climate monitoring agenda.
Who Should Attend
- Environmental scientists and ecologists working in climate change research or conservation
- GIS analysts and remote sensing specialists seeking to deepen climate monitoring capabilities
- Government officers in environment, lands, water, and meteorology ministries
- NGO and development sector professionals involved in climate adaptation or resilience programming
- Agriculture and food security specialists monitoring drought, crop stress, or land degradation
- Urban planners and disaster risk management professionals
- Researchers and postgraduate students in environmental sciences, geography, or related fields
- M&E officers responsible for tracking environmental indicators within development programmes
Learning Objectives
- Explain the fundamental principles of remote sensing, satellite orbits, and sensor types relevant to climate monitoring
- Navigate and access open-access satellite data repositories including Copernicus, NASA Earthdata, and USGS Earth Explorer
- Process and pre-process multispectral and SAR imagery for climate-related analyses using QGIS and Google Earth Engine
- Apply spectral indices such as NDVI, NDWI, and NBR to monitor vegetation health, water bodies, and fire scars
- Detect and quantify land-use and land-cover change over time using multi-temporal image analysis
- Monitor deforestation, degradation, and reforestation trends using time-series satellite data
- Assess flood extent, drought severity, and wildfire impacts using satellite-derived products
- Analyse urban heat island effects and surface temperature anomalies from thermal imagery
- Interpret glacier dynamics, snow cover changes, and sea-ice extent as indicators of climate change
- Generate professional-grade maps, dashboards, and reports communicating satellite-derived climate evidence
- Integrate satellite data with ground-truth observations and climate model outputs for enhanced accuracy
- Design a satellite-based monitoring framework aligned with national or organisational climate reporting requirements
- Apply ethical and data governance considerations in the use and dissemination of Earth observation products
Course Modules
Module 1: Foundations of Remote Sensing and Earth Observation
- Principles of electromagnetic radiation and spectral signatures
- Satellite orbit types: geostationary, polar-orbiting, and sun-synchronous
- Active vs. passive sensors: optical, SAR, thermal, and LiDAR
- Key satellite missions: Sentinel, Landsat, MODIS, and GOES
- Spatial, spectral, temporal, and radiometric resolution trade-offs and their implications for climate monitoring applications
- How sensor calibration and cross-mission consistency underpin long-term Earth observation records
Module 2: Accessing and Navigating Open-Access Satellite Data Portals
- Exploring the Copernicus Open Access Hub and Sentinel data products
- Downloading Landsat and MODIS imagery from USGS Earth Explorer and NASA Earthdata
- Understanding data formats: GeoTIFF, NetCDF, HDF, and metadata files
- Data selection criteria: spatial resolution, temporal frequency, and cloud cover thresholds
- Navigating specialised portals for atmospheric and oceanographic data: CEDA Archive, NASA Giovanni, and Copernicus Climate Data Store
- Organising downloaded datasets with consistent directory structures and file naming conventions for reproducible workflows
Module 3: Image Pre-Processing and Quality Control
- Radiometric and atmospheric correction techniques
- Cloud masking and gap-filling strategies for optical imagery
- Geometric correction and image co-registration
- Pre-processing workflows in QGIS and Google Earth Engine
- Topographic correction methods for mountainous terrain and their effect on reflectance accuracy
- Quality flag interpretation and band-specific artefact identification in Sentinel-2 and Landsat Collection 2 products
Module 4: Introduction to Google Earth Engine for Climate Analysis
- GEE architecture: image collections, feature collections, and reducers
- JavaScript API fundamentals for loading, filtering, and visualising datasets
- Building time-series charts and exporting processed imagery
- Best practices for scalable, reproducible analysis scripts
- Managing computational limits: tiling strategies, scale parameters, and avoiding memory overflow errors
- Using the GEE Python API with geemap for integration into Jupyter-based analytical environments
Module 5: Spectral Indices and Vegetation Monitoring
- Calculating and interpreting NDVI, EVI, and SAVI for vegetation health assessment
- Monitoring seasonal phenology and drought-induced vegetation stress
- Applying NDWI and MNDWI for surface water detection and wetland mapping
- Using NBR to map fire scars and assess post-fire ecosystem recovery
- Deriving Leaf Area Index and Fraction of Absorbed Photosynthetically Active Radiation from multispectral imagery
- Identifying spectral index saturation limitations in dense canopy environments and selecting appropriate alternatives
Module 6: Land-Use and Land-Cover Change Detection
- Supervised and unsupervised image classification techniques
