Synthetic Aperture Radar has a unique potential for continuous forest mapping as it is not affected by cloud cover. While longer wavelengths, such as L-band, are commonly used for forest applications, in this paper we assess the aptitude of C-band Sentinel-1 data for this purpose, for which there is much interest due to its high temporal resolution (five days) and “free, full, and open” data policy. We tested its ability to distinguish forest from non-forest in six study sites, located in Alaska, Colombia, Finland, Florida, Indonesia, and the UK. Using the time series for a full year significantly increases the classification accuracy compared to a single scene (a mean of $85\%$ compared to $77\%$ across the study sites for the best classifier). Our results show that we can further improve the mean accuracy to $87\%$ when only considering the annual mean and standard deviation of co-polarized (VV) and cross-polarized (VH) backscatter. In this case, separation accuracies of up to $93\%$ (in Finland) are possible, though in the worst case (Alaska), the highest possible accuracy using these variables was $80\%$ . The best overall performance was observed when using a Support Vector Machine classifier, outperforming random forest, k-Nearest-Neighbors, and Quadratic Discriminant Analysis. We further show that the small information content we found in the phase data is an artifact of terrain slope orientation and has a negligible impact on classifier performance. We conclude that for the purposes of forest mapping the smaller file size and easier to process GRD products are sufficient, unless the SLC products are used to compute the temporal coherence which was not tested in this study.

Miombo woodlands are integral to livelihoods across southern Africa, biodiversity in the region, and the global carbon cycle, making accurate and precise monitoring of their state and change essential. Here, we assembled a terrestrial and airborne lidar dataset covering 50 kha of intact and degraded miombo woodlands, and generated aboveground biomass estimates with low uncertainty via direct 3D measurements of forest structure. We found 1.71 ± 0.09 TgC was stored in aboveground biomass across this landscape, between 1.5 and 2.2 times more than the 0.79–1.14 TgC estimated by conventional methods. This difference is in part owing to the systematic underestimation of large trees by allometry. If these results were extrapolated across Africa’s miombo woodlands, their carbon stock would potentially require an upward revision of approximately 3.7 PgC, implying we currently underestimate their carbon sequestration and emissions potential, and disincentivise their protection and restoration.

Current satellite lidars have sparse spatial coverage, leading to uncertainty from sampling. This complicates robust change detection and does not allow applications that require continuous coverage. One potential way to increase lidar sampling density is to use more efficient lasers. All current spaceborne lidars use solid-state lasers with a limited efficiency of 5–8%. In this paper, we investigate the potential for using diode lasers, with their higher efficiencies, as an alternative. Diode lasers have reported efficiencies of about 25% and are much smaller and lighter than solid-state lasers. However, they can only emit good beam quality at lower peak powers, which has so far prevented them from being used in spaceborne lidar applications. In this paper, we assess whether the novel lidar modalities necessitated by these lower peak powers are suitable for satellite lidar, determined by whether they can match the design performance of GEDI by being able to accurately measure ground elevation through 98% canopy cover, referred to as having “98% beam sensitivity”. Through this, we show that a diode laser can be operated in pulse train or pulse compressed lidar (PCL) mode from space, using a photon-counting detector. In the best case scenario, this setup requires a detected energy of $E_{\det }=0.027 \mathrm{fJ}$ to achieve a beam sensitivity of 98%, which is less than the 0.28 $\mathrm{fJ}$ required by a full-waveform solid-state lidar instrument, exemplified by GEDI. When also accounting for the higher laser and detector efficiency, the diode laser in pulse train mode requires similar shot energy as a photon counting solid-state laser such as ICESat-2 which along with the higher laser efficiency could result in a doubling of coverage. We conclude that there is a clear opportunity for diode lasers to be used in spaceborne lidars, potentially allowing wider coverage through their higher efficiencies.