dataesr/scientific-paragraphs-categorization huggingface dataset:
g. ,  [68] ). From the two approaches that directly train the AMSR-E brightness temperatures to best match the SMOS L3 dataset, the global NN is better able to match SMOS L3 than the pixel-based REG approach, benefitting from a more complex training method that seems to be better able to account for frequency dependent differences like the vegetation attenuation. The physically based LPRM approach is able to perform in between the two direct training methods, while only optimizing for the vegetation and soil roughness parameter in the model, making AMSR-E LPRM less dependent on SMOS LPRM. The NN results however, show that there is a possibility that LPRM does not yet optimally account for the vegetation influences during dense vegetation conditions. Figure  3  shows the distribution of sample sizes (N) that are used within the NDVI binning procedure in this study.
Between an NDVI range of 0. 15 and 0. 65, the used points per bin are between 30% and 60% of the total possible points over our quasi-global study area. For regions with NDVI values under 0. 15, still >500 points remain per bin but becomes a smaller fraction of the total, while for an NDVI > 0. 65 the total used points decreases quickly. This is caused by an insufficient amount of precipitation events for the Rvalue approach (Sections 3. 2 and 4. 2) in the NDVI < 0. 15 region. Few retrievals over the dense forest in the SMOS LPRM dataset lead to reduced availability in the NDVI > 0. 65 bins, leaving to little overlapping samples for the comparison. Over the evaluated NDVI range, pixels with RFI occurrence in either L-, C-, or X-band are removed, leading to the removal of almost all of Eurasia due to L-band RFI  [63]  and gaps over regions like the US and India due to C-and Xband RFI  [44].
Comparison AMSR-E and SMOS Soil Moisture Figure  2  shows the results for the comparison of the three fusion approaches, i. e. , for AMSR-E LPRM against SMOS LPRM (LPRM), AMSR-E NN against SMOS L3 (NN) and AMSR-E REG against SMOS L3 (REG). Figure  2A  presents the correlations versus the average annual NDVI. NN gives the highest correlations between SMOS and AMSR-E soil moisture with temporal correlation coefficients above 0. 7 for NDVI values under 0. 7. The other two fusion datasets (LPRM and REG) reveal slightly lower correlations, but very similar to each other, having temporal correlation coefficients above 0. 7 for NDVI values close to 0. 6. REG shows higher correlations than LPRM for very low (<0. 15) values, while LPRM has slightly higher correlations between an NDVI of 0. 15 and 0.
Between an NDVI range of 0. 15 and 0. 65, the used points per bin are between 30% and 60% of the total possible points over our quasi-global study area. For regions with NDVI values under 0. 15, still >500 points remain per bin but becomes a smaller fraction of the total, while for an NDVI > 0. 65 the total used points decreases quickly. This is caused by an insufficient amount of precipitation events for the R value approach (Sections 3. 2 and 4. 2) in the NDVI < 0. 15 region. Few retrievals over the dense forest in the SMOS LPRM dataset lead to reduced availability in the NDVI > 0. 65 bins, leaving to little overlapping samples for the comparison. Over the evaluated NDVI range, pixels with RFI occurrence in either L-, C-, or X-band are removed, leading to the removal of almost all of Eurasia due to L-band RFI  [63]  and gaps over regions like the US and India due to C-and X-band RFI  [44].
R value Technique In Figure  4  the binned results of the precipitation-based R value verification technique are shown. The highest R value in the NDVI domain below 0. 35 is found for the AMSR-E LPRM dataset. The SMOS datasets reach the overall highest R value during moderate vegetation conditions, for NDVI values between 0. 35 and 0. 55. When NDVI goes over 0. 55, ASCAT soil moisture reaches the highest R value. The results for the different AMSR-E datasets vary significantly, with AMSR-E LPRM showing the higher values for the low vegetated regions compared to AMSR-E NN and AMSR-E REG, while at high NDVI values of over 0. 55 the AMSR-E NN dataset is able to sustain a higher R value. AMSR-E REG has the lowest values compared to the other AMSR-E products (i. e. , NN and LPRM) for NDVI > 0. 35, while performing reasonable over lower NDVI values.
