cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28593438
title: Earth observations reveal impacts of climate variability on maize cropping systems in sub-Saharan Africa

description: In Kenya, climate variability and change threaten smallholder, rainfed farms, with crop failures, yield reductions, and pest infestations. Efficient agroecological strategies, such as Push-Pull intercropping, offer documented benefits including pest control, improved soil fertility, and water conservation compared to traditional maize monocropping. To date, no studies exist comparing traditional maize monocropping and Push-Pull intercropping using earth observation tools over several growing seasons in East Africa. Our research addresses this by harmonizing Landsat 7, 8, 9 with Sentinel-2 remote sensing time series from 2016 to 2023. Phenological metrics of 15 growing seasons are extracted based on a threshold method using the Normalized Difference Vegetation Index (NDVI) as a vegetation proxy. Field data from 58 sites in southwestern Kenya provided training for this analysis, revealing detectable inter-class differences. Notably, Push-Pull intercrop fields showed greater resilience during biotic stress events, such as the locust outbreak in 2020 short rainy season and the fall armyworm infestation in combination with delayed and below-average rainfall during the short 2021 and the long 2022 growing seasons. Higher maximum NDVI and extended season duration indicated a higher resilience of Push-Pull farming under unfavorable agricultural conditions. Short growing seasons with unfavorable conditions showed earlier end of seasons in both systems, whereas long growing seasons with unfavorable conditions caused delayed onset and end of seasons. This study marks the first attempt to leverage earth observation data to compare traditional maize agriculture with agricultural systems featuring applied ecological management strategies, showcasing the potential of earth observation tools to monitor and evaluate agroecological resilience.
DOI: 10.6084/m9.figshare.28593438.v1
title: Earth observations reveal impacts of climate variability on maize cropping systems in sub-Saharan Africa

description: In Kenya, climate variability and change threaten smallholder, rainfed farms, with crop failures, yield reductions, and pest infestations. Efficient agroecological strategies, such as Push-Pull intercropping, offer documented benefits including pest control, improved soil fertility, and water conservation compared to traditional maize monocropping. To date, no studies exist comparing traditional maize monocropping and Push-Pull intercropping using earth observation tools over several growing seasons in East Africa. Our research addresses this by harmonizing Landsat 7, 8, 9 with Sentinel-2 remote sensing time series from 2016 to 2023. Phenological metrics of 15 growing seasons are extracted based on a threshold method using the Normalized Difference Vegetation Index (NDVI) as a vegetation proxy. Field data from 58 sites in southwestern Kenya provided training for this analysis, revealing detectable inter-class differences. Notably, Push-Pull intercrop fields showed greater resilience during biotic stress events, such as the locust outbreak in 2020 short rainy season and the fall armyworm infestation in combination with delayed and below-average rainfall during the short 2021 and the long 2022 growing seasons. Higher maximum NDVI and extended season duration indicated a higher resilience of Push-Pull farming under unfavorable agricultural conditions. Short growing seasons with unfavorable conditions showed earlier end of seasons in both systems, whereas long growing seasons with unfavorable conditions caused delayed onset and end of seasons. This study marks the first attempt to leverage earth observation data to compare traditional maize agriculture with agricultural systems featuring applied ecological management strategies, showcasing the potential of earth observation tools to monitor and evaluate agroecological resilience.
DOI: 10.6084/m9.figshare.30621280
title: A review of remote sensing in coastal aquaculture: data, geographic hotspots, methods, and challenges

description: Coastal aquaculture, involving the cultivation of aquatic organisms in near-coastal land-based ponds and marine-based suspended cages and floating rafts, is essential for meeting global fish demand. Yet, its rapid expansion, particularly in Asia, has raised concerns about seawater eutrophication, biodiversity loss, and food safety. Remote sensing has emerged as a critical tool for monitoring coastal aquaculture by scalable data collection, long-term monitoring, and access to remote marine areas. This study presents the first comprehensive review that integrates bibliometric and content analysis of 304 papers on remote sensing in coastal aquaculture (RSCA) published between 2000 and early 2024. It identifies three major research themes: aquaculture mapping (35.6%), land and sea use change detection (27.6%), and eco-environmental assessment (14.2%), with a notable growth in publications since 2014. Medium-resolution optical satellites (e.g. Landsat series and Sentinel-2) dominate coastal aquaculture studies in all themes. Geographically, RSCA studies are highly concentrated in Southeast Asia, particularly along China's coastline and the Mediterranean, with most conducted at small scales. In coastal aquaculture mapping, deep learning methods (43.5%) show strong potential for detecting complex features like raft boundaries, while traditional machine learning remains effective for large-scale, high-contrast aquaculture. For eco-environmental studies, spectral indicators derived from remote sensing effectively support indirect monitoring of land–sea transformation (e.g. mangrove degradation and coastline expansion), seawater quality degradation (e.g. eutrophication, algal blooms, and heavy metals), and biodiversity threats (e.g. habitat loss and microplastics). Key challenges include the lack of high-resolution open datasets, difficulties in summarizing the spectral characteristics of coastal aquaculture targets, accurate global aquaculture mapping, and the absence of an integrated environmental monitoring framework. Addressing these gaps is essential for advancing sustainable management and supporting global food security and eco-environmental goals.
DOI: 10.6084/m9.figshare.30621280.v1
title: A review of remote sensing in coastal aquaculture: data, geographic hotspots, methods, and challenges