- Accuracy assessment: confusion matrices, overall accuracy, and kappa coefficient
- Multi-temporal change detection: image differencing and post-classification comparison
- Mapping deforestation, agricultural expansion, and urban sprawl over time
- Designing statistically robust stratified random sampling schemes for reference data collection
- Applying object-based image analysis to reduce salt-and-pepper noise in high-resolution land-cover classifications
Module 7: Monitoring Drought, Floods, and Extreme Weather Events
- Using MODIS and Sentinel-1 SAR for flood extent mapping
- Deriving drought indices: SPI, VCI, and PDSI from satellite and reanalysis data
- Assessing wildfire burn severity using Landsat thermal and reflectance bands
- Integrating satellite products with early warning systems and humanitarian response
- Quantifying flood return periods and inundation duration using multi-date SAR time-series stacks
- Combining Sentinel-3 OLCI and ERA5 reanalysis data to characterise compound drought-heatwave events
Module 8: Thermal Imagery and Urban Heat Island Analysis
- Principles of thermal infrared remote sensing and land surface temperature retrieval
- Mapping urban heat islands using Landsat 8/9 TIRS and Sentinel-3 SLSTR
- Correlating surface temperature anomalies with land cover and imperviousness
- Supporting urban climate adaptation planning with thermal satellite evidence
- Applying split-window and single-channel algorithms to convert at-sensor radiance to land surface temperature
- Quantifying diurnal and seasonal variability in urban heat intensity using geostationary thermal time series
Module 9: Cryosphere and Oceanographic Monitoring
- Tracking glacier mass balance and retreat using optical and SAR time series
- Monitoring seasonal snow cover extent with MODIS snow products
- Sea-ice concentration and extent mapping using passive microwave data
- Satellite altimetry for sea-level rise monitoring: Jason and ICESat-2 missions
- Deriving permafrost extent and active layer thickness proxies from InSAR surface deformation measurements
- Using CryoSat-2 radar altimetry to estimate ice-sheet elevation change and contribute to global mass balance assessments
Module 10: Atmospheric Monitoring and Air Quality from Space
- Satellite-derived greenhouse gas products: CO₂ from OCO-2 and CH₄ from TROPOMI
- Monitoring aerosol optical depth and particulate matter using MODIS and VIIRS
- Tracking deforestation-linked emissions using GFED fire radiative power products
- Integrating atmospheric satellite data into national GHG inventory processes
- Retrieving tropospheric NO₂ and SO₂ columns from Sentinel-5P TROPOMI to identify industrial and traffic emission hotspots
- Evaluating satellite-derived atmospheric products against TCCON ground-based Fourier-transform spectrometer measurements for validation
Module 11: Integrating Satellite Data with Ground Observations and Climate Models
- Principles of data fusion: combining satellite, weather station, and field survey data
- Bias correction and downscaling satellite-derived climate variables
- Validating satellite products against in-situ measurements
- Using satellite data to calibrate and evaluate regional climate model outputs
- Applying machine learning regression techniques to merge multi-sensor precipitation estimates with gauge observations
- Quantifying and propagating uncertainty when satellite-derived variables feed into climate impact assessments
Module 12: Climate Reporting Frameworks and Satellite-Based Indicator Systems
- Linking satellite indicators to NDC monitoring, SDGs, and UNFCCC reporting
- Designing Essential Climate Variables (ECV) monitoring frameworks
- Geospatial dashboard development for climate transparency reporting
- Engaging policymakers and funders with satellite-derived climate evidence
- Structuring metadata and data provenance documentation to meet international climate reporting standards such as IPCC guidelines
- Establishing update cycles, threshold alerts, and versioning protocols to maintain long-term operational monitoring systems
Module 13: Capstone Project: Designing a Satellite-Based Climate Monitoring System
- Defining monitoring objectives, spatial extent, and indicator selection
- Constructing a reproducible analysis workflow using GEE or QGIS
- Producing a professional map series and written monitoring report
- Peer presentation of monitoring plan with facilitated expert feedback
- Developing a data management and version control plan using Git and cloud storage to ensure long-term reproducibility
- Articulating data limitations, uncertainty ranges, and recommended future monitoring enhancements within the final report
Requirements & Prerequisites
Participants should have a basic familiarity with environmental science, geography, or a related field, and a general understanding of climate change concepts. Prior exposure to GIS software or spatial data — even at an introductory level — is recommended, though not strictly required. Participants with no GIS background are encouraged to complete a short online GIS orientation before attending to maximise their learning experience.