The two SMOS products (i. e. , SMOS L3 and SMOS LPRM) demonstrate a similar performance throughout. ASCAT demonstrates a slightly better performance than SMOS datasets beyond NDVI values of 0. 55. These findings demonstrate how different sensors can complement each other based on their skill in detecting soil moisture anomalies, and show the added value of SMOS L-band retrievals over moderate vegetation regimes. Although the L-band observations show better performance over more densely vegetated areas than AMSR-E, there is still a drop in skill towards denser vegetation, as seen in earlier studies on SMOS over forested regions (e. g. ,  [69] ). The results concerning the complementary nature of the different sensor are generally in line with previous studies by De Jeu et al. [22] , Al-Yaari et al.
[23]  and Dorigo et al. [24]  but provides a better indication where L-band retrievals could add value in multi-sensor soil moisture data records containing both active and higher frequency passive soil moisture retrievals. Secondly, these studies focused more on bulk statistics instead of looking at the anomaly-based skill. While the two SMOS soil moisture datasets perform very similar, the three AMSR-E datasets based on the different fusion approaches demonstrate large differences. The lower R value of AMSR-E NN and AMSR-E REG over sparsely-vegetated regions (i. e. , NDVI < 0. 35), compared to AMRS-E LPRM, might be explained by their fusion strategies. The NN and REG approach are bound by the training set of SMOS L3 and its performance over specific regions, where the physically based LPRM might have an advantage.
Secondly, this could be caused by overtraining to match the seasonal cycles of the SMOS L3 datasets over anomaly detection. On the opposite site however, NN manages to keep a more stable performance over denser vegetation, which is most likely caused by making optimal use of the multi frequency AMSR-E input and L-band-based training dataset, while LPRM is not optimally correcting for the vegetation influences. However, even though more research is needed to fully understand the differences between the AMSR-E products and how this relates to vegetation density, it also shows the complementary nature of using different fusion approaches.
It should also be noted that for the NDVI over 0. 4, the AMSR-E NN, SMOS L3, SMOS LPRM and ASCAT generally demonstrate a very similar rmse. Between the AMSR-E datasets, the switch between AMSR-E NN and AMSR-E LPRM is already found at an NDVI of 0. 4 instead of 0. 55 for R value. While AMSR-E NN remains stable between an NDVI of 0. 35 and 0. 65, like the two SMOS datasets and ASCAT, both AMSR-E LPRM and AMSR-E REG start to show a clear increase in rmse close to an NDVI of 0. 45. AMSR-E-based soil moisture is again performing better over the more sparsely vegetated areas than SMOS. Al-Yaari et al. [64]  also found this difference but the reason for it is still unknown. It might be due to the difference of the radiometric accuracy of AMSR-E (<1 K) versus the radiometric accuracy of SMOS (2-4 K), which could play a role over regions where vegetation attenuation has a smaller impact than the direct signal to noise ratio performance.
Another plausible solution might be related to the complexity to estimate the effective temperature, Parinussa et al. [36]  showed that in (semi-)arid climate regions effective temperature derived from simultaneous Ka-band observations specifically adds information compared to extracting this effective temperature input from reanalysis models, which is currently being done for both SMOS soil moisture products. Thirdly, the different sampling depth of L-band as compared to C-band, especially in (semi-)arid conditions, could lead to more complex dielectric roughness effects, which are challenging to include in the models. The use of L-band does show a solid performance over the complete NDVI range and could contribute to a multi-sensor approach throughout. From a historical point of view it is also important to note that a combination of AMSR-E and ASCAT are also able to provide such a high-quality dataset (e.
g. , as is implemented within the ESA-CCI-SM). Concerning the different data fusion approaches, the message is similar to that of the R value evaluation. The two SMOS datasets have a comparable rmse throughout the different vegetation regimes, while for the AMSR-E datasets there is a clear difference between areas with lower NDVI where LPRM has the lowest error, and higher NDVI values where NN has the lowest error. REG shows reasonable rmse over sparsely vegetated areas but is unable to cope with denser vegetation conditions. Figure  5  shows the results of the second evaluation technique, the TCA, identically binned as for the Rvalue. It should again be noted that the Rvalue is a performance metric hence higher numbers indicate a better performance while TCA produces an error metric hence lower numbers indicate a better performance.
