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Spaceborne observations of the land surface provide reflectance information that is related to ecosystem optical and emissivity properties. Optical remote sensing typically measures surface reflectance in wavelengths ranging from ultraviolet to the longwave‐infrared regions, whereas microwave observations (including active and passive approaches) use longer wavelengths to observe brightness temperature (as it relates to soil moisture), and active LIDAR records light travel time that is related to how ecosystem structure modifies the profile of a laser beam return. Remote sensing per se does not measure above‐ or belowground structure directly; for example, the surface retrievals, referred to as Level 2 products, include the atmospherically and geometrically corrected surface information, but require the application of algorithms to derive vegetation or soil properties (i.e., Level 3 products) or models to produce process‐level information (i.e., Level 4 products).

In this context, remote sensing observations can provide spatially consistent information on ecosystem type and composition as well as ecosystem structure (i.e., via canopy height models), both of which can be used to derive information on above‐ and below ground carbon stocks. The use of remote‐sensing derived information also requires knowledge on how to integrate temporal revisit as well as ground sampling distance (GSD), or spatial resolution, which can introduce uncertainties in terms of detection of seasonally flooded wetlands, or introduce “double counting” of wetlands when using coarse spatial resolution data (>500 m).

Some examples of how remote sensing has informed wetland carbon stock accounting are included in this chapter; for example, where remote sensing data based on the MODIS sensor aboard AQUA and TERRA has been used to map vegetation indices linked to wetland habitat. Or where passive and active microwave observations (i.e., NASA’s AMSR and SMAP missions) have been used to derive permafrost and soil moisture information used in ecosystem models. And where radar and lidar information from the German Aerospace Center (DLR) mission Tandem‐X and airborne instruments (e.g., NASA’s G‐LiHT, ECOSAR) have been used to map canopy height and to relate this to biomass models to estimate aboveground carbon stocks. Advances in remote sensing, particularly in improved spatial resolution capabilities, will contribute to better delineation of wetland types using high‐resolution optical data (see Cooley et al., 2019) and for determining aboveground structure of vegetation in the case of the International Space Station (ISS) small‐footprint waveform lidar mission “GEDI.” In addition, operational and emerging hyperspectral remote sensing missions such as on the ISS like the DLR Earth Sensing Imaging Spectrometer (DESIS), and “free flying” spacecraft proposed by the European Space Agency CHIME and NASA Surface Biology and Geology (SBG) missions, have potential to use more sophisticated algorithms relating surface reflectance to soil carbon content.