Map Units Research Articles

Accurate Feature Extraction from Historical Geologic Maps Using Open-Set Segmentation and Detection

This study presents a novel AI method for extracting polygon and point features from historical geologic maps, representing a pivotal step for assessing the mineral resources needed for energy transition. Our innovative method involves using map units in the legends as prompts for one-shot segmentation and detection in geological feature extraction. The model, integrated with a human-in-the-loop system, enables geologists to refine results efficiently, combining the power of AI with expert oversight. Tested on geologic maps annotated by USGS and DARPA for the AI4CMA DARPA Challenge, our approach achieved a median F1 score of 0.91 for polygon feature segmentation and 0.73 for point feature detection when such features had abundant annotated data, outperforming current benchmarks. By efficiently and accurately digitizing historical geologic map, our method promises to provide crucial insights for responsible policymaking and effective resource management in the global energy transition.

Understanding the Sorghum-growing Soils in a Semi-arid Ecosystem of Telangana, India and Assessing their Land Suitability for Sorghum Cultivation

Background: Sorghum (Jowar), a nutritionally rich, great millet is frequently cultivated in semi-arid tracts of India but at places without considering the suitable soil-sites, which result in lower productivity. Thus, understanding of sorghum-growing pedo-environment vis-à-vis crop requirements is a pre-requisite for soil-site evaluation of crop and hence present case study was carried out in Mahabubnagar rural mandal of Mahabubnagar district, Telangana state. Methods: The detailed soil survey (1:10000 scale) of Mahabubnagar Rural mandal (Mahabubnagar district), Telangana was carried out using Sentinel 2 satellite data and mapped with 66 mapping units. These mapping units along with relevant physical and chemical properties were evaluated for their suitability for sorghum farming. Result: Our results indicated that major sorghum-growing soils of Mahabubnagar Rural mandal have been grouped as Typic Rhodustalfs, Aquic Haplustalfs, and Typic Haplusterts at the sub group level. Out of the 18-soil series, major sorghum-growing soils belong to Telugugudam, Kotakadira and Potanpalle series occupying 7.56 % of the geographical area. The soil-site evaluation study indicated that the maximum area (33.99 per cent) was rated as marginally suitable with limitations of gravelliness, nutrient availability, rooting conditions, and texture while 1.51 per cent area was highly suitable for sorghum cultivation.

The nature of information and the implication for making predictions about soil properties using soil maps

Information theory is one method to evaluate how soil data can be utilised to estimate the amount of information produced by grouping or classifying the soil into classes or spatial polygons. Information theory provides the means of making “information” a measurable quantity. This paper explores how information theory can be applied to an existing, conventional soil map for the Lower Macquarie alluvial plain in New South Wales. It uses stepwise interpretation tables to estimate information levels for pH, salinity, ESP and shrink swell potential. An estimate is made of the amount of information produced for each of the soil profile classes (SPCs) and the soil map units. The amount of information produced varied between soil properties and for different soil depths. It also varied between the SPCs and soil map units. The soil map identified high amounts of available information for some soil properties, particularly in the surface soils for salinity and ESP. Moderate levels of information were generally available for pH. For other soil properties, only low amounts of information were available, especially for salinity and shrink swell potential in the deep subsoils. The overall estimate indicated that the soil map provided 50% of the total potential available information. The amount of available information produced for the soil properties was shown to be highly correlated to the standard deviation of the soil properties. The application of information theory and the use of stepwise interpretation tables was shown to be a useful method of evaluating the amount of information produced by a soil map for a range of soil properties.