These TCA results generally confirm the findings obtained by the Rvalue technique (Section 4. 2), however, small differences were demonstrated too. AMSR-E LPRM shows the lowest rmse values within the lower NDVI range (NDVI < 0. 4), then the SMOS products (i. e. , SMOS L3 and SMOS LPRM) takes over and demonstrate the best performance indicated by the lowest rmse values. At more densely vegetated regions, the ASCAT soil moisture anomalies demonstrate the best performance, which is indicated by the lowest rmse. It should also be noted that for the NDVI over 0. 4, the AMSR-E NN, SMOS L3, SMOS LPRM and ASCAT generally demonstrate a very similar rmse. Between the AMSR-E datasets, the switch between AMSR-E NN and AMSR-E LPRM is already found at an NDVI of 0. 4 instead of 0. 55 for Rvalue. While AMSR-E NN remains stable between an NDVI of 0.
35 and 0. 65, like the two SMOS datasets and ASCAT, both AMSR-E LPRM and AMSR-E REG start to show a clear increase in rmse close to an NDVI of 0. 45. AMSR-E-based soil moisture is again performing better over the more sparsely vegetated areas than SMOS. Al-Yaari et al. [64]  also found this difference but the reason for it is still unknown. It might be due to the difference of the radiometric accuracy of AMSR-E (<1 K) versus the radiometric accuracy of SMOS (2-4 K), which could play a role over regions where vegetation attenuation has a smaller impact than the direct signal to noise ratio performance. Another plausible solution might be related to the complexity to estimate the effective temperature, Parinussa et al. [36]  showed that in.
Results over Two Regions with a Strong Land-Atmosphere Coupling From a climate perspective, soil moisture datasets are of high interest in regions with a strong land-atmosphere coupling. In this chapter, the focus will be put on two of the largest areas that meet the criteria for such a region. The first area covers the central Great Plains of North America, and the second the Sahel region. The results of the R value and TCA for the six individual soil moisture datasets are shown in Figure  6  (the central Great Plains) and Figure  7  (the Sahel). over the forested regions in the east. The influence of the vegetation change on rmse can be seen in both AMSR-E and SMOS datasets, with AMSR-E significantly increasing to values over 0. 04 m 3 m -3 in the forested areas. Between the AMSR-E datasets, the AMSR-E NN dataset has the lowest overall rmse, followed by AMSR-E LPRM and to a lesser extent AMSR-E REG.
ASCAT demonstrates rmse values between 0. 02 and 0. 03 m 3 m -3 , slightly lower to the west and higher to the east. For the TCA, results were masked over the Chihuahuan Desert (Figure  6G-L ), as the TCA as it does not meet the standard TCA requirements over that area  [38]. The second area that was analyzed in more detail is the Sahel region, of which the results of the R value and TCA are shown in Figure  7. In the R value results, a clear split can be seen between the northwestern and southeastern part of the region. The highest values for the northwestern part are found for the AMSR-E LPRM dataset, reaching high R value of >0. 5 for the complete area, this is followed by AMSR-E REG, AMSR-E NN, SMOS L3 and SMOS LPRM, which keep values of >0. 4 for the whole region, and finally ASCAT, which has the lowest values over this semi-arid region.
In the southeastern part, covered with denser vegetation, the highest R value are found for the SMOS L3 dataset, still reaching values beyond 0. 4 in many cases. This is followed by SMOS LPRM, ASCAT, AMSR-E NN, AMSR-E LPRM and low values <0. 2 are found for AMSR-E REG. The results from TCA over the Sahel are less clear. Here four datasets showing a good performance with mainly rmse < 0. 025 m 3 m -3 , which are the ASCAT, SMOS L3, SMOS LPRM and AMSR-E NN. The AMSR-E LPRM and AMSR-E REG show several areas where the rmse is higher than 0. 04 m 3 m -3. In the southern part this reflects the area with denser vegetation and is in line with what was seen in the R value patterns; however, in the more northern parts, where AMSR-E LPRM and AMSR-REG show the highest R value , several of those areas also appear as highest rmse in the TCA.
The findings over the Sahel and central Great Plains reflect the results from the earlier sections in this study. The AMSR-E datasets, especially AMSR-E LPRM, are able to provide high-quality soil moisture datasets over the low to moderate vegetated areas and both SMOS datasets are extending the quality more towards moderate to densely vegetated areas, while ASCAT shows a reasonable performance over the whole range, being less affected by vegetation variations, but with the exception of the dry/semi-arid areas. These findings are in line with an earlier study over the Continental United States  [55]  that also used the TCA, verified through ground-based soil moisture observations. The good performance of remotely sensed soil moisture over the Sahel was also shown by Rodriguez-Fernandez et al.
