dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS This work was supported by the  Prix Bettencourt Coups d'E ´lan pour la Recherche Francaise  attributed to Y. G. , a grant from  Agence Nationale de la Recherche ,  France  ( ANR CE11 , LiquidFact) to Y. G. (including a post-doctoral grant to Q. N. ), and a grant from  Fondation pour la Recherche Medicale, France , to Y. G. (including a post-doctoral grant to D. G. ). This work has benefited from the core facilities of  Imagerie-Gif , supported by  France-BioImaging  ( ANR-10-INBS-04-01 ). We thank  Institut Jacques Monod ,  CNRS ,  Region Ile de France , and  GIS IBiSA  for financial support of the ProteoSeine@IJM platform. We thank  Aurelie Albertini  for careful reading of the manuscript.
Materials and methods To map above-and belowground woody biomass in China during 2003-2020, we 1) integrated state-of-the-art satellitederived forest AGB and canopy height information with ground-sourced plot data; 2) developed an improved vegetation water content (VWC) dataset covering 2003-2020 based on the intercalibration of vegetation optical depth retrieved from various microwave sensors; 3) combined multiple sources of remote sensing and in-situ observations simultaneously https://doi. org/10. 5194/essd-2022-286 Preprint. Discussion started: 26 September 2022 c Author(s) 2022. CC BY 4. 0 License. allowing for both high-resolution and long-term mapping; 4) built a plot data-based random forest or regression model to estimate BGB based on AGB information; and 5) harmonized scale differences existing between plot measurements and satellite observations.
An improved benchmark map of aboveground biomass (AGB) in China We used the three latest high-quality global-scale AGB datasets (GlobBiomass, CCI-Biomass and GLASS-Biomass v2) to derive our benchmark map. Using SAR, LiDAR and optical images, the first global high-resolution (100 m) forest AGB dataset for the year 2018, GlobBiomass, was published through the European Space Agency (ESA)'s Data User Element (DUE) project  (Santoro et al. , 2021). The relative root mean square error (RMSE) was below 30%, although biomass tends to be underestimated in dense forests  (Mialon et al. , 2020). Subsequently, ESA's Climate Change Initiative (CCI) published a global AGB map for all vegetation in 2017 using a slightly different algorithm, followed by AGB maps for 2010, 2018 and 2019  (Santoro and Cartus, 2019, 2021).
Datasets derived from different methods have their advantages in different regions. By referring to adequate in-situ data, we may combine the advantages of different datasets. Accordingly, integration of several high-quality AGB maps has become popular. The Global Land Surface Satellite (GLASS) AGB v2 was developed by fusing the AGB maps in  (Hu et al. , 2016) ,  (Su et al. , 2016)  and  (Thurner et al. , 2014) , etc. , through the linear combination method. GLASS-Biomass v2 roughly represents the AGB status in the year 2000, the median period of the collected woody plots' data  (Zhang and Liang, 2020). We aggregated the GlobBiomass and CCI-Biomass maps from 1/1125° resolution to 1/120° (approximately 1 km) and resampled the GLASS-Biomass product from 0. 01° to 1/120°. Because the biomass of herbaceous vegetation was not included in GlobBiomass and GLASS-Biomass datasets due to the high uncertainty, the study area in this study was determined as all the 1/120° pixels (note: the 1/120° resolution is referred to as a pixel hereinafter) for which AGB data are available from each of these three woody biomass datasets.
In September 2018, ICESat-2 provided an Advanced Topographic Laser Altimeter System (ATLAS) that provides more accurate and denser measurements of canopy height than GLAS  (Markus et al. , 2017). ICESAT-2 data have been applied in canopy height estimation at large scales  (Liu et al. , 2022). This study selected the ATL08 land and vegetation V004 product, and the 98% height retrievals of all canopy photons in each 100 m segment can best represent the mean top canopy height  (Neuenschwander et al. , 2020). All ATLAS records acquired in China's woody areas during 2018~2020 were incorporated. ATLAS has three strong and three weak beams. According to previous studies, the canopy heights retrieved using strong beams are generally more accurate than those retrieved by weak beams  (Neuenschwander et al.