Self-supervised multimodal change detection based on difference contrast learning for remote sensing imagery

Most existing change detection (CD) methods target homogeneous images. However, in real-world scenarios like disaster management, where CD is urgent and pre-changed and post-changed images are typical of different modalities, significant challenges arise for multimodal change detection (MCD). One challenge is that bi-temporal image pairs, sourced from distinct sensors, may cause an image domain gap. Another issue surfaces when multimodal bi-temporal image pairs require collaborative input from domain experts who are specialized among different image fields for pixel-level annotation, resulting in scarce annotated samples. To address these challenges, this paper proposes a novel self-supervised difference contrast learning framework (Self-DCF). This framework facilitates networks training without labelled samples by automatically exploiting the feature information inherent in bi-temporal imagery to supervise each other mutually. Additionally, a Unified Mapping Unit reduces the domain gap between different modal images. The efficiency and robustness of Self-DCF are validated on five popular datasets, outperforming state-of-the-art algorithms.

Physicochemical characteristics of andisols and their correlation to potato yield based on land mapping units

The high phosphate retention in andisol soil is often associated with a decrease in potato yield. Additionally, identification of the physical and chemical characteristics of andisols and their correlation to potato yield through soil mapping units (SMUs) is necessary to facilitate field surveys. Therefore, this study aimed to identify the physicochemical characteristics of andisols with a focus on available phosphorus (P) and the correlation to potato production based on SMUs. The experiment was conducted in Karo District, North Sumatra, Indonesia, from July 2022 to February 2023. A descriptive-analytical method was used by overlaying maps of slope, soil types, and altitude until 10 SMUs were selected that were planted with potatoes. Soil physicochemical properties were identified, while Pearson correlation analysis was performed on available-P and potato yield using IBM SPSS software. The results showed that the silt and clay fractions positively correlated with available-P in andisols. All soil physical characteristics were categorized as very weak to weak in supporting potato yield. A total of four SMUs including 1, 5, 8, and 10 were found to have lower available-P and very low to moderate correlations with soil pH, organic-C, C:N ratio, cation exchange capacity (CEC), as well as total-P. Based on the results, potato yield could increase by 1.765 and 0.380 tons ha-1 through the addition of organic-C and C:N ratio in Karo District. Therefore, soil amendment is required as an alternative to improve andisols soil.

Soil landscapes of the United States (SOLUS): Developing predictive soil property maps of the conterminous United States using hybrid training sets

AbstractDetailed soil property maps are increasingly important for land management decisions and environmental modeling. The US Soil Survey is investing in production of the Soil Landscapes of the United States (SOLUS), a new set of national predictive soil property maps. This paper documents initial 100‐m resolution maps of 20 soil properties that include various textural fractions, physical parameters, chemical parameters, carbon, and depth to restrictions. Many of these properties have not been previously mapped at this resolution. A hybrid training strategy helped increase training data by roughly 10‐fold over previous similar studies by combining commonly used laboratory data with underutilized field descriptions tied to soil survey map unit component property estimates (to help represent within polygon variability) as well as randomly selected soil survey map unit weighted average property estimates. Relative prediction intervals were used to help select which training data sources improved model performance. Conventional and spatial cross‐validation strategies yielded generally strong coefficients of determination between 0.5 and 0.7, but with substantial variability and outliers among the various properties, types of training data, and depths. Internal review of the maps highlighted both strengths and weaknesses of the maps, but most of the critical comments were in areas with high model uncertainty that can be used to guide future improvements. Generally, previously glaciated areas and complex large alluvial basins were harder to model. The new SOLUS 100‐m maps will be updated in the future to address identified issues and feedback as users interact with the data.

A Semi-Detailed Survey of Soils in the City of Jaboatão dos Guararapes – PE

The economic development of the city of Jaboatão dos Guararapes was generated spontaneously without considering the land use capacity or its position in the landscape. Due to the occupation and intensive disordered use of the city pedological coverage, there have been mass movements that have caused economic and social losses. These can be resized based on the understanding of the pedological aspects inherent to the city. The objective of the present paper was to map the soils of the city area at a semi-detailed level. For this purpose, different toposequences were used, with soil examinations at a rate of 1 for every 2.75 km², totaling 94 points. The mapping and taxonomic classification of soils were made in accordance with standards, procedures and criteria adopted by Embrapa Soils. Based on the morphological data, locations were defined for soil description and sampling, totaling 14 soil profiles. 15 soil classes were identified, and 23 mapping units were defined, which highlighted the notable role of the source material in the distribution of the city's pedological properties. The mapping units that require greater care for use are: a) in hill environments - PAdr1, PVAd1 and PVd; and b) in the fluvial-marine plains – EKu1, EKu2 and GXbd2. The expansion of urban areas must consider the type of soil and the behavior of the material, especially during the rainy season, in order to minimize risks and adverse losses.