[26] , where they outperformed modeled data over this region. Although globally showing similar results, it is important to note that there are differences between the two verification techniques when looking at the spatial images. Over the northern part of the Sahel this can be seen when comparing the results of the AMSR-E LPRM and AMSR-E REG datasets, the R value technique shows the best performance for these datasets, while the rmse from the TCA goes up unnaturally high. The opposite is seen for ASCAT, which might be an indication that it is an artifact introduced by the TCA. In studies by Pellarin et al. [70, 71] , AMSR-E observations were used to improve precipitation estimates over the Sahel region, and especially showed to be effective in the northern regions with sparse vegetation.
Secondly the quality of the individual datasets to detect soil moisture was evaluated using two different evaluation techniques, the precipitation-based R value technique and the Triple Collocation Analysis (TCA), including ASCAT to cover the active counterpart of satellite-based soil moisture retrievals. The results from the R value technique show the strength of using the three different data sources. AMSR-E LPRM soil moisture performed best over areas with an NDVI of up to 0. 35, the SMOS datasets perform bester over areas with an NDVI between 0. 35 and 0. 55, and a ASCAT shows the highest performance. This combination of sources manages to keep high R value between 0. 55 and 0. 4 up to an NDVI of 0. 75, slowly decreasing with vegetation density. The TCA shows many similarities to the results as found from applying the R value technique, keeping a low rmse in areas up to an NDVI of 0.
80. Again AMSR-E LPRM shows the best performance, i. e. , lowest rmse, in sparsely vegetated areas. After that, several datasets show a similar level of performance, primarily AMSR-E NN, SMOS LPRM, SMOS L3 and ASCAT. While the two SMOS datasets, i. e. , L3 and LPRM, perform very similar throughout the analysis, the three AMSR-E datasets demonstrate structural differences. AMSR-E LPRM benefits from the physical model over regions with low to moderate vegetation, showing the highest overall performance. AMSR-E NN has the advantage in areas with moderate to dense vegetation, presumably caused by the use of multiple frequencies from AMSR-E and the training to L-band observations, which are less affected by vegetation attenuation. AMSR-E REG performs reasonably well over sparse vegetation, but is unable to properly account for denser vegetation.
In the last section two specific regions with land-atmosphere coupling, the Sahel and the central Great Plains of North America, were evaluated in more detail. The findings here mainly reflect results that are found in earlier sections. When looking at both the R value and TCA results, the soil moisture retrievals derived from AMSR-E observations are able to perform best over more sparsely vegetated areas, SMOS shows a good performance over the whole range of NDVI, and ASCAT showing similar results as SMOS in the regions with denser (e. g. , NDVI > 0. 5) vegetation. It should be stressed, however, as some unnatural results can be seen in the TCA results over the Sahel, that soil moisture evaluations should preferably be done using multiple verification techniques. The currently available satellite-based soil moisture datasets, based on both passive (e.
Study" (Contract #  4000110112 ). Further funding was provided under  ESA 's  Climate Change Initiative Program  " Phase 2 of the ESA Climate Change Initiative-Soil Moisture ECV ", contract no. 4000112226/14/I-NB ). Author Contributions:  Robin van der Schalie  and  Richard de Jeu  designed this study, performed the main analysis, led the writing of the paper and prepared the LPRM based datasets. Nemesio Rodríguez-Fernández  and  Yann Kerr  prepared the NN and SMOS datasets and contributed to the writing and revising of the manuscript. Amen Al-Yaari  and  Yann Kerr  prepared the REG dataset and contributed to the writing and revising of the manuscript.
g. , secondary organic aerosol formation, gas-particle partitioning, dry and wet deposition). There are currently no detailed modeling studies conducted for the SEA domain, especially maritime SEA, which includes Indonesia with its large biomass open burning (OB) emissions. For such a modeling effort, first a reasonably accurate regional EI database should be prepared to generate input data. Several global and regional EI databases are available which also cover the SEA domain. These datasets have been developed using the activity data taken from several international data sources  (Zhang et al. , 2009; EC-JRC/PBL, 2010)  or based on the results of large-scale energy models  (Streets et al. , 2003). Efforts therefore should be put forward to update the SEA EI databases in order to generate emission input data for SEA regional modeling studies.
