dataesr/scientific-paragraphs-categorization huggingface dataset:
This test consisted in discarding the field plots for which the coefficient of variation (CV) of the SAR intensities in the considered 3 × 3 pixels neighbourhoods was higher than 0. 25. This threshold is a trade-off between the need to discard heterogeneous plots and the need to keep a sufficient number of plots for a reliable training of the model. In total, 144 field plots were selected, located in 8 countries (Cameroon, Burkina Faso, Malawi, Mali, Ghana, Mozambique, Botswana and South Africa), with a mean plot size of 0. 89 ha. The field data collection period spreads between 2006 and 2012, with a distribution averaging 2010 ± 2. 5 year. The selected in situ data characteristics are summarized in Table  S1. In each field plot, the AGB has been estimated by applying allometric equations to in situ measurements of tree parameters (diameter at breast height, tree height, basal area), following proper sampling methodologies.
SAR data description The global dual polarisation (HH, HV) 25 m resolution ALOS PALSAR mosaic produced by JAXA for the year 2010 was used in this study. The mosaic consists of a collage of Fine Beam Dual-polarisation (FBD) data strips acquired in ascending mode with HH and HV polarizations. For each pixel, the mosaic dataset also includes values of the local incidence angle and acquisition date. The FBD data have a swath width of about 70 km. To cover the African continent, about 180 data strips have been used. About 91% of the data have been acquired in 2010, between 1 May and 28 November, and the remaining gaps have been filled with FBD imagery from 2009 and 2008. The data have been processed by JAXA using the large-scale mosaicking algorithm described in  Shimada and Ohtaki (2010) , including ortho-rectification, slope correction and radiometric calibration between neighbouring strips.
Issues related to data homogeneity in large mosaics composed of multi-date acquisitions When applying a single inversion scheme to radar data acquired at different dates, as is the case in the PALSAR mosaic, sources of error include variations of environmental effects between acquisition strips (soil and vegetation moisture, leaf-on/leaf-off and grass/understory conditions, e. g. if images are acquired during different seasons) and, less importantly, radar backscatter variation caused by radar calibration and incidence angle variation in a data strip. As a result, sharp radiometric discontinuities can be observed between adjacent PALSAR strips when the mosaics are produced. These discontinuities, although they locally represent the reality of the backscatter at the time of the acquisitions, are considered troublesome for large-scale applications based on the mosaics.
, 2013; Urbazaev et al. , 2015). In the wet season, the vast majority of trees have their leaves on, but the random occurrence of rainfall events leads to locally reduced sensitivity in the backscatter-AGB relationship as well as larger inter-strip variations. As a consequence, we decided to estimate the AGB separately in dry season areas and in wet season areas, keeping in mind that better results should be found in the dry season. The delimitation between dry and wet season areas is set by a rainfall isoline corresponding to a mean total rainfall of 500 mm over the June-September period (Fig. 2 , right), calculated from rainfall data of the WorldClim database (http://www. worldclim. org) representative of the 1950-2000 period  (Hijmans et al. , 2005).
Approach The relationship between the radar backscatter and AGB is complex and depends on a large number of parameters related to vegetation (forest structure, vegetation water content, presence/absence of leaves) and to the ground (soil moisture, soil roughness, topography), collectively designated as "environmental conditions". The lack of suitable datasets available globally at sufficient resolutions makes it virtually impossible to estimate AGB from the backscatter by taking into account these environmental conditions explicitly. In this study, the approach that we use consists in performing the AGB estimation separately in the "wet season areas" and in the "dry season areas" described in Section 3. 2. 2, such as most of the backscatter variability is minimised with respect to the most important factors.
Analysis of backscatter versus biomass The sensitivity of the radar backscatter to AGB is analysed using the 2010 ALOS mosaic and in situ AGB data. Fig. 3. shows the backscatter data (γ 0 HH and γ 0 HV ) as a function of the AGB of the in situ plots, where in situ plots located in the wet season areas and dry season areas, as defined in Section 3. 2. 2, are plotted in blue and red respectively. As expected, we found that the relationship between the radar backscatter and AGB depends on wet and dry season areas. Note that for the wet season areas, in situ plots cover a larger range of AGB (up to 250 Mg•ha -1 ), whereas for the dry season areas, in situ AGB is under 120 Mg•ha -1. In the range of AGB under 100 Mg•ha -1 , as expected, the backscatter is mostly higher in wet season areas, which is explained by the higher soil and vegetation wetness, and probably also by the vegetation structure and phenology (leaf-on).