Application of the Coastal and Marine Ecological Classification Standard (CMECS) to Create Benthic Geologic Habitat Maps for Portions of Acadia National Park, Maine, USA

The Coastal and Marine Ecological Classification Standard (CMECS) was applied to four portions of Acadia National Park, USA, focusing on intertidal rocky and tidal flat habitats. Side-scan sonar coupled with multi-phase echo sounder bathymetry are the primary data sources used to map the seafloor, coupled with underwater video imagery and surface grab samples for grain size and macrofaunal analysis. The CMECS Substrate, Geoform, and Biotic components were effective in describing the study areas. However, integrating the CMECS components to define Biotopes was more challenging due to the limited number of grab samples available and because the dominant species within a given map unit is largely inconsistent. While Biotopes ultimately could not be defined in this study, working within the CMECS framework to create statistically significant biotopes revealed the complexity of these study areas that may otherwise have been overlooked. This study demonstrates the effectiveness of the CMECS classification, including the framework’s ability to be flexible in communicating information.

Towards a more realistic estimation of urban land take by combining cadastral parcels and building footprints

Despite its importance as a research issue, a widely accepted methodology of estimating land take resulting from urbanization has not yet been reached. Accurate geospatial datasets are currently available at a European and global level; however, different methods of quantifying urban land take could lead to diverse outputs, potentially resulting in underestimation. This can be alarming as encroaching urban sprawl is emerging as a major environmental challenge by destroying natural habitats, consuming productive agricultural land, and contributing to climate change by increasing energy demands. To address this knowledge gap, we propose an estimation of urban land take that combines cadastral parcels and building footprints. Land parcels can be considered as a suitable minimum mapping unit as they are directly related to the spatial level where economic decisions on land use conversion are made. The proposed geospatial method is compared to methods that depend on datasets of High Resolution Layer Imperviousness, Global Building Footprints (alone), Corine Land Cover, Urban Atlas, and Global Human Settlement Layer. The method is exemplified in case studies in Greece, specifically: (a) two islands of the South Aegean Region (Mykonos and Thera), that are heavily impacted by tourism development and sprawl and (b) a peri-urban zone (Thermaikos-Michaniona) within the metropolitan area of Thessaloniki, impacted by intense suburbanization. Results show that urban land consumes important shares of available land since the mid-20th century, this fact highlighting the dynamic encroachment of urban development. Calculation shows that other methods could underestimate urbanized areas by up to 80%–90%. In the discussion section, the advantages of shifting the focus from the pixel to the parcel are further justified, while explicit links to spatial planning policies for sprawl containment are drawn. Such policies could be informed by more realistic estimations of urban land take, in order to proceed with strategic and regulatory measures to support the ‘no net land take’ policy of Europe.

Soil quality assessment and mapping in basaltic terrain of Central India for sustainable soil and crop management using integrated PCA and GIS

