References, Prerequisites & Cross-Course Links
This page collects every prerequisite topic, textbook reference, and related CoursesHub course for the Earth Observation & Satellite Monitoring curriculum. Use it as a roadmap before you begin, or as a lookup when a module invokes concepts from another discipline.
How to use this page: Prerequisite cards tell you which topics from each course are needed and where they appear in the EO modules. The textbook list is organised by module so you can dive deeper into any single application area. Related-course and online-resource sections point you to complementary material across CoursesHub and the wider web.
1. Prerequisites & Recommended Background
The EO course draws on ideas from many branches of physics, mathematics, and geoscience. Each card below links to the relevant CoursesHub course and summarises exactly which topics feed into which EO modules.
Linear algebra (matrix operations, eigenvalues), Fourier transforms (spectral analysis, FFT), statistics (regression, hypothesis testing), calculus (partial derivatives, gradient descent).
M2 Physical Principles (radiative transfer equations), M4 InSAR (phase unwrapping, least-squares inversion), M5 Drought (time-series decomposition), M8 Land Use (classification accuracy metrics).
Maxwell's equations, EM wave propagation, polarization states (linear, circular, elliptical), antenna theory, radar equation.
M1 Satellite Systems (sensor design, antenna patterns), M2 Physical Principles (Planck's law, Stefan-Boltzmann, Rayleigh scattering), M4 InSAR (SAR signal model, coherence), M6 Floods (SAR backscatter from water).
Navier-Stokes equations, turbulence, boundary layers, atmospheric dynamics, geostrophic flow, Ekman spirals.
M5 Drought (evapotranspiration modelling), M6 Floods (hydraulic modelling, flood propagation), M7 Climate (ocean circulation, sea-ice dynamics), NOAA Reception (atmospheric refraction of RF signals).
Bayesian inference, hypothesis testing, classification metrics (precision, recall, F1, confusion matrix), maximum likelihood estimation, Monte Carlo methods.
M4 InSAR (deformation model inversion, uncertainty quantification), M6 Floods (Otsu thresholding, ROC curves), M8 Land Use (Random Forest classification, cross-validation, accuracy assessment).
Atmospheric structure (troposphere, stratosphere), weather systems (cyclones, fronts), climate variability (ENSO, NAO), radiation budget, greenhouse effect.
M5 Drought (drought indices, SPI), M7 Climate (COβ/CHβ monitoring, SST anomalies, marine heatwaves), NOAA Reception (weather satellite imagery interpretation).
Plate tectonics, seismology (seismic wave propagation, focal mechanisms), geodynamics, rock mechanics, geodesy.
M4 Earthquake Monitoring (Okada fault model, coseismic deformation), M7 Climate (GRACE gravity field, ice-sheet mass balance), Spectral Bands (mineral spectroscopy).
Crustal deformation, fault mechanics (strike-slip, thrust, normal), elastic rebound theory, interseismic strain accumulation, GPS geodesy.
M4 Earthquake Monitoring (InSAR deformation mapping, PSInSAR time series, Coulomb stress modelling), M7 Climate (post-glacial rebound, GIA corrections for GRACE).
Ocean circulation (thermohaline, wind-driven), sea surface temperature (SST) retrieval, sea level change, ocean colour (chlorophyll-a), altimetry.
M7 Climate (SST anomaly detection, marine heatwave classification, sea-ice extent), M5 Drought (ocean-atmosphere teleconnections), Spectral Bands (ocean colour band ratios).
Note: You do not need to complete every prerequisite before starting the EO course. Modules M1-M3 are largely self-contained. The deeper mathematical and physical background becomes essential from M4 (InSAR) onwards. We recommend at least a working familiarity with linear algebra and EM wave propagation before tackling M4-M8.
2. Textbook References by Module
The following references are cited throughout the course modules. Entries are grouped by the module(s) that rely on them most heavily. Where a reference spans multiple modules it appears under the earliest relevant one.