The comparison will give information on the validity of the simplification. One common formulation is a 3-parameter semi-empirical equation which consists in a sum of the ground (a) and vegetation (b) contributions weighted by the attenuation caused by different vegetation layers (e -c. AGB ): a b e 1 e c c 0 A G B A G B = + - - - γ • • ( ). • (5) This formulation corresponds to the modified Water Cloud Model  (Santoro et al. , 2002) , an adaptation of the original Water Cloud Model  (Attema and Ulaby, 1978)  widely used at higher frequencies like Cband, except that the stem volume is replaced by the biomass. This 3parameter model has already been used for L-band data by several authors  (Cartus et al. , 2012; Mermoz et al. , 2014; Michelakis et al. , 2014; Mitchard et al. , 2011). For such long wavelengths however, attenuation is significantly less important than at C-band so that the effects of ground scattering are more important, whether through its direct contribution or through the double bounce contribution, especially in open landscapes like savannahs.
Calibration of the direct model Different approaches can be used to estimate the three parameters, a, b, and c, required for Eq. (  5 ). Regarding the parameters a and b, we can use statistical regressions between the SAR data and in situ plot data, provided there are a sufficient number of plots, well distributed over the relevant range of AGB and plot conditions. When the in situ plot data do not meet these requirements, parameter a can be estimated from pixels corresponding to bare ground (i. e. bare soil or grassy areas) and b from pixels corresponding to closed forests. One option is to use ancillary data such as Landsat tree cover (LTC) continuous field from  Sexton et al. (2013) , similarly to what was done in  Cartus et al. (2014). Mean values of backscatter from pixels corresponding to LTC values of 0% (or lower than a threshold) and 100% (or higher than a threshold) crown cover are used as proxy for estimating parameters a and b.
This approach is however expected to provide better results for the estimation of parameter b than for parameter a, as the low canopy pixels in the LTC dataset will tend to incorporate, in addition to the targeted bare soil or grassy areas, low shrub areas which can have a significant woody In this study, we had a sufficient amount of low AGB plots (below 25 Mg•ha -1 ) to accurately calibrate parameter a. However, there was a lack of high AGB plots (above 150 Mg•ha -1 ) to estimate properly parameter b, especially for the dry conditions. Thus, we extracted separately for each region the backscatter values over values of LTC larger than 90%, to pick up the "dense forest" pixels only. The probability density functions of γ 0 HV and γ 0 HH over dense forests are plotted in Fig. 4. The mean and standard deviations are -6.
Post-processing of AGB estimates The method described in Sections 4. 2 to 4. 5 is applied to the 2010 PALSAR mosaic data over Africa, independently over the dry season and wet season areas. While the maximum AGB value used to define the prior probability p(B) in the Bayesian scheme is equal to 100 Mg•ha -1 , as mentioned in Section 4. 5, the resulting AGB values estimated through the Bayesian inversion (MMSE estimator) never exceed 85 Mg•ha -1. Because AGB estimates in the range of 0 to 85 Mg•ha -1 would yield large errors in some biomes like dense forests, the inversion should better be restricted to the savannah and woodland ecosystems. One potential way of doing this could be to invert pixel values into AGB with the direct model and mask out pixels with an AGB higher than 100 Mg•ha -1. However, as was evidenced in  Mermoz et al.
Among the available land cover maps in Africa, the Global land cover (GLC) 2000 map  (Mayaux et al. , 2004) , which is based on SPOT-VE-GETATION data, has a resolution of 1 km. The GlobCover 2009  (Arino et al. , 2012)  and the CCI Land Cover 2010 map have a resolution of 300 m. From these maps, we selected the most recent CCI Land Cover 2010 map. In this map, we considered as dense forest the class Broadleaved evergreen closed to open forest. We also used the CCI Land Cover 2010 map to mask out flooded forests, mangroves, urban areas and water bodies. The GLC 2000 map was used to mask out some bare areas embedded with rocks that lead to false-alarms in AGB detection, for example in Southern Algeria or in Northern Chad, as these areas are not The pre-processed and filtered 2010 PALSAR mosaic data at 25 m resolution, where the dense forest, flooded forests, mangroves, urban areas, bare rocks and water bodies have been excluded, have been inverted on a pixel basis into AGB values using the Bayesian inversions for dry and wet conditions.