The cereal-based cropping system plays a vital role in ensuring food security in the Indian subcontinent. However, the productivity of these systems has seen a continuous decline due to the degradation of soil quality. This study aims to develop a Soil Quality Index (SQI) for such cropping systems. A detailed survey was conducted in the Bareli watershed of Seoni district, Madhya Pradesh, at a 1:10000 scale using high-resolution satellite data and Geographic Information System (GIS) technology. The survey identified and mapped 5 soil series: Diwartola, Diwara, Bareli-1, Bareli-2 and Bareli-3. Soil quality was evaluated based on morphological, physical and chemical properties as well as fertility parameters. Key indicators for soil quality assessment included sand, silt, clay content, bulk density, hydraulic conductivity, available water capacity and coefficient of linear extensibility (COLE). Additionally, pH, electrical conductivity, organic carbon, cation exchange capacity and nutrients like N, P, K, Fe, Mn, Cu and Zn were considered. The SQI was calculated using integrated principal component analysis, which involved selecting a minimum data set (MDS), assigning weights and scoring indicators. The results revealed that Diwartola soils had high quality (242.7 ha, 13.5 % TGA), Bareli-1 and Bareli-3 soils were of medium quality (462.8 ha, 25.7 % TGA), while Diwara and Bareli-2 soils were of low quality (966.1 ha, 53.8 % TGA). Agro-interventions such as agri-horticulture, agro-forestry, silvi-pasture, intensive cultivation and soil and water conservation measures were recommended based on the different mapping units.

Geochemical distribution of major and trace elements in hydromorphic soil profiles developed on recent alluvium

Geochemical composition and distribution of major elements study is vital in terms of investigating the major soil forming compounds and to know the fate of trace elements in soils. Particle size distribution (PSD), pH, organic carbon (OC), and major and trace elements of hydromorphic soils formed on recent alluvium in Obafemi-Owode Local Government Area of Ogun state, Nigeria were analysed using standard procedures. Three profiles pits were dug based on the mapping units identified, and samples were collected for laboratory analysis. Data were subjected descriptive statistics and Pearson’ correlation coefficients using IBM SPSS version 27. The PSD was dominated by sand content, followed by clay and silt, and soil pH in KCl was strongly acid (3.17–4.30) and pH in H2O was moderately acid (4.74–5.80), while OC contents were low to moderate (1.22–6.69 g/kg). The major elemental results showed higher concentration of SiO2 (>50 %) connotes presence quartz, feldspar, and clay minerals. Manganese, Cu, Ba, Zr, and Zn have higher concentrations among the trace elements. Major elements ratios (SiO2/Al2O3, K2O/Al2O3) showed moderate weathering stage, while trace elements ratios (Ni/Co and Cu/Zn) revealed deposition under humid conditions and anoxic environment. The statistical analysis revealed that most elements have skewness closed to zero except MgO and K2O. Correlation coefficients between the elements are positive and significant (p < 0.05 and p < 0.01) indicating that their presence can be accounted for by similar lithology, though some have negative relationship which could be as a result of anthropogenic activities. The study found that elements were potential tracers for the presence of human activities and environmental changes.

Applications and challenges of digital soil mapping in Africa

The mapping of soils in Africa is at least a century old. We currently have access to various maps depicting mapping units locally and for the continent. In the past two decades, there has been a growing interest in alternatives for generating soil maps through digital soil mapping (DSM) techniques. There are, however, numerous challenges pertaining to the implementation of DSM in Africa, such as the unavailability of appropriate covariates, age and positional error in the measurements, low sampling density, and spatial clustering of the soil data used to fit and validate the models. This review aims to investigate the current state of DSM in Africa, identify challenges specific to implementing DSM in Africa and the ways it has been solved in the literature. We found that nearly half of African countries had an existing digital soil map covering either a local or national area, and that most studies were performed at a local extent. Soil carbon was the most common property under study, whereas soil hydraulic variables were seldom reported. Nearly all studies performed mapping for the topsoil up to 30 cm and calculated validation statistics using existing datasets but without collecting a post-mapping probability sample. Few studies (i.e., 11%) reported an estimate of map uncertainty. Half of the studies had in mind a downstream application (e.g., soil fertility assessment) in the map generation. We further correlated the area of study and sampling density and found a strong negative relationship. About 30% of the studies relied on legacy soil datasets and had a lack of sufficient spatial coverage of their area of study. From this review, we highlight some research opportunities and suggest improvements in the current methodologies. Future research should focus on capacity building in DSM, new data collection, and legacy data rescue. New initiatives, that should be initiated and led from within the continent, could support the long-term monitoring of soils and updating of soil information systems while ensuring their contextualised usability. This pairs with better delivery of existing DSM studies to stakeholders and the generation of a value-added proposition to governmental institutions.