M1-M2: Satellite Systems & Physical Principles
- Lillesand, T. M., Kiefer, R. W. & Chipman, J. W. (2015). Remote Sensing and Image Interpretation. 7th ed. Wiley.
- Jensen, J. R. (2016). Introductory Digital Image Processing: A Remote Sensing Perspective. 4th ed. Pearson.
- Rees, W. G. (2013). Physical Principles of Remote Sensing. 3rd ed. Cambridge University Press.
- Elachi, C. & van Zyl, J. J. (2006). Introduction to the Physics and Techniques of Remote Sensing. 2nd ed. Wiley-Interscience.
- Schowengerdt, R. A. (2007). Remote Sensing: Models and Methods for Image Processing. 3rd ed. Academic Press.
M3: Data Access & APIs
- Boeing, G. (2017). OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Computers, Environment and Urban Systems, 65, 126β139.
- Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18β27.
M4: Earthquake Monitoring (InSAR)
- BΓΌrgmann, R., Rosen, P. A. & Fielding, E. J. (2000). Synthetic aperture radar interferometry to measure Earthβs surface topography and its deformation. Annual Review of Earth and Planetary Sciences, 28, 169β209.
- Massonnet, D. & Feigl, K. L. (1998). Radar interferometry and its application to changes in the Earthβs surface. Reviews of Geophysics, 36(4), 441β500.
- Okada, Y. (1985). Surface deformation due to shear and tensile faults in a half-space. Bulletin of the Seismological Society of America, 75(4), 1135β1154.
- Hooper, A., Bekaert, D., Spaans, K. & ArΔ±kan, M. (2012). Recent advances in SAR interferometry time series analysis for measuring crustal deformation. Tectonophysics, 514β517, 1β13.
- Yunjun, Z., Fattahi, H. & Amelung, F. (2019). Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction. Computers & Geosciences, 133, 104331. [MintPy]
M5: Drought & Vegetation
- Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127β150.
- Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X. & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1β2), 195β213.
- Wan, Z. & Dozier, J. (1996). A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Transactions on Geoscience and Remote Sensing, 34(4), 892β905.
M6: Flood Mapping
- Twele, A., Cao, W., Plank, S. & Martinis, S. (2016). Sentinel-1-based flood mapping: a fully automated processing chain. International Journal of Remote Sensing, 37(13), 2990β3004.
- Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62β66.
- Schumann, G. J.-P. & Moller, D. K. (2015). Microwave remote sensing of flood inundation. Physics and Chemistry of the Earth, Parts A/B/C, 83β84, 84β95.
M7: Climate Monitoring
- Comiso, J. C. (2010). Polar Oceans from Space. Springer.
- Tapley, B. D., Bettadpur, S., Ries, J. C., Thompson, P. F. & Watkins, M. M. (2004). GRACE measurements of mass variability in the Earth system. Science, 305(5683), 503β505. (See also Space Science Reviews, 108.)
- Veefkind, J. P., Aben, I., McMullan, K., FΓΆrster, H., de Vries, J., Otter, G., ... & Levelt, P. F. (2012). TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications. Atmospheric Measurement Techniques, 5(5), 1187β1199.
- Hobday, A. J., Alexander, L. V., Perkins, S. E., Smale, D. A., Straub, S. C., Oliver, E. C. J., ... & Wernberg, T. (2016). A hierarchical approach to defining marine heatwaves. Progress in Oceanography, 141, 227β238.
M8: Land Use & Land Cover
- Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5β32.
- Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., ... & Arino, O. (2021). ESA WorldCover 10 m 2020 v100. Zenodo.
- Brown, C. F., Brumby, S. P., Guzder-Williams, B., Birch, T., Hyde, S. B., Mez, J., ... & Tait, A. M. (2022). Dynamic World, Near real-time global 10 m land use land cover mapping. Scientific Data, 9, 251.