, 2020). Hence, in each 1/120° resolution pixel, we counted the numbers of valid strong beam and weak beam observations within the pixel during 2018~2020. If the number of strong beam records exceeded 5, then only those higher quality data were used. Otherwise, if there were at least 5 valid observations, but the number of strong beam retrievals was not enough, all data in the pixel were https://doi. org/10. 5194/essd-2022-286 Preprint. Discussion started: 26 September 2022 c Author(s) 2022. CC BY 4. 0 License. incorporated. Afterwards, we adopted the median absolute deviation (MAD) method to detect and eliminate outliers  (Leys et al. , 2013). For the remaining reliable canopy height retrievals in each pixel, we took the average weighted by the corresponding canopy cover fractions. Here, the top canopy cover fraction was estimated as the ratio of canopy photons to the number of all photons in the 100 m segment.
By following the above steps, we mapped forest height over China using ATLAS data. Because gaps remain between the tracks of ICESAT-2, although ATLAS's six beams enable a larger spatial coverage than other LiDAR instruments, the derived forest height map provides values in only 42% of all woody pixels in China. Because of the potentially bad LiDAR estimates, the highest 2. 5% extreme values were further excluded. Another new LiDAR instrument is NASA's Global Ecosystem Dynamics Investigation (GEDI). It is optimized for global canopy height estimation, and has been collecting data in China since April 2019  (Dubayah et al. , 2020). However, the orbital gaps of GEDI are much larger than that of ICESAT-2, resulting in a quite limited spatial coverage by direct observation. Therefore, this study adopted the Global Forest Canopy Height 2019 dataset provided by the Global Land Analysis and Discover (GLAD).
GLAD's canopy height was mapped by integrating the GEDI's forest structure measurements globally with Landsat maps through machine learning  (Potapov et al. , 2020). The original 30 m resolution data were averaged to 1/120°. GEDI does not collect data in north of 51. 6°N  (Dubayah et al. , 2020) , but the highest latitude of China is about 53. 56°N. Therefore, we used an alternative global gridded forest height map that was developed earlier through machine learning, yet based on the ICESAT GLAS retrievals  (Simard et al. , 2011). Because AGB is more related to the forest volume rather than just the canopy height, we further multiplied the three different LiDAR-based canopy height maps with the percent of tree cover (hereinafter TC) acquired from MOD44B v006-dataset. The products of multiplications are hereinafter ATLAS-derived volume, GEDI-derived volume and GLAS-derived volume.
The training target should be a large number of high-quality 1/120° pixel-scale AGB data in China. This study collated forest or shrubland plots' AGB calculated using allometric equations or clear-cutting methods from various published papers, including  (Luo, 1996) ,  (Luo et al. , 2014) ,  (Guo and Ren, 2014) ,  (Peng et al. , 2016) ,  (Wang et al. , 2014) ,  (Guo et al. , 2021) ,  (Yang et al. , 2017) ,  (Liu et al. , 2020)  and  (Nie et al. , 2016). In addition to AGB, we recorded the BGB, stand age, vegetation species or type and location information. The spatial distributions of all these woody plots are shown in Figure  S1a. Some records were measured in 1990~2000, whereas others were acquired during 2000~2010. Although the nominal periods of plot measurements are different from those of the remote sensing-based predictor biomass maps (i.
Here, land cover type comes from the Copernicus Global Land Service: Land Cover 100m (CGLS-LC)-epoch 2017: v3. 0. 1 data  (Buchhorn et al. , 2020). It includes not only discrete land cover classification but also the fractions of forestland, shrubland, grassland and cropland at 100 m resolution. We aggregated these high-resolution land cover fractions to 1/120° to reduce the uncertainties. It should be noted that VCF data cannot be applied here because they indicate the fractions of pure tree cover and short vegetation cover, yet a forestland contains bare ground or herbs among trees. The RF model trainings were conducted in MATLAB R2021a software. After the RF trainings, three sets of simulations were https://doi. org/10. 5194/essd-2022-286 Preprint. Discussion started: 26 September 2022 c Author(s) 2022.
An improved vegetation optical depth dataset covering 2003-2020 To derive a long time series of AGB, long-term continuous microwave VOD (i. e. , vegetation opacity) data is useful  (Jackson and Schmugge, 1991; O'neill et al. , 2021). Through cumulative distribution function (CDF) matching among different VOD products, the vegetation optical depth climate archive (VODCA) was developed  (Moesinger et al. , 2020). The 'C-band' product which was retrieved using several C-band microwave sensors including AMSR-E, WindSat and AMSR2 and covers 2003~2018, is a better indicator of the whole woody plants' biomass than those retrieved using higher frequency microwave bands (i. e. , X-band and Ku-band). However, compared to the VOD retrieved from L-band sensors such as SMOS  (Wigneron et al. , 2021)  and SMAP  (Konings et al.