Multi-Robot Navigation System Design Based on Proximal Policy Optimization Algorithm

The more path conflicts between multiple robots, the more time it takes to avoid each other, and the more navigation time it takes for the robots to complete all tasks. This study designs a multi-robot navigation system based on deep reinforcement learning to provide an innovative and effective method for global path planning of multi-robot navigation. It can plan paths with fewer path conflicts for all robots so that the overall navigation time for the robots to complete all tasks can be reduced. Compared with existing methods of global path planning for multi-robot navigation, this study proposes new perspectives and methods. It emphasizes reducing the number of path conflicts first to reduce the overall navigation time. The system consists of a localization unit, an environment map unit, a path planning unit, and an environment monitoring unit, which provides functions for calculating robot coordinates, generating preselected paths, selecting optimal path combinations, robot navigation, and environment monitoring. We use topological maps to simplify the map representation for multi-robot path planning so that the proposed method can perform path planning for more robots in more complex environments. The proximal policy optimization (PPO) is used as the algorithm for deep reinforcement learning. This study combines the path selection method of deep reinforcement learning with the A* algorithm, which effectively reduces the number of path conflicts in multi-robot path planning and improves the overall navigation time. In addition, we used the reciprocal velocity obstacles algorithm for local path planning in the robot, combined with the proposed global path planning method, to achieve complete and effective multi-robot navigation. Some simulation results in NVIDIA Isaac Sim show that for 1000 multi-robot navigation tasks, the maximum number of path conflicts that can be reduced is 60,375 under nine simulation conditions.

Soil cover on complex glacial terrain and challenges for large-scale soil mapping using the World Reference Base (WRB) classification system

In conditions of complex glacial topography, large-scale soil mapping (1:10 000) according to the World reference base for soil resources (WRB) 2022 classification faces significant challenges. High diversity of reference soil groups (RSG) and qualifiers can be found in a small area, and this diversity is not related to the influence of a single but rather to a set of soil-forming factors. As a part of the study, the classification of soils on postglacial landscape was conducted using the WRB 2022 classification system. The analysis was focused on examining relationships between RSG and qualifiers, and also with type of geological deposits, location on the terrain, human activities (drainage of agricultural land, soil tillage and erosion). Survey showed that, in a relatively small area, soil cover consisted of WRB RSG such as Luvisols, Retisols, Regosols, Stagnosols, Gleysols Phaeozems, Planosols, Podzols, Histosols and Anthrosols, with the respective sets of qualifiers. Soils corresponding to the Anthrosol RSG were formed during the construction of a drainage system by burying the former soil with deeper material and later by translocation of colluvium by soil erosion. In the complex glacial topography, the distribution of RSG and their qualifiers is specific, which poses a number of challenges for soil mapping. The study confirmed the possibility of WRB classification for large-scale mapping of agricultural soils on postglacial landscape at the RSG level. The use of qualifiers for definition of mapping units in similar conditions of complex terrain and geological deposits (when mapping on a scale of 1:10 000) is possible only by increasing sampling density.

Attributes and Fertility Ranking of Lade Soils in the Southern Guinea Savanna of Nigeria