description: Coastal aquaculture, involving the cultivation of aquatic organisms in near-coastal land-based ponds and marine-based suspended cages and floating rafts, is essential for meeting global fish demand. Yet, its rapid expansion, particularly in Asia, has raised concerns about seawater eutrophication, biodiversity loss, and food safety. Remote sensing has emerged as a critical tool for monitoring coastal aquaculture by scalable data collection, long-term monitoring, and access to remote marine areas. This study presents the first comprehensive review that integrates bibliometric and content analysis of 304 papers on remote sensing in coastal aquaculture (RSCA) published between 2000 and early 2024. It identifies three major research themes: aquaculture mapping (35.6%), land and sea use change detection (27.6%), and eco-environmental assessment (14.2%), with a notable growth in publications since 2014. Medium-resolution optical satellites (e.g. Landsat series and Sentinel-2) dominate coastal aquaculture studies in all themes. Geographically, RSCA studies are highly concentrated in Southeast Asia, particularly along China's coastline and the Mediterranean, with most conducted at small scales. In coastal aquaculture mapping, deep learning methods (43.5%) show strong potential for detecting complex features like raft boundaries, while traditional machine learning remains effective for large-scale, high-contrast aquaculture. For eco-environmental studies, spectral indicators derived from remote sensing effectively support indirect monitoring of land–sea transformation (e.g. mangrove degradation and coastline expansion), seawater quality degradation (e.g. eutrophication, algal blooms, and heavy metals), and biodiversity threats (e.g. habitat loss and microplastics). Key challenges include the lack of high-resolution open datasets, difficulties in summarizing the spectral characteristics of coastal aquaculture targets, accurate global aquaculture mapping, and the absence of an integrated environmental monitoring framework. Addressing these gaps is essential for advancing sustainable management and supporting global food security and eco-environmental goals.
DOI: 10.6084/m9.figshare.29041219
title: Methane emissions from Australia estimated by inverse analysis using in-situ and Satellite (GOSAT) atmospheric observations

description: Australia has significant sources of atmospheric methane (CH₄), driven by extensive coal and natural gas production, livestock, and large-scale fires. Accurate quantification and characterization of CH₄ emissions are critical for effective climate mitigation strategies in Australia. In this study, we employed an inverse analysis of atmospheric CH₄ observations from the GOSAT satellite and surface measurements from 2016 to 2021 to assess CH₄ emissions in Australia. The inversion process integrates anthropogenic and natural emissions as prior estimates, optimizing them with the NIES-TM-FLEXPART-variational model (NTFVAR) at a resolution of up to 0.1° × 0.1°. We validated the performance of our inverse model using data obtained from the United Nations Environment Program Methane Science (UNEP), Airborne Research Australia 2018 aircraft-based atmospheric CH₄ measurement campaigns. Compared to prior emission estimates, optimized emissions dramatically enhanced the accuracy of modeled concentrations, aligning them much better with observations. Our results indicate that the estimated inland CH<sub>4</sub> emissions in Australia amount to 6.84 ± 0.51 Tg CH<sub>4</sub> yr<sup>−1</sup> and anthropogenic emissions amount to 4.20 ± 0.08 Tg CH<sub>4</sub> yr<sup>−1</sup>, both slightly lower than the values reported in existing inventories. Moreover, our results unveil noteworthy spatiotemporal characteristics, such as upward corrections during the warm season, particularly in Southeastern Australia. During the three most severe months of the 2019–2020 bushfire season, emissions from biomass burning surged by 0.68 Tg, constituting over 71% of the total emission increase. These results highlight the importance of continuous observation and analysis of sectoral emissions, particularly near major sources, to guide targeted emission reduction strategies. The spatiotemporal characteristics identified in this study underscore the need for adaptive and region-specific approaches to CH₄ emission management in Australia.
DOI: 10.6084/m9.figshare.29041219.v1
title: Methane emissions from Australia estimated by inverse analysis using in-situ and Satellite (GOSAT) atmospheric observations