Precision The precision of the estimated AGB, which refers to how close the agreement is between repeated estimates, is linked to the precision of all the measurements involved in the Bayesian inversion scheme, i. e. the in situ AGB measurements and the SAR data measurements. The precision of the in situ AGB measurements is represented by the standard deviation value σ AGB field calculated for each in situ plot, following Eq. (1) and the methodology described in Section 3. 1. 2. The precision of the SAR data measurements is defined by the standard deviation value σ SAR calculated for each HH and HV backscatter value, following Eq. (  4 ). These uncertainties linked to the in situ AGB estimates and to the SAR data are propagated step-by-step through the whole inversion scheme using a Monte Carlo approach, which allows obtaining the precision of the estimated AGB.
Results Because the other large-scale AGB datasets available over Africa  (Avitabile et al. , 2016; Baccini et al. , 2012 Baccini et al. , , 2015;; Saatchi et al. , 2011)  give conflicting estimates in the savannahs and woodlands regions, and because very few inventory plots exist in these areas, it is impossible to provide an overall evaluation of the AGB estimates that we have produced in this study. In order to provide a partial evaluation of our results, the AGB map produced in this study is visually compared with the other datasets in Section 5. 1 and some differences are analysed. We then perform a cross-validation of our dataset using the available in situ inventory data in Section 5. 2. 1, and a local validation with independent LiDAR estimates in the lowveld region in South Africa in Section 5.
Visual assessment of the biomass dataset Fig. 6  shows the AGB map of Africa produced using the approach described in this paper. An uncertainty value is associated with each AGB estimate, using the methodology described in Section 4. 7. The four pan-tropical AGB datasets mentioned in the Introduction  (Avitabile et al. , 2016; Baccini et al. , 2012 Baccini et al. , , 2015;; Saatchi et al. , 2011)  are displayed in Fig. 7  for comparison with our AGB dataset. Fig. 7  shows a clear under-estimation of biomass in savannas in the dataset of Avitable when compared to others, as well as less intermediate values between high and low biomass. For example, biomass stocks estimated in Ethiopia are much larger in the datasets of Saatchi and Baccini than in Avitabile's. Whereas Baccini's dataset shows larger biomass estimates than Saatchi's and Avitabile's in some areas like in Angola, it shows as well less areas of intermediate values (from 40 to 60 t/ha) than Saatchi's dataset, like in Kenya, Somalia and Ethiopia for example.
The dataset derived in this study (Fig. 6 ) shows even more biomass dynamics than Saatchi's dataset, e. g. in Somalia. Figs. 8  and 9  show full-resolution comparisons between the AGB estimates produced from ALOS PALSAR in this study and the four other datasets for three subsets selected across different biomes of Africa. In the forest-savannah transition zone depicted in Fig. 8 , it can be seen that the datasets by Saatchi and Baccini have a generally higher biomass in the savannahs than our estimates, which have a range of AGB similar to the dataset by Avitabile. A visual inspection of high-resolution optical imagery available in Google Earth reveals that the very low biomass areas (0-10 Mg•ha -1 ) depicted in our dataset and in Avitabile's actually correspond to treeless areas. Therefore, the datasets of Saatchi and Baccini provide in this case incorrect estimates, around 30-40 Mg•ha -1 , probably because the spatial extrapolation of LiDAR samples with passive optical observations is misled by the grass layer.
Besides, the two high resolution maps show fine details that cannot be seen in the three coarser resolution maps, like gallery forests along rivers. The second cross-dataset comparison represents a "tiger bush" landscape in Sudan (Fig. 9 ), i. e. a patterned vegetation community consisting of alternating narrow bands of trees or shrubs, with bare ground or low herb cover in between, that run roughly parallel to contour lines of equal elevation in arid and semi-arid regions  (Lefever and Lejeune, 1997). These patterns are too fine to be observed in the coarse resolution maps, but the 30 m resolution map by  Baccini et al. (2015)  also fails to capture these features, while they are clearly visible in the PALSAR map as well as in the very high resolution optical image shown in the figure. Our biomass estimates could therefore be helpful to understand the distribution and formation of tiger bush and other similar patterns occurring between savannah, forest and grassland on a wide scale, as a potential extension to what was done in small-scale studies using high-resolution optical imagery  (Deblauwe et al.
