cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29066868.v1
title: Landslide mapping with deep learning: the role of pre-/post-event SAR features and multi-sensor data fusion

description: Landslide mapping is critically important for providing detailed spatial information on hazard extent in a timely manner that ultimately contributes to the protection of human lives and critical infrastructure. In the context of increasing demands for scalable and automated solutions, Earth Observation (EO) data coupled with deep learning offer great potential to enhance the speed and accuracy of emergency mapping. This study explores the utility of a deep learning model with the U-Net architecture for automated landslide mapping using data from optical Sentinel-2 and Synthetic Aperture Radar (SAR) Sentinel-1 satellites. We investigate the effectiveness of various optical (visible, near-infrared, and short-wave infrared) and SAR-derived features (backscatter coefficients, polarimetric features, interferometric coherence), used both independently and in combination. Additionally, we assess the impact of increasing the number of pre-/post-event SAR observations on classification performance. The U-Net models are trained and tested using globally distributed and limited reference data (563 unique patches). Optical features consisted of one pre-/post-event feature, whereas SAR features had three for each reference sample. Our analysis shows that the highest classification accuracies are consistently achieved using optical features (F1-score of 0.83 with visible, near-, and short-wave infrared bands). No substantial improvements were recorded when SAR features were combined with optical features. The usage of the most common optical features (visible and near-infrared) shows the lowest accuracies compared to their combination of short-wave infrared or red-edge bands. Increasing the number of pre-/post-event SAR features improves the SAR-based accuracies. To promote further advancements in automated landslide mapping using deep learning, the landslide reference dataset generated in this study is freely available at (https://doi.org/10.5281/zenodo.15284357).
DOI: 10.6084/m9.figshare.29205837
title: Mapping global urban inequality under climate change and its interaction with sustainable development

description: Global cities are significantly affected by climate change, leading to increased inequality and further vulnerability among marginalized groups. To understand the impacts and challenges of climate change on urban inequality and enable long-term dynamic monitoring, we established an open-source, replicable, and visually-based bibliometric analysis workflow on the KNIME platform. Meanwhile, the systematic literature review was achieved by adding a meta-analysis. On this basis, we evaluated urban inequality in global countries from 2000 to 2021, exploring whether academic attention (represented by the number of published articles) is balanced with actual climate vulnerability and urban inequality. Finally, through Spearman correlation and network analysis methods, we also revealed how urban inequalities affect and constrain the achievement of the Sustainable Development Goals (SDGs). The results indicate that the United States, China, and India were early to pay attention to urban inequality under climate change and contributed a large number of published articles. The research themes can be categorized into 10 parts, including climate, cultural, and economic inequality. From 2000 to 2021, the overall trend of urban inequality in global countries decreased, with average and median scores gradually decreasing from 0.44 and 0.46 to 0.43. Additionally, there is an imbalance between actual climate vulnerability, urban inequality, and academic attention. Most countries in Africa, South America, South Asia, and the Middle East urgently need academic assistance and technical support. Except for SDG12 (Responsible consumption and production) and SDG13 (Climate action), most types of urban inequalities will hinder the performance of SDGs, with a negative correlation. Cultural, human, and health inequalities, as well as SDG12, SDG13, SDG11 (Sustainable cities and communities), and SDG16 (Peace, justice, and strong institutions) have become key nodes in the correlation network for synergistically advancing urban equity and sustainable development. Simultaneously, differentiated strategies should be adopted: countries with highly interconnected networks such as high-income countries should pursue integrated policy coordination, while those with strongly modular networks such as high climate vulnerable countries should adopt localized solutions and strengthen cross-module collaboration. Our study can provide a scientific basis for formulating targeted policies and intervention measures to address urban inequality under the long-term disruptions of climate change, thereby promoting the sustainable development of cities, as well as social equity and justice.
DOI: 10.6084/m9.figshare.29205837.v1
title: Mapping global urban inequality under climate change and its interaction with sustainable development