Our research used integrated EI and modeling tools to provide the spatial and seasonal distributions of aerosol species (PM 10 , PM 2. 5 , and BC) in SEA for 2007 and the co-benefits (for air quality, health, and climate forcing) of selected emission reduction measures for 2030. This paper presents the SEA emissions for the base year of 2007 and the WRF-CHIMERE performance evaluation. CHIMERE  (Menut et al. , 2013, and references therein)  was used to simulate three-dimensional (3-D) aerosol concentrations in the domain using the meteorological fields generated by the Weather Research and Forecasting (WRF) model  (Michalakes et al. , 2004). The model results were evaluated using available ground-based measurements of PM 10 , PM 2. 5 , and BC in several SEA cities. The extended aerosol optical depth (AOD) module (AODEM), detailed in  Landi and Curci (2011) , was applied to calculate the total columnar AOD and BC AOD.
Emission inventory and emission input data The emissions from major anthropogenic sources in Indonesia, Thailand, and Cambodia were developed following the EI framework given in the Atmospheric Brown Cloud Emission Inventory Manual (ABC EIM)  (Shrestha et al. , 2013)  using the activity data summarized in Table  1. A detailed EI methodology for Indonesia was presented in  Permadi et al. (2017b). The biomass OB categories considered in this study included crop residue open burning (CROB) and forest fires (aboveground forest fires and peatland fires). The CROB emissions (aerosol and trace gases) for Thailand for 2007 were taken from  Kanabkaew and Kim Oanh (2011) , and both CROB and aboveground forest fire emissions for Indonesia were from  Permadi and Kim Oanh (2013) , also for 2007. For Cambodia, CROB emissions for 2007 were also included in the inventory  (Permadi, 2013)  but forest fire emissions were taken from the Global Fire Emission Database v3 (GFEDv3) database  (van der Werf et al.
, 2010). CROB emissions for Thailand, Indonesia, and Cambodia were estimated from crop production statistics, residue to crop ratio, dry matter to crop residue ratio, fraction of biomass burned in the field, combustion efficiency, and emission factors. The emission results were higher than other databases that used the MODIS product (e. g. , GFEDv3) because the satellite may not efficiently capture short-lived, small-sized, sporadic CROB fires  (Permadi and Kim Oanh, 2013). For other countries in the domain, the emissions from the aboveground forest fires were from  Song et al. (2009)  while those from CROB were from GFEDv3 for the base year of 2007. The 2007 emissions from peatland fires of all countries in the SEA domain were also taken from GFEDv3 (van der  Werf et al. , 2010). The GFEDv3 database was developed using a combination of MODIS burned area and active fire prod-ucts, which is believed to better detect the peatland fires than the MODIS burned area product MCD45A1 alone for forest fire detection  (Shi et al.
Spatial distributions were made based on source activity data or relevant proxies collected for the administrative boundaries. For example, for Indonesia, the emissions were disaggregated at the district level (300 districts), while for Thailand and Cambodia emissions were presented for the provincial level (76 and 24 provinces, respectively). Further, the emissions were gridded into 0. 25 • × 0. 25 • using the Geographic Information System (GIS) tool. For other countries in SEA, the emissions of SO 2 , NO x , CO, VOC, PM 10 , PM 2. 5 , BC, and OC were taken from the available online gridded EI databases (grid size of 0. 5 • , i. e. , ∼ 50 km) compiled by the Center for Global and Regional Environmental Research (CGRER)  (Zhang et al. , 2009). The gridded CH 4 and NH 3 emissions that were not included in CGRER were taken from the global Emission Database for Global Atmospheric Research (EDGAR) (EC-JRC/PBL, 2010), with a grid resolution of 10 km×10 km.
Emissions from all considered sources were compiled for the base year of 2007 and were gridded to 0. 25 • × 0. 25 • (∼ 30 km × 30 km) for the modeling input. Monthly based activity data were obtained whenever available to construct monthly emissions, but when the data were not available relevant proxies were used. Specific country hourly variations of emissions were extracted from the available studies in SEA. For Indonesia, detailed methodology and data sources to construct monthly and hourly emissions of major anthropogenic sources are detailed in  Permadi et al. (2017b). For aboveground forest vegetation, crop residue, and municipal solid waste OB emissions, the monthly and hourly profiles were adopted from  Permadi and Kim Oanh (2013). For Thailand, the hourly profiles for power plants and industry were obtained from  Pham et al.