cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28777160
title: Agriculture intensification and moisture-induced Thar desert greening: implications for energy balance, socio-economy, and biodiversity

description: Despite being the largest biome, landscape dynamics in drylands is largely under-explored. We investigate the changing landscape in the most inhabited desert – the Thar, for last two decades (2001–2020), its drivers, quantification of natural and anthropogenic control, future evolution and potential implications. We employ a suite of advanced statistical and machine learning (ML) techniques on remote sensing and reanalyses data. We find that the Thar desert is greening in recent decades as evident in the increased surface greenness (28.4%), leaf area (21.7%), photosynthesis (33.5%) and productivity (22.9%). Enhanced moisture availability by precipitation (P, 29.7%) and soil moisture (SM, 4.5%) triggers this greening, predominantly in post-monsoon (September–November). This moisture-induced greening of Thar is driven by the westward shift of Indian Summer Monsoon (ISM) rainfall. Partial correlation reveals that SM has a stronger control on vegetation than P and the Granger Causality confirms that SM has a direct causal effect on vegetation in all seasons. The change detection analysis shows an increase in croplands (23.5%) at the expense of shrublands (−5.3%), as there are conversions of barren lands to shrublands and then shrublands to croplands aided by the enhanced water availability from irrigation (12.4% in 2000–2015) through canals and inland water bodies. Random Forest (RF) model reveals that climate change (60.7%) has a greater control on Thar greening than human influence (39.3%). Henceforth, the Thar desert is greening due to enhanced moisture availability and agriculture intensification, which is likely to get amplified in future and might substantially change the landscape of this “great desert” by the end of the 21<sup>st</sup> century. Greening of Thar can improve the socio-economic scenario and help to attain food security, but can be a great threat for the traditional agropastrolism, indigenous biodiversity and change the regional energy balance.
DOI: 10.6084/m9.figshare.28777160.v1
title: Agriculture intensification and moisture-induced Thar desert greening: implications for energy balance, socio-economy, and biodiversity

description: Despite being the largest biome, landscape dynamics in drylands is largely under-explored. We investigate the changing landscape in the most inhabited desert – the Thar, for last two decades (2001–2020), its drivers, quantification of natural and anthropogenic control, future evolution and potential implications. We employ a suite of advanced statistical and machine learning (ML) techniques on remote sensing and reanalyses data. We find that the Thar desert is greening in recent decades as evident in the increased surface greenness (28.4%), leaf area (21.7%), photosynthesis (33.5%) and productivity (22.9%). Enhanced moisture availability by precipitation (P, 29.7%) and soil moisture (SM, 4.5%) triggers this greening, predominantly in post-monsoon (September–November). This moisture-induced greening of Thar is driven by the westward shift of Indian Summer Monsoon (ISM) rainfall. Partial correlation reveals that SM has a stronger control on vegetation than P and the Granger Causality confirms that SM has a direct causal effect on vegetation in all seasons. The change detection analysis shows an increase in croplands (23.5%) at the expense of shrublands (−5.3%), as there are conversions of barren lands to shrublands and then shrublands to croplands aided by the enhanced water availability from irrigation (12.4% in 2000–2015) through canals and inland water bodies. Random Forest (RF) model reveals that climate change (60.7%) has a greater control on Thar greening than human influence (39.3%). Henceforth, the Thar desert is greening due to enhanced moisture availability and agriculture intensification, which is likely to get amplified in future and might substantially change the landscape of this “great desert” by the end of the 21<sup>st</sup> century. Greening of Thar can improve the socio-economic scenario and help to attain food security, but can be a great threat for the traditional agropastrolism, indigenous biodiversity and change the regional energy balance.
DOI: 10.6084/m9.figshare.28524459
title: Comparison of the capability and performance of “photosynthesis” and “structure” indices in retrieving vegetation phenology in the Northern Hemisphere