Training Methodology
- Instructor-led concept sessions using annotated satellite imagery and real-world case studies from Africa, the Middle East, and global climate hotspots
- Hands-on lab exercises in QGIS and Google Earth Engine using live datasets from Copernicus, NASA Earthdata, and USGS platforms
- Guided image interpretation workshops where participants analyse multispectral composites and identify land-cover classes and climate anomalies
- Time-series analysis practicals in which participants build change-detection workflows and interpret multi-year climate trends
- Group-based scenario exercises simulating real monitoring challenges — such as assessing post-flood damage or tracking seasonal drought progression
- Peer review sessions where participants critique each other's maps and analysis outputs against professional standards
- Integration workshops connecting satellite data to climate reporting templates aligned with NDC and SDG indicator frameworks
- Capstone project in which each participant designs and presents a satellite-based monitoring plan for their own organisational context
Organisational Benefits
- Organisations gain staff who can independently design and operate satellite-based climate monitoring systems, reducing dependence on costly external consultants
- Enhanced capacity to generate spatially explicit evidence for climate reporting under national and international frameworks
- Improved early warning capabilities for climate-related disasters such as floods, droughts, and wildfires
- Stronger proposal competitiveness for climate finance, as funders increasingly require remote sensing-based monitoring plans
- Reduced field data collection costs through integration of satellite-derived indicators into M&E systems
- Ability to produce high-quality geospatial visualisations that strengthen advocacy, communication, and stakeholder engagement
- Alignment with global standards and open-data platforms, ensuring long-term sustainability of monitoring efforts
- A workforce equipped to contribute meaningfully to national climate adaptation and mitigation planning processes
Where Is Satellite Imagery and Climate Change Monitoring Training Offered?
This course runs in-person across: Kigali, Cape Town, Nairobi, Nakuru, Addis Ababa, Pretoria, Mombasa, Naivasha, Abuja, Riyadh, Zanzibar, Dubai, Accra, Dar es Salaam, Arusha, Johannesburg, Kampala, Lagos, Kisumu, plus Live Virtual (online) cohorts. Browse live dates for your city below.
Upcoming Schedules
| Location | Format | Dates | Price |
|---|
| Kigali, Rwanda |
In-Person |
5 Oct 2026 – 16 Oct 2026 |
USD 3,800 |
| Cape Town, South Africa |
In-Person |
5 Oct 2026 – 16 Oct 2026 |
USD 7,600 |
| Nairobi, Kenya |
In-Person |
5 Oct 2026 – 16 Oct 2026 |
KES 230,000 |
| Live Virtual |
Online |
5 Oct 2026 – 16 Oct 2026 |
KES 160,000 |
| Nakuru, Kenya |
In-Person |
12 Oct 2026 – 23 Oct 2026 |
KES 230,000 |
| Addis Ababa, Ethiopia |
In-Person |
12 Oct 2026 – 23 Oct 2026 |
USD 4,800 |
| Live Virtual |
Online |
17 Oct 2026 – 6 Dec 2026 |
KES 160,000 |
| Pretoria, South Africa |
In-Person |
19 Oct 2026 – 30 Oct 2026 |
USD 6,600 |
| Mombasa, Kenya |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
KES 240,000 |
| Naivasha, Kenya |
In-Person |
19 Oct 2026 – 30 Oct 2026 |
KES 230,000 |
| Abuja, Nigeria |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 5,600 |
| Riyadh, Saudi Arabia |
In-Person |
19 Oct 2026 – 30 Oct 2026 |
USD 9,000 |
| Zanzibar, Tanzania |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 5,000 |
| Dubai, United Arab Emirates |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 8,200 |
| Accra, Ghana |
In-Person |
26 Oct 2026 – 4 Nov 2026 |
USD 7,600 |
+236 more dates — view live schedule →
Frequently Asked Questions
Do I need prior GIS or remote sensing experience to attend?
A basic familiarity with spatial data or GIS concepts is recommended, and participants with prior exposure will progress more quickly through the practical sessions. However, the course begins with foundational principles and is structured to bring motivated beginners up to speed. Participants with no GIS background are advised to complete a short introductory GIS module beforehand.
What software and tools will be used during the course?
The course primarily uses QGIS (free and open-source) and Google Earth Engine (free with a registered account), along with open-access satellite data from ESA Copernicus, NASA Earthdata, and USGS. Participants should bring a laptop capable of running QGIS and with stable internet access for GEE sessions. All software installation guides are shared in advance of the training.
Will I receive a certificate upon completion?
Yes. Participants who attend the full ten-day programme and complete the capstone project will receive a Certificate of Completion from TrainingComplex, recognising their proficiency in satellite imagery analysis and climate change monitoring.
Is this course available online or only in person?
The course is primarily delivered in person across multiple African and international locations to maximise the quality of hands-on lab sessions. However, selected cohorts are offered as live virtual sessions for participants unable to travel, with the same curriculum and interactive exercises conducted via video conferencing and shared cloud-based platforms.
How current are the datasets and case studies used in the training?
All practical exercises use current or near-current satellite imagery and datasets, typically from the past one to three years, ensuring that the analysis scenarios reflect real and ongoing climate change dynamics. Case studies are drawn from African, Middle Eastern, and global contexts to ensure relevance to participants across different operating environments.