description: Australia has significant sources of atmospheric methane (CH₄), driven by extensive coal and natural gas production, livestock, and large-scale fires. Accurate quantification and characterization of CH₄ emissions are critical for effective climate mitigation strategies in Australia. In this study, we employed an inverse analysis of atmospheric CH₄ observations from the GOSAT satellite and surface measurements from 2016 to 2021 to assess CH₄ emissions in Australia. The inversion process integrates anthropogenic and natural emissions as prior estimates, optimizing them with the NIES-TM-FLEXPART-variational model (NTFVAR) at a resolution of up to 0.1° × 0.1°. We validated the performance of our inverse model using data obtained from the United Nations Environment Program Methane Science (UNEP), Airborne Research Australia 2018 aircraft-based atmospheric CH₄ measurement campaigns. Compared to prior emission estimates, optimized emissions dramatically enhanced the accuracy of modeled concentrations, aligning them much better with observations. Our results indicate that the estimated inland CH<sub>4</sub> emissions in Australia amount to 6.84 ± 0.51 Tg CH<sub>4</sub> yr<sup>−1</sup> and anthropogenic emissions amount to 4.20 ± 0.08 Tg CH<sub>4</sub> yr<sup>−1</sup>, both slightly lower than the values reported in existing inventories. Moreover, our results unveil noteworthy spatiotemporal characteristics, such as upward corrections during the warm season, particularly in Southeastern Australia. During the three most severe months of the 2019–2020 bushfire season, emissions from biomass burning surged by 0.68 Tg, constituting over 71% of the total emission increase. These results highlight the importance of continuous observation and analysis of sectoral emissions, particularly near major sources, to guide targeted emission reduction strategies. The spatiotemporal characteristics identified in this study underscore the need for adaptive and region-specific approaches to CH₄ emission management in Australia.
DOI: 10.6084/m9.figshare.29875396
title: MFFNet: a wavelet transform-based multimodal frequency fusion network for remote sensing semantic segmentation

description: The use of multimodal data for semantic segmentation in remote sensing has attracted considerable interest, as it enables the integration of complementary information from various sensors. However, conventional multimodal fusion methods primarily operate in the spatial domain. Given the substantial divergence and inherent redundancy across modalities, direct fusion in the spatial domain often leads to the accumulation of irrelevant information and the loss of useful features. Furthermore, spatial-domain fusion alone is insufficient to fully exploit the complementary characteristics of multimodal data. To address these challenges, we introduce a wavelet transform-based multimodal frequency fusion network (MFFNet) to compensate for the limitations of spatial-domain fusion by introducing frequency-domain information. Specifically, we propose the spatial-frequency domain wavelet attention fusion module (SFWAF), which uses weight-shared spatial-domain branches to extract generic spatial features for different modalities. The SFWAF module uses the discrete wavelet transform (DWT) to map different modal features into the frequency domain for fusion and adaptively integrates the dual-domain features using a learnable weighting factor. Additionally, we propose a lightweight frequency-enhanced feature fusion (FEF) module for multiscale feature integration. This module fuses high-frequency components from various modalities using a fixed fusion strategy to preserve critical edge and detail information. Extensive experimental results on the ISPRS Vaihingen, ISPRS Potsdam, and WHU-OPT-SAR datasets demonstrate that MFFNet outperforms traditional multimodal fusion methods, achieving mIoU of 84.21% and 85.88% on the Vaihingen and Potsdam datasets, respectively, and overall accuracies of 92.26% and 91.16%.
DOI: 10.6084/m9.figshare.29875396.v1
title: MFFNet: a wavelet transform-based multimodal frequency fusion network for remote sensing semantic segmentation

description: The use of multimodal data for semantic segmentation in remote sensing has attracted considerable interest, as it enables the integration of complementary information from various sensors. However, conventional multimodal fusion methods primarily operate in the spatial domain. Given the substantial divergence and inherent redundancy across modalities, direct fusion in the spatial domain often leads to the accumulation of irrelevant information and the loss of useful features. Furthermore, spatial-domain fusion alone is insufficient to fully exploit the complementary characteristics of multimodal data. To address these challenges, we introduce a wavelet transform-based multimodal frequency fusion network (MFFNet) to compensate for the limitations of spatial-domain fusion by introducing frequency-domain information. Specifically, we propose the spatial-frequency domain wavelet attention fusion module (SFWAF), which uses weight-shared spatial-domain branches to extract generic spatial features for different modalities. The SFWAF module uses the discrete wavelet transform (DWT) to map different modal features into the frequency domain for fusion and adaptively integrates the dual-domain features using a learnable weighting factor. Additionally, we propose a lightweight frequency-enhanced feature fusion (FEF) module for multiscale feature integration. This module fuses high-frequency components from various modalities using a fixed fusion strategy to preserve critical edge and detail information. Extensive experimental results on the ISPRS Vaihingen, ISPRS Potsdam, and WHU-OPT-SAR datasets demonstrate that MFFNet outperforms traditional multimodal fusion methods, achieving mIoU of 84.21% and 85.88% on the Vaihingen and Potsdam datasets, respectively, and overall accuracies of 92.26% and 91.16%.
DOI: 10.6084/m9.figshare.29364792
title: From rainforests to drylands: a context-specific framework for mapping land use and land cover dynamics in Northeast Brazil (2000–2020)