description: Phenology refers to the natural responses of vegetation to environmental factors and disturbances throughout its life cycle. Focusing on the mid-high latitudes vegetation in the Northern Hemisphere, this study reported the differences in phenology extracted using “photosynthesis” and “structure” indices, specifically solar-induced chlorophyll fluorescence (SIF) and leaf area index (LAI), respectively. We analyzed the dynamics of phenology inverted by different indices and validated the inversion accuracy using data from FLUXNET2015 gross primary productivity (GPP). The results revealed that: (1) Both the start of the growing seasons (SOS) and the end of the growing seasons (EOS) showed significant advancing trend. On average, the SOS derived from SIF (143 days) was later than that derived from LAI (136 days), whereas the EOS derived from SIF (236 days) occurred earlier than that from LAI (245 days); (2) Across all four climatic zones, phenology was dominated by an advancing trend, with the rate of phenological change derived from SIF generally faster than that from LAI. Distinct differences in phenological trends were observed among various vegetation types; (3) Phenological parameters extracted using SIF were closer to actual observations than those derived from LAI, with the accuracy of SOS inversion generally higher than that of EOS for both indices. The study demonstrates that “photosynthesis” indices can produce more accurate photosynthetic phenology than “structure” indices, which provided a reference for the subsequent vegetation phenology studies.
DOI: 10.6084/m9.figshare.28524459.v1
title: Comparison of the capability and performance of “photosynthesis” and “structure” indices in retrieving vegetation phenology in the Northern Hemisphere

description: Phenology refers to the natural responses of vegetation to environmental factors and disturbances throughout its life cycle. Focusing on the mid-high latitudes vegetation in the Northern Hemisphere, this study reported the differences in phenology extracted using “photosynthesis” and “structure” indices, specifically solar-induced chlorophyll fluorescence (SIF) and leaf area index (LAI), respectively. We analyzed the dynamics of phenology inverted by different indices and validated the inversion accuracy using data from FLUXNET2015 gross primary productivity (GPP). The results revealed that: (1) Both the start of the growing seasons (SOS) and the end of the growing seasons (EOS) showed significant advancing trend. On average, the SOS derived from SIF (143 days) was later than that derived from LAI (136 days), whereas the EOS derived from SIF (236 days) occurred earlier than that from LAI (245 days); (2) Across all four climatic zones, phenology was dominated by an advancing trend, with the rate of phenological change derived from SIF generally faster than that from LAI. Distinct differences in phenological trends were observed among various vegetation types; (3) Phenological parameters extracted using SIF were closer to actual observations than those derived from LAI, with the accuracy of SOS inversion generally higher than that of EOS for both indices. The study demonstrates that “photosynthesis” indices can produce more accurate photosynthetic phenology than “structure” indices, which provided a reference for the subsequent vegetation phenology studies.
DOI: 10.6084/m9.figshare.30833155
title: Global hourly seamless AOD through measurement-adjusted machine learning fusion of multi-satellite and reanalysis data

description: Global seamless Aerosol Optical Depth (AOD) dataset with high accuray is essential for understanding the aerosols impacts on human health, weather, and climate. Traditional AOD reconstruction models are designed to fill gaps in satellite-derived AOD caused by cloud cover, precipitation, or limited satellite overpasses. These models typically rely on a single satellite product as the target variable, with auxiliary inputs such as meteorological and reanalysis data. However, their applicability at the global scale is limited by sample imbalance (e.g. sparse coverage in polar regions and nighttime) and by the inability to effectively incorporate lower-quality satellite products. To overcome these limitations, this study proposes an AOD fusion framework that integrates measurement adjustment theory with a machine learning algorithm to combine multiple satellite AOD products with the MERRA−2 reanalysis dataset. Validation results show that the fused AOD estimates exhibit stronger agreement with AERONET Solar/Lunar observations (R<sup>2</sup> = 0.76/0.66) compared to both traditional reconstruction models (R<sup>2</sup> = 0.68/0.53) and MERRA−2 AOD (R<sup>2</sup> = 0.66/0.60), for daytime/nighttime conditions. The fusion model effectively addresses sample imbalance issues, which often cause traditional models to overestimate AOD, especially in polar regions (e.g. Antarctica, by 0.14) and at night (by 0.06), due to the absence of satellite observations. This improvement stems from the model’s ability to leverage the complementary strengths of multiple datasets through measurement adjustment theory. By using MERRA−2 as a spatiotemporal continuous reference during training, the model not only fills data gaps but also benefits from the enhanced regional performance of individual satellite products. For example, incorporating NOAA and SNPP DT AOD, previously underutilized in reconstruction due to low standalone accuracy, led to further accuracy gains over dark surfaces such as oceans, improving R<sup>2</sup> by 0.01 and reducing RMSE by 0.0027. Notably, the fusion model reduces the annual mean AOD by ~40% compared to reconstruction model, highlighting its potential to correct systematic overestimation in data-sparse regions and produce more reliable high-frequency global AOD datasets.
DOI: 10.6084/m9.figshare.30833155.v1
title: Global hourly seamless AOD through measurement-adjusted machine learning fusion of multi-satellite and reanalysis data