, 2017) , C-band VODs are still less sensitive to the biomass of trunks and branches, which contribute most to the AGB  (Li et al. , 2021). Therefore, C-band VODs usually have high-frequency variations due to the strong variation in leaf water content per biomass under rainfall or drought events  (Li et al. , 2021). In addition, VODCA's C-band product is not perfectly continuous. Because the AMSR2 sensor does not share any temporal overlap with AMSR-E, the rescaling of AMSR2 data towards AMSR-E was based on the assumption that VOD remained stable over 2010~2013, i. e. , extending the VODCA dataset to 2020 by using AMSR2 observations. Details are as follows. Due to a very high level of radio frequency interference (RFI), SMOS data are noisy and even missing in China, especially in eastern China  (Wigneron et al.
Moreover, we also determined the average annual number of VOD peaks for each grid after setting the thresholds of minimum distance between two peaks, peak height and dominance of peaks to reasonable values. Specifically, for grid cells where woody plants exist yet without VOD data, the values were filled by sequentially searching and averaging nearby valid values  (Chen et al. , 2019b). For VODCA's C-band VOD during 2003~2018, after filtering out the abnormal values and virtually filling the data in 2002 and 2019, we performed the Harmonic Analysis of Time Series (HANTS) filtering  (Menenti et al. , 1993; Roerink et al. , 2000). Either high or low outliers were excluded, while the number of frequencies to be considered above zero frequency in the Fourier function was set to the product of the mean annual peak number detected by SMAP VOD and the number of years.
In the RF model, the training target is the per-area BGB at 8182 forest plots, while the predictors included not only forest plots' per-area AGB, forest type (hereinafter FOR_T), stand age, but also mean annual temperature (MAT), temperature seasonality (standard deviation of monthly temperature×100, abbreviated as Tsea), mean annual precipitation (MAP) and precipitation seasonality (coefficient of variation of monthly precipitation, Psea). These climatic factors can be obtained from the corresponding papers, or estimated from the WorldClim v2. 1 dataset  (Fick and Hijmans, 2017). In this study, FOR_T includes evergreen broadleaf forest (EBF), deciduous broadleaf forest (DBF), evergreen needleleaf forest (ENF), deciduous needleleaf forest (DNF), and mixed forest (MF), and was determined based on the major tree species in the plot.
After the RF   (Li et al. , 2018). Because shrubland plots are relatively limited, and the species and stand age information was hardly provided, we directly converted the decomposed shrublands' per-area AGB into the per-area BGB during 2003-2020 using the above regression relationship. Finally, by referring to the forestland and shrubland area percentages in the CGLS-LC dataset, we mapped the annual woody BGB in China at 1/120° resolution. In addition, we also calculated the relative errors and uncertainties of AGB and BGB in each year during 2003-2020 (see section 3. 4).
Data comparison and verifications Apart from cross-validations based on woody plots' AGB and BGB measurements over China, we further verified our AGB and BGB estimates by referring to the results of Tang et al. , who conducted a methodologically consistent field campaign involving 7800 forest plots and 1200 shrubland plots throughout China and then utilized the random forest approach to spatially map AGB and BGB  (Tang et al. , 2018). Although the plot measurements are not publicly available, various statistics of this national official product were reported, e. g. , the AGB, BGB and RSR for each woody vegetation type. Therefore, using the discrete land cover classification map in the CGLS-LC dataset, we classified China's woody ecosystems into six woody ecosystem types, i. e. , EBF, DBF, ENF, DNF, MF and shrubland (SHR) ecosystems, according to the majority in every pixel (Figure  S7 ).
The EBF, DBF, ENF, DNF, MF and SHR ecosystems account for 59. 6%, 18. 4%, 15. 3%, 6. 1%, 0. 1% and 0. 5%, respectively, of the total woody area in China. Because the MF and SHR ecosystems both have very limited areas, this study just compares the AGB and BGB per area among the four major forest ecosystems in China. Similarly, the change in woody biomass or carbon stock in China can be verified by several measurements and remote sensing-based studies  (Fang et al. , 2018; Qiu et al. , 2020; Xu et al. , 2018). We also compared the calculated spatial pattern of woody biomass and its trend against that of existing global/regional longterm woody biomass datasets, including the well-received global long-term AGB between 1993~2012  (Liu et al. , 2015)  and an updated woody biomass dataset covering 2001~2019  (Xu et al.