Spectral Bands & Sensor Design
- Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., ... & Bargellini, P. (2012). Sentinel-2: ESAβs Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment, 120, 25β36.
- Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., ... & Zhu, Z. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154β172.
- Rouse, J. W., Haas, R. H., Schell, J. A. & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. In Proceedings of the Third Earth Resources Technology Satellite-1 Symposium, NASA SP-351, 309β317.
NOAA Satellite Reception
- Vallado, D. A. (2013). Fundamentals of Astrodynamics and Applications. 4th ed. Microcosm Press.
- Kidder, S. Q. & Vonder Haar, T. H. (1995). Satellite Meteorology: An Introduction. Academic Press.
Citation convention: In-module citations use author-year format. Full bibliographic details are collected here. If you notice a missing or incorrect reference, please open an issue on the course repository.
4. Key Online Resources
These platforms, tools, and data portals are referenced across multiple EO modules. Bookmark them β you will return to them often.
Copernicus Data Space Ecosystem
dataspace.copernicus.eu↗Primary access point for all Copernicus Sentinel data. Offers a browser, Jupyter notebooks, and OData/STAC APIs for programmatic access.
USGS EarthExplorer
earthexplorer.usgs.gov↗Search and download Landsat, MODIS, aerial imagery, and other USGS-hosted remote sensing datasets.
Google Earth Engine
earthengine.google.com↗Cloud-based platform for planetary-scale geospatial analysis. Multi-petabyte catalogue with JavaScript and Python APIs.
NASA Earthdata
earthdata.nasa.gov↗Central portal for NASA Earth science data. Includes AppEEARS, Giovanni, Worldview, and CMR search.
ESA EO Portal
eoportal.org↗Comprehensive directory of Earth observation missions, instruments, and sensor specifications from all agencies.
Sentinel Hub
www.sentinel-hub.com↗Commercial platform for on-the-fly processing and visualisation of Sentinel, Landsat, and other satellite imagery via OGC services.
STAC Index
stacindex.org↗Community catalogue of SpatioTemporal Asset Catalogues (STAC). Discover STAC-compliant data providers and tools.
SNAP / ESA Toolboxes
step.esa.int↗Sentinel Application Platform (SNAP) and associated toolboxes for Sentinel-1 (SAR), Sentinel-2 (optical), and Sentinel-3 processing.
Open-source InSAR time-series analysis package. Implements small-baseline (SBAS) and persistent scatterer approaches.
Pangeo
pangeo.io↗Community platform for Big Data geoscience. Integrates Xarray, Dask, Zarr, and Jupyter for scalable analysis of climate and EO data.
Access note: Most platforms above offer free tiers or open data access. Google Earth Engine and Copernicus Data Space require a free account registration. Sentinel Hub offers a limited free tier; full API access requires a subscription.
5. Mathematical Notation Quick Reference
Key equations and notation used throughout the course, collected here for convenient lookup.