description: Global cities are significantly affected by climate change, leading to increased inequality and further vulnerability among marginalized groups. To understand the impacts and challenges of climate change on urban inequality and enable long-term dynamic monitoring, we established an open-source, replicable, and visually-based bibliometric analysis workflow on the KNIME platform. Meanwhile, the systematic literature review was achieved by adding a meta-analysis. On this basis, we evaluated urban inequality in global countries from 2000 to 2021, exploring whether academic attention (represented by the number of published articles) is balanced with actual climate vulnerability and urban inequality. Finally, through Spearman correlation and network analysis methods, we also revealed how urban inequalities affect and constrain the achievement of the Sustainable Development Goals (SDGs). The results indicate that the United States, China, and India were early to pay attention to urban inequality under climate change and contributed a large number of published articles. The research themes can be categorized into 10 parts, including climate, cultural, and economic inequality. From 2000 to 2021, the overall trend of urban inequality in global countries decreased, with average and median scores gradually decreasing from 0.44 and 0.46 to 0.43. Additionally, there is an imbalance between actual climate vulnerability, urban inequality, and academic attention. Most countries in Africa, South America, South Asia, and the Middle East urgently need academic assistance and technical support. Except for SDG12 (Responsible consumption and production) and SDG13 (Climate action), most types of urban inequalities will hinder the performance of SDGs, with a negative correlation. Cultural, human, and health inequalities, as well as SDG12, SDG13, SDG11 (Sustainable cities and communities), and SDG16 (Peace, justice, and strong institutions) have become key nodes in the correlation network for synergistically advancing urban equity and sustainable development. Simultaneously, differentiated strategies should be adopted: countries with highly interconnected networks such as high-income countries should pursue integrated policy coordination, while those with strongly modular networks such as high climate vulnerable countries should adopt localized solutions and strengthen cross-module collaboration. Our study can provide a scientific basis for formulating targeted policies and intervention measures to address urban inequality under the long-term disruptions of climate change, thereby promoting the sustainable development of cities, as well as social equity and justice.
DOI: 10.6084/m9.figshare.29041399
title: Improving chlorophyll-a estimation using Sentinel-2 data: a comparative analysis of augmented datasets

description: Although remote sensing using machine learning techniques can effectively monitor harmful algal blooms, their application is often limited by data availability. The synergetic impacts of rapid urbanization and climate change contribute to the unprecedented occurrence of severe algal blooms, which require sufficient high-concentration data for successful model training. In this study, we evaluated the feasibility of integrating datasets from two different watersheds to estimate chlorophyll-a (Chl-a) concentrations using machine learning models with Sentinel-2 imagery. The original dataset, consisting of data from the Nakdong (ND) River, and two augmented datasets – an integrated dataset combining the Geum (GE) and ND rivers (GEND) and a resampled ND dataset using the synthetic minority oversampling technique for regression with Gaussian noise (ND-SMOGN) – were used to train six machine learning models. Models trained on the augmented datasets, GEND and ND-SMOGN, successfully addressed this underestimation issue for the sample with the highest Chl-a concentration. Among the six algorithms, multilayer perceptron with attention mechanism exploited the highest performance across all indicators with coefficient of determination (<i>R</i><sup><i>2</i></sup>) and root mean square error (RMSE) values of 0.93 and 2.76. Model interpretations revealed that models trained on GEND assigned high significance to B03 (560 nm) to B05 (705 nm), aligning with the optical characteristics of Chl-a, whereas models trained on ND and ND-SMOGN also emphasized less relevant bands. This study provides valuable insights into improving model performance, understanding the impacts of data availability, and informing the development of more accurate and reliable environmental management practices.
DOI: 10.6084/m9.figshare.29041399.v1
title: Improving chlorophyll-a estimation using Sentinel-2 data: a comparative analysis of augmented datasets

description: Although remote sensing using machine learning techniques can effectively monitor harmful algal blooms, their application is often limited by data availability. The synergetic impacts of rapid urbanization and climate change contribute to the unprecedented occurrence of severe algal blooms, which require sufficient high-concentration data for successful model training. In this study, we evaluated the feasibility of integrating datasets from two different watersheds to estimate chlorophyll-a (Chl-a) concentrations using machine learning models with Sentinel-2 imagery. The original dataset, consisting of data from the Nakdong (ND) River, and two augmented datasets – an integrated dataset combining the Geum (GE) and ND rivers (GEND) and a resampled ND dataset using the synthetic minority oversampling technique for regression with Gaussian noise (ND-SMOGN) – were used to train six machine learning models. Models trained on the augmented datasets, GEND and ND-SMOGN, successfully addressed this underestimation issue for the sample with the highest Chl-a concentration. Among the six algorithms, multilayer perceptron with attention mechanism exploited the highest performance across all indicators with coefficient of determination (<i>R</i><sup><i>2</i></sup>) and root mean square error (RMSE) values of 0.93 and 2.76. Model interpretations revealed that models trained on GEND assigned high significance to B03 (560 nm) to B05 (705 nm), aligning with the optical characteristics of Chl-a, whereas models trained on ND and ND-SMOGN also emphasized less relevant bands. This study provides valuable insights into improving model performance, understanding the impacts of data availability, and informing the development of more accurate and reliable environmental management practices.
DOI: 10.6084/m9.figshare.28777157
title: A stand-alone framework for predicting spatiotemporal errors in satellite-based soil moisture using tree-based models and deep neural networks