, 2021). Finally, to justify the random forest models for biomass and allocation predictions, we drew partial dependence plots  (PDPs)  in MATLAB R2021a ® to show the marginal effect that one predictor has on the training target (e. g. , BGB at forest plots) from a machine learning model  (Hastie et al. , 2009). Here, for each predictor, we excluded the extreme values (the lowest 1% and the highest 1%) before calculating the corresponding PDP to ensure its robustness. Ten RF trainings were performed to derive the mean PDP values as well as the standard deviations.
Model development and Evaluation In the first step, i. e. , the benchmark woodland AGB mapping, when ATLAS data-derived canopy height was applied as an additional predictor in the RF model apart from the three biomass datasets (see section 2. 1), the number of data points available for training was 1392, and the predicted R-square (R 2 ) according to the ten-fold cross validation was 0. 49±0. 06 (mean ± standard deviation). The training efficiency is limited by the potential errors in plot-level AGB records as well as the CGLS-LC's land cover fraction maps, and the scale difference between satellite and plot-level observations. Although introducing climatic and topographic variables as predictors could increase the R 2 of ten-fold cross validation, these variables contain high spatial autocorrelation, and thus even an elevated R 2 cannot indicate a higher predictive performance  (Ploton et al.
, 2020). According to a test addressing the relative contribution of the four predictors, GLASS-Biomass dataset contributed most to the woody AGB mapping (35%), followed by GlobBiomass (24%), CCI-Biomass (22%) and ATLASderived tree volume (19%). When GEDI-derived and GLAS-derived wood volumes were respectively used, the available data points increased to 3842 and 2286, but the mean R 2 values were 0. 36, because the direct observations of ICESAT-2 were supposed to be more accurate than the canopy heights estimated through machine learning by GLAD or Simard et al. After combining these three sets of AGB simulations, the R 2 between the resulting benchmark AGB map for China (Figure  S1c ) and the upscaled plots' AGB was 0. 56, while the RMSE was 40. 38 t ha -1 (Figure  S1b ). When comparing the GlobBiomass, CCIBiomass and GLASS-Biomass datasets against the upscaled plots' AGB, the R 2 were 0.
We predict higher woody biomass in the central-south and southwest China but lower biomass values in the northern and northwest regions (Figure  S9a~f ). The spatial pattern of our AGB map agrees well with that of recent high-quality China's forest AGB maps which were developed by integrating Lidar, P-band SAR and forest inventory data  (Su et al. , 2016; Huang et al. , 2019)   S11a, c ). Spatially, this study predicted an obviously faster biomass gains in southern China than their datasets (Figure  S11 ). Reforestation and forest management led to a short term extensive carbon sequestration in southern China, which was estimated as 220±100 Tg AGB yr -1 during 2002~2017 by developing a regional RF model between MODIS well  (Wang et al. , 2020; Yang et al. , 2021). Accordingly, by fusing low-frequency active, passive microwaves and the advanced LiDAR-derived canopy heights under the reference of extensive field measurements, this study could generate updated estimates on the spatial hotspots of woody biomass and its trends in China.
Uncertainties of the woody biomass dataset and future prospects The uncertainties of AGB in this study came from four sources: 1) the improved benchmark AGB map for China; 2) the extension of AGB time series based on the long-term integrated VOD and high/short vegetation coverage datasets; 3) downscaling of coarse resolution AGB; and 4) the AGB decomposition process. The uncertainty in BGB was composed of the error within the AGB time series and the prediction uncertainty of the models that transform AGB to BGB. The details for the calculation of each source of error are in Text S2. Finally, the annual AGB and BGB's relative error can be calculated as the square root of the sum of squares of all relative errors, which is referred to as the 'error propagation rule' when assuming that each error is independent and random  (Huang et al.
, 2021). By multiplying the annual AGB or BGB map with the corresponding relative error, we mapped the AGB and BGB's uncertainties in China annually during 2003-2020. As shown in Figure  S12 , the spatial patterns of the relative errors of AGB and BGB are similar. Relative errors were lower in the pure forests located in northeast China and the south Tibet, and high in the mixed forest and shrublands in the southwest karst regions and part of North China. A recent study revealed that the variation in VODs is correlated with not only biomass, but also soil moisture availability  (Konings et al. , 2021). To alleviate this source of uncertainty as much as possible, we have incorporated the satellite-based surface soil moisture dataset to account for the impact from the interannual variation in water content per biomass.