Radiometry & Spectral Indices
Planck's Law (spectral radiance)
\[ B_\lambda(T) = \frac{2hc^2}{\lambda^5} \cdot \frac{1}{e^{hc / (\lambda k_B T)} - 1} \]
Normalised Difference Vegetation Index (NDVI)
\[ \text{NDVI} = \frac{\rho_{\text{NIR}} - \rho_{\text{Red}}}{\rho_{\text{NIR}} + \rho_{\text{Red}}} \]
Enhanced Vegetation Index (EVI)
\[ \text{EVI} = G \cdot \frac{\rho_{\text{NIR}} - \rho_{\text{Red}}}{\rho_{\text{NIR}} + C_1 \rho_{\text{Red}} - C_2 \rho_{\text{Blue}} + L} \]
SAR & InSAR
Radar Equation
\[ P_r = \frac{P_t G^2 \lambda^2 \sigma}{(4\pi)^3 R^4} \]
InSAR Phase (line-of-sight displacement)
\[ \Delta \phi = \frac{4\pi}{\lambda} \cdot d_{\text{LOS}} \]
Coherence
\[ \gamma = \frac{|\langle s_1 s_2^* \rangle|}{\sqrt{\langle |s_1|^2 \rangle \langle |s_2|^2 \rangle}} \]
Classification & Accuracy
Overall Accuracy
\[ \text{OA} = \frac{\sum_{i=1}^{k} n_{ii}}{N} \]
Cohen's Kappa
\[ \kappa = \frac{p_o - p_e}{1 - p_e} \]
F1 Score
\[ F_1 = 2 \cdot \frac{\text{Precision} \cdot \text{Recall}}{\text{Precision} + \text{Recall}} \]
Otsu Thresholding (Flood Mapping)
Inter-class Variance
\[ \sigma_B^2(t) = \omega_0(t)\,\omega_1(t)\,[\mu_0(t) - \mu_1(t)]^2 \]
Optimal Threshold
\[ t^* = \arg\max_t \; \sigma_B^2(t) \]
Okada Fault Model (Earthquake Monitoring)
Surface Displacement (vertical component, simplified)
\[ u_z(\mathbf{x}) = \frac{U}{2\pi} \left[ \sin\delta \cdot I_1(\mathbf{x}) - d \cdot I_2(\mathbf{x}) \right] \]
where \( U \) is the slip magnitude, \( \delta \) the dip angle, \( d \) the fault depth, and \( I_1, I_2 \)are Chinnery integration terms. See Okada (1985) for the full analytical expressions.
Orbital Mechanics (NOAA Reception)
Orbital Period
\[ T = 2\pi \sqrt{\frac{a^3}{\mu}} \]
Doppler Shift (APT frequency)
\[ f_{\text{obs}} = f_0 \left(1 + \frac{v_r}{c}\right) \]
where \( v_r \) is the radial velocity of the satellite relative to the ground station, \( f_0 = 137 \) MHz (NOAA APT), and \( c \) is the speed of light.
6. Module Dependency Graph
A quick overview of how the EO modules build on each other and which prerequisite courses feed into each one.
Mathematics ββββββ¬βββββββββββββββββββββββββββββββββββββββββββββββββββ
Electrodynamics ββ€ β
βΌ βΌ
ββββββββββββ ββββββββββββββββ ββββββββββββββββββββββββ
β M1: Sat βββββΆβ M2: Physical βββββΆβ M3: Data Access β
β Systems β β Principles β β & APIs β
ββββββββββββ ββββββββ¬ββββββββ ββββββββββ¬βββββββββββββ
β β
ββββββββββββββββββββββΌβββββββββββββββββββββββ€
β β β
βΌ βΌ βΌ
βββββββββββββββββ βββββββββββββββββ βββββββββββββββββββββββββββββ
β M4: Earthquakeβ β M5: Drought & β β M6: Flood Mapping β
β (InSAR) β β Vegetation β β (SAR Thresholding) β
βββββββββ¬ββββββββ βββββββββ¬ββββββββ βββββββββββββββ¬ββββββββββββββ
β β β
ββββββββββββ¬ββββββββ΄βββββββββββββββββββββββββ
βΌ
ββββββββββββββββββββββ ββββββββββββββββββββββ
β M7: Climate β β M8: Land Use & β
β Monitoring β β Land Cover (ML) β
ββββββββββββββββββββββ ββββββββββββββββββββββ
Prerequisites feeding each module:
βββββββββββββββββββββββββββββββββ
M1-M3 : Electrodynamics, Mathematics (basics)
M4 : Tectonics, Earth Sciences, Linear Algebra
M5 : Climatology, Fluid Mechanics, Statistics
M6 : Fluid Mechanics, Probability & Statistics
M7 : Oceanography, Climatology, Fluid Mechanics
M8 : Probability & Statistics (classification)