description: Soil moisture (SM) has been recognized as one of the essential climate variables in Earth science. Incorporating spatiotemporal information from satellite SM observations into land surface models via land data assimilation (LDA) has emerged as a promising method to improve continuous SM modeling and climate extreme monitoring. Since the reliable satellite SM error dynamics are crucial for successful LDA applications but often assumed to remain static over time, this study presents a novel framework that combines triple collocation analysis (TCA) with advanced machine learning techniques, such as Light Gradient Boosting Machine (LGBM) and Deep Neural Networks (DNN), to accurately quantify spatially and temporally continuous satellite-based SM characteristics on a global scale. In this study, the stand-alone TCA-based time-variant SM error prediction models, which rely exclusively on data used for the Soil Moisture Active Passive (SMAP) retrieval, were developed and comprehensively evaluated. These models successfully recover error information over areas where SMAP SM error data were previously unusable due to uncertain error characteristics. In addition, the stand-alone SMAP-based model demonstrates superior performance compared to model that relies on external datasets, such as the Global Land Data Assimilation System (GLDAS). These findings provide valuable insights into the dynamic nature of satellite-based SM error under various environmental conditions and present a novel way to improve LDA. Furthermore, the proposed methodology can be extended to predict error dynamics for other satellite-based geophysical datasets, broadening its potential applications beyond SMAP.
DOI: 10.6084/m9.figshare.28777157.v1
title: A stand-alone framework for predicting spatiotemporal errors in satellite-based soil moisture using tree-based models and deep neural networks

description: Soil moisture (SM) has been recognized as one of the essential climate variables in Earth science. Incorporating spatiotemporal information from satellite SM observations into land surface models via land data assimilation (LDA) has emerged as a promising method to improve continuous SM modeling and climate extreme monitoring. Since the reliable satellite SM error dynamics are crucial for successful LDA applications but often assumed to remain static over time, this study presents a novel framework that combines triple collocation analysis (TCA) with advanced machine learning techniques, such as Light Gradient Boosting Machine (LGBM) and Deep Neural Networks (DNN), to accurately quantify spatially and temporally continuous satellite-based SM characteristics on a global scale. In this study, the stand-alone TCA-based time-variant SM error prediction models, which rely exclusively on data used for the Soil Moisture Active Passive (SMAP) retrieval, were developed and comprehensively evaluated. These models successfully recover error information over areas where SMAP SM error data were previously unusable due to uncertain error characteristics. In addition, the stand-alone SMAP-based model demonstrates superior performance compared to model that relies on external datasets, such as the Global Land Data Assimilation System (GLDAS). These findings provide valuable insights into the dynamic nature of satellite-based SM error under various environmental conditions and present a novel way to improve LDA. Furthermore, the proposed methodology can be extended to predict error dynamics for other satellite-based geophysical datasets, broadening its potential applications beyond SMAP.
DOI: 10.6084/m9.figshare.28652318
title: Detection of driving factors and critical thresholds for carbon sequestration capacity in urban agglomerations using a combined causal inference and machine learning approach

description: The carbon sequestration capacity in urban agglomeration ecosystems is crucial for enhancing scientific understanding of the carbon cycle and promoting sustainable development to mitigate climate change. However, existing studies on driving factors, particularly regarding determining causal mechanisms and critical thresholds for carbon sequestration in urban agglomeration ecosystems remain unclear. To address this knowledge gap, we propose a CMSC framework which integrates causal inference and machine learning methods to reveal the underlying mechanisms and determine the thresholds of drivers affecting carbon sequestration in the Yangtze River Delta urban agglomeration (YRDUA). The underlying driving mechanisms of carbon sequestration were heterogeneous between municipal county and non-municipal county in YRDUA. The thresholds of nighttime light, surface solar radiation downwards, air temperature, total precipitation and population density that impacted carbon sequestration in non-municipal (municipal) counties of YRDUA were 0.04 (0.4) nW·cm<sup>−2</sup>·sr<sup>−1</sup>·yr<sup>−1</sup>, −6.1 × 10<sup>4</sup> (−5.46 × 10<sup>4</sup>) J·m<sup>−2</sup>·yr<sup>−1</sup>, 0.013 (0.017) K·yr<sup>−1</sup>, 3.64 × 10<sup>−5</sup> (2.51 × 10<sup>−5</sup>) m·yr<sup>−1</sup> and −0.04 people·km<sup>−2</sup>·yr<sup>−1</sup>, respectively. Furthermore, the long-term (from 2021 to 2100) carbon sequestration dataset with county-level scale in the YRDUA was generated using the causal inference-based machine learning model. In the context of carbon neutrality, we found that the optimal emission scenario for low-carbon sustainable development of YRDUA is SSP3, under which the average carbon sequestration in most counties will exceed 1 × 10<sup>7</sup> t. Our study provides a constructive basis for a science-based carbon cycle and ecological management in the urban agglomeration of China.
DOI: 10.6084/m9.figshare.28652318.v1
title: Detection of driving factors and critical thresholds for carbon sequestration capacity in urban agglomerations using a combined causal inference and machine learning approach