description: Northeast Brazil (NEB), about three times the area of Spain, hosts &gt;90% of Brazil’s drylands along with tropical rain- and dry forests. Climate variability, 45–60% cloud cover, and scarce reference data limit land use and land cover (LULC) mapping accuracy to ~80% across much of the region. Here, we introduce NEB’s first context-specific LULC framework, using phenologically timed annual MODIS mosaics (2000–2020) with &lt;0.5% pixel gaps and minimal seasonal bias. Ecoregions were individually classified using customized random forest (RF) models with 56 features – including linear spectral unmixing fractions – prior to NEB-wide integration. Binary masks excluding non-mangrove pixels during RF training significantly improved mangrove-rainforest separation. Our two-tier classification scheme uniquely distinguishes NEB shrublands nationally, aligns with global datasets (7 Level-1 classes), and preserves ecoregion detail (16 Level-2 classes). Validated against &gt;10,000 independent points, the 2018 NEB-wide map achieved 90.5% overall accuracy at Level-1. At Level-2, ecoregional accuracies were 95.9% (Amazon), 94.3% (Atlantic Forest), 89.4% (Cerrado), and 87.9% (Caatinga). Per-pixel spatial agreement with national and global datasets ranged from 29–70%. Between the 2000 and 2020 endpoints, ~540,000 km<sup>2</sup> of NEB underwent LULC changes, based on pixel counts. Forest declined 22%, grasslands 68%, and agriculture expanded 140% – roughly 10 million soccer fields – mainly in the Cerrado MaToPiBa (Maranhão, Tocantins, Piauí, Bahia) frontier. Meanwhile, encroachment around protected areas intensified, particularly in the Amazon. This open-access product (http://www.dsr.inpe.br/DSR/laboratorios/LAF) sets a benchmark for LULC mapping in global dryland-forest mosaics, positioning NEB as a model for data-driven land management.
DOI: 10.6084/m9.figshare.29364792.v1
title: From rainforests to drylands: a context-specific framework for mapping land use and land cover dynamics in Northeast Brazil (2000–2020)

description: Northeast Brazil (NEB), about three times the area of Spain, hosts &gt;90% of Brazil’s drylands along with tropical rain- and dry forests. Climate variability, 45–60% cloud cover, and scarce reference data limit land use and land cover (LULC) mapping accuracy to ~80% across much of the region. Here, we introduce NEB’s first context-specific LULC framework, using phenologically timed annual MODIS mosaics (2000–2020) with &lt;0.5% pixel gaps and minimal seasonal bias. Ecoregions were individually classified using customized random forest (RF) models with 56 features – including linear spectral unmixing fractions – prior to NEB-wide integration. Binary masks excluding non-mangrove pixels during RF training significantly improved mangrove-rainforest separation. Our two-tier classification scheme uniquely distinguishes NEB shrublands nationally, aligns with global datasets (7 Level-1 classes), and preserves ecoregion detail (16 Level-2 classes). Validated against &gt;10,000 independent points, the 2018 NEB-wide map achieved 90.5% overall accuracy at Level-1. At Level-2, ecoregional accuracies were 95.9% (Amazon), 94.3% (Atlantic Forest), 89.4% (Cerrado), and 87.9% (Caatinga). Per-pixel spatial agreement with national and global datasets ranged from 29–70%. Between the 2000 and 2020 endpoints, ~540,000 km<sup>2</sup> of NEB underwent LULC changes, based on pixel counts. Forest declined 22%, grasslands 68%, and agriculture expanded 140% – roughly 10 million soccer fields – mainly in the Cerrado MaToPiBa (Maranhão, Tocantins, Piauí, Bahia) frontier. Meanwhile, encroachment around protected areas intensified, particularly in the Amazon. This open-access product (http://www.dsr.inpe.br/DSR/laboratorios/LAF) sets a benchmark for LULC mapping in global dryland-forest mosaics, positioning NEB as a model for data-driven land management.
DOI: 10.6084/m9.figshare.29066868
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).