A major constraint restraining agricultural production in Nigeria is the scarcity of gen on soil property. The concomitant improper land use results in land degradation and dwindled agrarian efficiency. Consequently, this study seeks to assess the physicochemical attributes and classify and rank Lade soils according to fertility potential. The free survey was conducted to collect representative soil data. Six (6) profile pits designated L1 to L6 were dug to characterize the mapping units using soil samples obtained from horizons in respective profiles for the study of physical and chemical characteristics employing standard laboratory techniques. Profile description and soil classification at the Great Group level followed USDA guidelines. Fertility Capability Classification system was used to evaluate the soils for arable cropping. Soil was indicative of a relatively high structural index (&gt;9%), strong to slightly acidic (4.0 – 6.3). Organic carbon (8.02 – 9.24 g kg -1 ), available P (9.66 – 25.76 mg kg -1 ) and total N (0.09 – 0.34 g kg -1 ) were mostly moderate. Extractable bases ranged from high to very high while soil color diverse from dark yellowish brown (10YR 4/4) on the surface to reddish brown (2.5YR 4/3) in the subsoils. Soils L1 - L6 were classified as Endoaqualfs, Kandiustalfs, Haplustalfs, Haplustalfs, Plinthustalfs and Kandiustalfs, respectively. Potential suitability for arable crop place pedons L1 and L3 in class S2 (moderately suitable), whereas other pedons were rated as S3 (marginally suitable). The study gives an insight into the judicious use of inorganic fertilizer combined with good residue management and drainage for fertility improvement of the soil. Keywords : Characterization, Fertility capability classification, nutrient status, sandstone, soil taxonomy

An equivalent circuit model based design method for wideband absorber using resistive FSS

Abstract In this paper, a design method for wideband absorbers based on equivalent circuit model&amp;#xD;(ECM) is proposed. Based on the design requirements, a general ECM is established. Then, the optimized circuit parameters can be obtained by using the particle swarm optimization&amp;#xD;(PSO) algorithm. Meanwhile, the ECM combined with PSO algorithm can also realize the electromagnetic performance evaluations. Finally, the according mapping unit topologies are designed. Among them, a superior wideband absorber is selected, which consists of two layers of FSS loaded with resistive film, a single dielectric plate, an air layer and a metal layer.&amp;#xD;The fractional bandwidth of the absorber is 149.3% (3.09-20.36 GHz), the total thickness is&amp;#xD;9.35 mm. The experiment results consistent well with the simulation results, verifying the universality and effectiveness of the proposed design method.&amp;#xD;

A novel approach to identify the microclimatic edge effect width of urban green spaces at the landscape level: A case study of Changsha, China

Microclimate (e.g., temperature and humidity) in urban green space (UGS) patches changes nonlinearly from the edge to the interior, displaying a microclimatic edge effect (MEE). Reducing the MEE width is an effective and practical strategy for UGS planning and management to enhance climatic benefits (e.g., cooling). However, cost-effective approaches are not available to quantify the MEE width, let alone explore its driving factors. This study proposes a novel and cost-effective method to quantify the MEE width, based on the relationship between climatic variables (e.g., temperature and humidity) and the percentage of remaining UGS after successive edge removals. The method was tested in the subtropical city of Changsha, China, considering air temperature (AT) and humidity. Its effectiveness was also tested using widely available land surface temperature (LST) datasets while considering different spatial resolutions of the UGS map (i.e., 1–10 m) and different analytical units (i.e., 1 km2 grid and township census tract). Finally, the generalizability of this method was validated with data on six other representative Chinese cities. The results show that: (1) The estimated MEE widths for temperature and humidity in Changsha are about 8 m and 6 m, respectively. (2) The approach obtains consistent MEE widths irrespective of temperature types and analytical units. (3) A high spatial resolution UGS map (i.e., 1 m) is recommended for higher accuracy. (4) This approach effectively identifies MEE width in six other representative Chinese cities (Beijing, Chengdu, Shanghai, Shenyang, Wuhan, Xi’an), demonstrating its generalizability. This novel approach provides an easy and fast method to identify the MEE width at the landscape scale, which can help (1) better understand the relationship between UGS fragmentation and the urban microclimate, and (2) better plan and manage UGS to improve the urban microclimate.