For example, BIOMASS, a fully polarimetric P-band SAR, is scheduled to be launched in 2022  (Le Toan et al. , 2011). Therefore, in the future the relationship between P-band microwave retrievals and biomass should be addressed, as well as the calibration of historical AGB datasets (e. g. , the long-term AGB dataset in this study) against the P-band SAR-based AGB benchmark map to extend the time series. On the other hand, more in-situ AGB and BGB measurements in larger plots and in approximate years are needed to further improve the estimation of belowground biomass allocation.
Acknowledgements We are grateful to all the data contributors, especially  Yuan Zhang  and  Huabing Huang  for sharing the forest stand age map and forest aboveground biomass map in China to us. Financial support This work was supported by the  National Key Research and Development Program of China  ( 2017YFA0604700 ),  National Science Foundation of China  ( 41991233 ) and the  Chinese Academy of Sciences  ( QYZDY-SSW-DQC025 ). https://doi. org/10. 5194/essd-2022-286  Preprint. Discussion started: 26 September 2022 c Author(s) 2022. CC BY 4. 0  License.
https://doi. org/10. 5194/essd-2022-286 Preprint. Discussion started: 26 September 2022 c Author(s) 2022. CC BY 4. 0 License. the last and first two years of both sensors. For China where land use cover changes were prevalent, this assumption may lead to a bias of AMSR2-based VODCA data during 2013~2018 compared to the values in this period if the retrievals from AMSR-E were available. Theoretically, this bias is spatially variable, and is positively correlated with local VOD changes from 2010 to 2013. Thus, to develop an improved VOD dataset covering 2003-2020 for China, we focused on 1) filtering out the high-frequency fluctuations in VODCA's product and other C-band VOD products; 2) mapping and correcting the bias of AMSR2-based VODCA data during 2013~2018 compared to the AMSR-E-based VODCA VOD; 3) rescaling the Cband VODs against L-band VOD data to make their spatial patterns more correlated with that of woody plants' AGB; 4).
Finally, to ensure a temporally-consistent VOD time series from 2003 to 2020, for 2019 and 2020, we adopted the AMSR2 C-band VOD and converted it into L-band-like VOD by referring to SMAP data. Here, we chose the AMSR2/GCOM-W1 surface soil moisture (LPRM) L3 1 day 10 km x 10 km descending V001 dataset, and resampled it to 1/12° resolution. Upon noticing sharp changes in AMSR2 VOD at the beginning of 2016 in many grid cells in China, this study selected the records https://doi. org/10. 5194/essd-2022-286 Preprint. Discussion started: 26 September 2022 c Author(s) 2022. CC BY 4. 0 License. from only 2017~2020, which were calibrated and rescaled later through improved CDF matching by referring to the SMAP's VOD during 2017~2018. Using the temporally-continuous and spatially L-band-like VOD dataset from 2003 to 2020, we calculated both annual mean and median VOD values as two robust indicators of annual AGB.
Methods We used multiple spatial datasets (Supplementary Tables  3  and 4 ) to implement a prioritization approach to identify priority geographies (that is, countries and production systems) for livestock system investments and to identify adoption constraints to adaptation and mitigation options. We selected two main challenges related to climate adaptation and mitigation strategies: (1) livestock production and producer livelihoods are threatened by short-and long-term rainfall and temperature variations and their changing predictability; and (2) livestock are responsible for a substantial proportion of national and global GHG emissions. A fully detailed description of the methods is provided in the Supplementary Information. We used the livestock production system classification of Robinson et al.
Analysis of adaptation We performed a geospatial data analysis to characterize livestock production systems and producer livelihoods and to identify threats related to short-and long-term variations in rainfall and temperature. First, we mapped regions projected to experience climatic hazards and masked them by their adaptive capacity within the livestock systems (Supplementary Fig. 8 ). Second, we assessed the exposure per hazard within each country and livestock system (Supplementary Fig. 9 ). Exposure was represented by the total VOP  42  , total rural population  43  , pasture area  44  and TLUs. TLU values were computed following Rothman-Ostrow et al. 45  using data from the Gridded Livestock of the World database (version 3)  46. VOP data were derived from ref. 42, which used a combination of modelling and FAOSTAT data 13 to produce geospatial datasets of the value of livestock production.