, 2011). Fig. 10  shows the precision and accuracy datasets calculated on a pixel basis as described in Section 4. 7. The percent precision error represents σ AGB est /AGB est , where σ AGB est is the standard deviation of the AGB estimates obtained through repeated inversions affected by field and SAR measurement errors. In the savannahs and woodlands, this value is remarkably stable, at around 11% in the dry season areas and 14% in the wet season areas. The higher error values in the wet season areas reflect the smaller dynamic range of the backscatter-AGB relationships. In areas of very low/high AGB values (i. e. low/high HH and HV backscatters), the precision error falls to very low values as the AGB estimator consistently gives values close to the minimum/maximum invertible value. The 95% HPDI is represented by a map of its lower and higher bounds.
Monte-Carlo cross-validation In this approach, we randomly split the reference dataset (N = 144) a thousand times into two equal subsets, one for training and one for validation, for the wet season area and the dry season area separatelyAt each iteration, the a and c parameters of the direct model are calibrated using the training dataset as described in Section 4. 3, and the predictive accuracy of the Bayesian inversion is assessed using the validation data. Fig. 11  shows the distribution of the Root Mean Square Difference (RMSD) and correlation coefficient ρ of the corresponding 1000 AGB inversions. The overall mean RMSD, calculated at the plot scale, is 17. 0 Mg•ha -1 (16. 6 Mg•ha -1 for the wet season area and 17. 1 Mg•ha -1 for the dry season areas) and the overall correlation coefficient is 0.
76 (0. 75 for the wet season area and 0. 77 for the dry season areas). Note that these figures are calculated by keeping only in situ AGB estimates lower than 100 Mg•ha -1 in the analysis, because most field plots higher than 100 Mg•ha -1 are masked out during the post-processing, and they are overrepresented in our samples compared to the real distribution of AGB in the woodlands and savannahs of Africa. Including them in this cross-validation provides degraded results which are not thought to be representative of our dataset, which focuses on the 0-100 Mg•ha -1 biomass range: the overall mean RMSD reaches 46. 3 Mg•ha-1 (wet season: 69. 3 Mg•ha -1 , dry season: 22. 9 Mg•ha -1 ), and the overall correlation coefficient decreases to 0. 60 (wet season: 0. 61, dry season: 0. 69).
12 m) were computed by subtracting the DEM from the CSM. The AGB LiDAR derived metric was modelled using a linear regression, ground estimated AGB (within 25 m field plots) and a simple H × CC LiDAR metric (where H is the mean top-of-canopy height and CC is the canopy cover of a 25 m pixel resolution)  (Colgan et al. , 2012). The RMSD of the LiDAR derived AGB estimates is reported to be 19. 2 Mg•ha -1  (Naidoo et al. , 2015). We calculated the coefficient of determination ρ and RMSD between the AGB estimates from this LiDAR dataset on one hand, and the AGB estimates from the PALSAR data or from the 30 m resolution dataset of  Baccini et al. (2015)  on the other hand. The PALSAR dataset produced in this study generally performs better than the 30 m Baccini dataset over these savannah and woodland landscapes, with a ρ of 0.
84 (0. 58 for Baccini) and RMSD of 7. 7 Mg•ha -1 (10. 8 Mg•ha -1 for Baccini), despite a slight overestimation for AGB values larger than 40 Mg•ha -1. The corresponding scatterplots are shown in Fig. 13 , and reveal that unlike our estimates, the estimates from  Baccini et al. (2015)  are significantly biased with respect to the LiDAR estimates. Note that the outstanding agreement between our dataset and the LiDAR dataset, although covering a variety of soil types (granitic, basaltic) and land management practices (communal rangelands, protected areas), is limited to a small region of a specific biome (the lowveld) and may not be representative of the whole African savannahs.