Characterisation of the pedagogical knowledge of evolution content using the mapping approach

ABSTRACT The attributes of Pedagogical Content Knowledge (PCK) related to biological evolution in Biology teachers with varying years of professional experience were investigated. Unlike previous research that used questionnaires and interviews, we adopted a mapping approach, a more contemporary exploratory methodology. We conceptualised PCK as a narrative knowledge, which inquiring through a hypothetical classroom situation, reveals a teleological epistemological obstacle about evolution. We explored the perceptions, predictions, and justifications of 32 teachers, using their narratives as analysis units for mapping in accordance with their teaching experience. The results highlight that the most linked components of PCK are knowledge of the curriculum, knowledge of students, and teaching strategies. It was observed that novice teachers exhibit PCK with a greater number of linked components. Conversely, expert teachers recognise assessment knowledge in the structure of PCK, while novices emphasise the purposes and nature of the sciences. These findings suggest that the exploration of the relationships between components should not be viewed uniformly, but in terms of the type of the generating knowledge.

Global biomass maps can increase the precision of (sub)national aboveground biomass estimates: A comparison across tropical countries

Countries within the tropics face ongoing challenges in completing or updating their national forest inventories (NFIs), critical for estimating aboveground biomass (AGB) and for forest-related greenhouse gas (GHG) accounting. While previous studies have explored the integration of map information with local reference data to fill in data gaps, limited attention has been given to the specific challenges presented by the clustered plot designs frequently employed by NFIs when combined with remote sensing-based biomass map units. This research addresses these complexities by conducting four country case-studies, encompassing a variety of NFI characteristics within a range of AGB densities. Examining four country case-studies (Peru, Guyana, Tanzania, Mozambique), we assess the potential of European Space Agency's Climate Change Initiative (CCI) global biomass maps to increase precision in (sub)national AGB estimates. We compare a baseline approach using NFI field-based data with a model-assisted scenario incorporating a locally calibrated CCI biomass map as auxiliary information.The original CCI biomass maps systematically underestimate AGB in three of the four countries at both the country and stratum level, with particularly weak agreement at finer map resolution. However, after calibration with country-specific NFI data, stratum and country-level AGB estimates from the model-assisted scenario align well with those obtained solely from field-based data and official country reports. Introducing maps as a source of auxiliary information fairly increased the precision of stratum and country-wise AGB estimates, offering greater confidence in estimating AGB for GHG reporting purposes.Considering the challenges tropical countries face with implementing their NFIs, it is sensible to explore the potential benefits of biomass maps for climate change reporting mechanisms across biomes. While country-specific NFI design assumptions guided our model-assisted inference strategies, this study also uncovers transferable insights from the application of global biomass maps with NFI data, providing valuable lessons for climate research and policy communities.

Detailed chemical composition analysis of the Soi crater region on Titan

The Soi crater region (0° to 60°N, 180°W to −110°W), which includes the well-preserved Soi crater in its center, spans a region from Titan's aeolian-dominated equatorial regions to fluvially-dominated high northern latitudes. This provides a rich diversity of landscapes, one that is also representative of the diversity encountered across Titan. Schoenfeld et al. (2023) mapped this region at 1:800,000 scale and produced a geomorphological map showing that the area consists of 22 types of geomorphological units. The Visual and Infrared Mapping Spectrometer (VIMS) coverage of the region enabled the detailed analysis of spectra of 261 different locations using a radiative transfer technique and a mixing model, yielding compositional constraints on Titan's optical surface layer. Additional constraints on composition on the near-surface substrate were obtained from microwave emissivity. We have derived combinations of top surface materials between dark materials, tholins, water-ice, and methane suggesting that dark mobile organic material at equatorial and high latitudes indicates “young” terrains and compositions, while tholin/water-ice mixtures that dominate areas around latitude 35°N show a material that is older plains deposits that we interpret to be the end stage of aeolian and fluvial transport and deposition. We found no spectral evidence of CO2, HC3N, and NH3 ice. We use the stratigraphic relations between the various mapping units and the relation between the geomorphology and the composition of the surface layers to build hypotheses on the origin and evolution of the regional geology. We suggest that sedimentary deposits, likely aeolian, are dominant in the region with fluvial activity and leaching changing the nature of the top surfaces of the midlatitude areas of the Soi crater region.