description: The carbon sequestration capacity in urban agglomeration ecosystems is crucial for enhancing scientific understanding of the carbon cycle and promoting sustainable development to mitigate climate change. However, existing studies on driving factors, particularly regarding determining causal mechanisms and critical thresholds for carbon sequestration in urban agglomeration ecosystems remain unclear. To address this knowledge gap, we propose a CMSC framework which integrates causal inference and machine learning methods to reveal the underlying mechanisms and determine the thresholds of drivers affecting carbon sequestration in the Yangtze River Delta urban agglomeration (YRDUA). The underlying driving mechanisms of carbon sequestration were heterogeneous between municipal county and non-municipal county in YRDUA. The thresholds of nighttime light, surface solar radiation downwards, air temperature, total precipitation and population density that impacted carbon sequestration in non-municipal (municipal) counties of YRDUA were 0.04 (0.4) nW·cm<sup>−2</sup>·sr<sup>−1</sup>·yr<sup>−1</sup>, −6.1 × 10<sup>4</sup> (−5.46 × 10<sup>4</sup>) J·m<sup>−2</sup>·yr<sup>−1</sup>, 0.013 (0.017) K·yr<sup>−1</sup>, 3.64 × 10<sup>−5</sup> (2.51 × 10<sup>−5</sup>) m·yr<sup>−1</sup> and −0.04 people·km<sup>−2</sup>·yr<sup>−1</sup>, respectively. Furthermore, the long-term (from 2021 to 2100) carbon sequestration dataset with county-level scale in the YRDUA was generated using the causal inference-based machine learning model. In the context of carbon neutrality, we found that the optimal emission scenario for low-carbon sustainable development of YRDUA is SSP3, under which the average carbon sequestration in most counties will exceed 1 × 10<sup>7</sup> t. Our study provides a constructive basis for a science-based carbon cycle and ecological management in the urban agglomeration of China.
DOI: 10.6084/m9.figshare.29148229
title: Understanding discrepancies in soil moisture from SMAP and AMSR2: insights into performance and dry-down behavior

description: Understanding the dynamic changes in soil moisture (SM) is crucial for studying land–atmosphere interactions in hydrometeorology. While numerous SM datasets have been developed for passive microwave remote sensing systems operating at various frequencies, the consistency of SM dry-down patterns observed by different sensors remains uncertain. Additionally, the use of distinct algorithms across SM products complicates direct comparisons. This study addresses these issues by producing two new enhanced-resolution (approximately 10-km) SM datasets using the same multi-channel collaborative algorithm (MCCA). These datasets are retrieved from the L-band brightness temperature (Tb) from Soil Moisture Active Passive (SMAP) and the C/X/Ku-band Tb from Advanced Microwave Scanning Radiometer 2 (AMSR2), referred to as MCCA SMAP and MCCA AMSR2. The satellite-derived SM data are evaluated and compared using 40 globally distributed SM observation networks at both regional (dense network) and grid scales. The results indicate that the Pearson correlation coefficients (R) of MCCA SMAP and MCCA AMSR2 SM in 23 dense networks are 0.795 and 0.664, respectively, with unbiased root-mean-square deviation (ubRMSD) of 0.041 m<sup>3</sup>/m<sup>3</sup> and 0.048 m<sup>3</sup>/m<sup>3</sup>, respectively. Both datasets perform better at the regional scale than at the grid scale. The analysis shows that SMAP outperforms AMSR2 overall, although the sensing capabilities of both payloads decline with increasing vegetation water content (VWC). Further analysis of global SM dry-down patterns reveals minimal differences in the magnitude of SM dry-down between the two payloads, but slightly larger differences in the effective wilting point. A notable disparity was observed in SM memory, with SMAP exhibiting a significantly longer memory than AMSR2. Analysis of SM loss rates shows that SMAP has a lower loss rate compared to AMSR2, consistent with the theoretical expectation that lower-frequency observations, such as those from SMAP, observes deeper soil layers. These results highlight the importance of considering differences in payload configurations when using remote sensing SM products for studies of land–atmosphere interactions in hydrometeorology and for improving land surface models (LSMs).