Analysis of carbon stocks in African countries The AGB dataset produced in this study was used to assess the above-ground biomass and carbon contained in all African countries, except the small insular countries (Cape Verde, Comoros, Mauritius, São Tomé and Príncipe, and Seychelles). To account for the limitations of the method, which estimates AGB only until 85 Mg•ha -1 , we assigned to the masked dense forest and flooded forest pixels an AGB of 300 Mg•ha -1. This value was chosen as a trade-off between the AGB estimates of tropical forest reported in  Saugier et al. (2001)  (390 Mg•ha -1) and in FAO (2001) (190 Mg•ha -1 ), and of African equatorial forest reported in IPCC (2006) (400 Mg•ha -1 ) and  Gibbs and Brown (2007)  (198 Mg•ha -1 ). Mangroves were assigned an AGB value calculated in each country from the  Saatchi et al.
(2011)  estimates. The total AGB of each country is calculated from the ALOS PALSAR AGB dataset, and transformed to above-ground carbon stocks by using a 0. 5 conversion factor. The C stocks per country (broken down into dense forests, savannahs and woodlands, and total) are reported in Table  S2 , together with the proportion of the country area masked as dense forests, flooded forests, mangroves, urban areas, and water bodies. Fig. 14  shows a comparison between the national carbon stocks estimated in this study and those estimated by FAO,  Saatchi et al. (2011 ), Baccini et al. (2012)  and  Avitabile et al. (2016). The corresponding values are also reported in Table  S2. Our carbon stocks estimates are generally very well correlated with the other remote-sensingbased estimates (correlation coefficient ρ between 0.
Our total estimates are very similar to those of  Saatchi et al. (2011)  (-1. 5%), while we provide significantly lower values than  Baccini et al. (2012)  (-11. 8%), and higher values than  Avitabile et al. (2016)   (+ 19. 4%). These discrepancies can be further analysed by considering the calculated carbon stock density in each country (in MtC•ha -1 ). Fig. S2  reveals that our dataset tends to provide lower AGB estimates than  Baccini et al. (2012)  in countries dominated by savannahs and woodlands and higher AGB estimates in the countries dominated by dense forests, while the opposite is true with  Avitabile et al. (2016). The comparison between the mean national carbon stock densities calculated in our study and in  Saatchi et al. (2011)  show highly-correlated and almost unbiased estimates.
Caveats While the validation approaches adopted in Section 5. 2 indicate that the AGB dataset described in this paper has a good accuracy (RMSD of 17 Mg•ha -1 for the cross-validation with the field plots from all Africa, and 7. 7 Mg•ha -1 for the local validation in the South African lowveld), it is still worth mentioning a few caveats linked to the intrinsic limitations of the PALSAR mosaic dataset and of the method. First, as already mentioned in Section 4. 1, our approach consists in estimating AGB separately in the "wet season areas" and in the "dry season areas" in order to minimize the backscatter variation linked to non-homogeneous environmental conditions (soil moisture levels or vegetation phenology). However, these environmental conditions can locally deviate significantly from the average environmental conditions of the area they belong to (wet season area or dry season area), for example in case of a heavy rainfall shortly before the PALSAR acquisition in the dry season area or an exceptional draught in the wet season area.
6, can also locally degrade the information content of our dataset. Because the land cover datasets used for masking have a coarser resolution and have their own errors, of, these areas are imperfectly masked out. In particular in forest/savannah transition areas, it is expected that small dense forest pockets and gallery forests will not be masked out and will be assigned an AGB value lower than their actual value, while small savannah pockets inside dense forest areas will be masked out. The national estimates provided in Section 5. 3 can therefore be affected by the quality of the masking, and by other assumptions used  in the calculation (e. g. the assignment of an AGB equal to 300 Mg•ha -1 for the dense forests). The overall accuracy of the retained/not-retained classification used for masking (where the "not-retained" class contains all the CCI Land Cover classes for which we do not want to provide AGB estimates, i.
Conclusion Based on a dual-polarisation PALSAR mosaic produced from ALOS PALSAR imagery acquired in 2010, we developed the first 25 m resolution wall-to-wall map of Africa dedicated specifically to the AGB of savannahs and woodlands, a biome of paramount importance in the carbon budget of this continent. Visual comparisons of our AGB dataset with the other AGB datasets at similar (30 m) or coarser (300 m to 1 km) resolutions, and with independent estimates from an airborne LiDAR dataset in a savannah area, highlight the merits of our dataset in the savannahs and woodlands areas, both in terms of resolution and sensitivity. Uncertainties linked in particular to the effects of the environmental conditions are minimised by performing the AGB inversion in "dry season" and "wet season" areas separately.