description: Global seamless Aerosol Optical Depth (AOD) dataset with high accuray is essential for understanding the aerosols impacts on human health, weather, and climate. Traditional AOD reconstruction models are designed to fill gaps in satellite-derived AOD caused by cloud cover, precipitation, or limited satellite overpasses. These models typically rely on a single satellite product as the target variable, with auxiliary inputs such as meteorological and reanalysis data. However, their applicability at the global scale is limited by sample imbalance (e.g. sparse coverage in polar regions and nighttime) and by the inability to effectively incorporate lower-quality satellite products. To overcome these limitations, this study proposes an AOD fusion framework that integrates measurement adjustment theory with a machine learning algorithm to combine multiple satellite AOD products with the MERRA−2 reanalysis dataset. Validation results show that the fused AOD estimates exhibit stronger agreement with AERONET Solar/Lunar observations (R<sup>2</sup> = 0.76/0.66) compared to both traditional reconstruction models (R<sup>2</sup> = 0.68/0.53) and MERRA−2 AOD (R<sup>2</sup> = 0.66/0.60), for daytime/nighttime conditions. The fusion model effectively addresses sample imbalance issues, which often cause traditional models to overestimate AOD, especially in polar regions (e.g. Antarctica, by 0.14) and at night (by 0.06), due to the absence of satellite observations. This improvement stems from the model’s ability to leverage the complementary strengths of multiple datasets through measurement adjustment theory. By using MERRA−2 as a spatiotemporal continuous reference during training, the model not only fills data gaps but also benefits from the enhanced regional performance of individual satellite products. For example, incorporating NOAA and SNPP DT AOD, previously underutilized in reconstruction due to low standalone accuracy, led to further accuracy gains over dark surfaces such as oceans, improving R<sup>2</sup> by 0.01 and reducing RMSE by 0.0027. Notably, the fusion model reduces the annual mean AOD by ~40% compared to reconstruction model, highlighting its potential to correct systematic overestimation in data-sparse regions and produce more reliable high-frequency global AOD datasets.
DOI: 10.6084/m9.figshare.28632529
title: Unmixing marsh vegetation species across multiple sensors and spatial scales

description: Coastal marsh ecosystems are critical for providing essential habitats, buffering coastlines against erosion, and sequestering carbon. However, there is a global trend where these ecosystems are disappearing due to rising sea levels, wave-drive erosion, and human activities. Accurate and repeatable observations of marsh vegetation distributions are essential for understanding marsh resilience and informing conservation strategies. Mapping the fractional abundance (FA) of each marsh species using standard airborne/satellite sensor pixels can provide accurate information about marsh vegetation distribution and a deeper insight into marsh dynamics. This study explores the feasibility of estimating FA for marsh vegetation species across the diverse pixel sizes of different remote sensors, based on ground truthing information obtained from Unmanned Aerial Vehicle (UAV) data and field surveys. Specifically, the FA values of each species within the pixels from airborne, WorldView-2 (WV2), and Sentinel-2 (SL2) data, with pixel sizes of 0.15 m, 0.5 m, and 10 m, respectively, were estimated by using a Rescaled Random Forest Regression (RRFR) algorithm. Our results suggest that Random Forest Classification can accurately classify marsh vegetation, with extremely high levels of accuracy when applied to UAV data. This demonstrates that UAVs are cost-effective and efficient for acquiring ground truthing to inform FA estimation algorithms. Our analyses also indicate that the RRFR can accurately unmix dominant marsh species (<i>Spartina</i> and <i>Juncus</i>), as well as bare soil, across various spatial resolutions. However, the unmixing of <i>Salicornia</i>, a minor species, proved to be challenging, indicating the need for additional ground truthing data to capture information relating to minor species. The approach proposed in this study can facilitate the analysis and monitoring of marsh vegetation dynamics, supporting effective conservation and management practices to enhance marsh resilience.
DOI: 10.6084/m9.figshare.28632529.v1
title: Unmixing marsh vegetation species across multiple sensors and spatial scales

description: Coastal marsh ecosystems are critical for providing essential habitats, buffering coastlines against erosion, and sequestering carbon. However, there is a global trend where these ecosystems are disappearing due to rising sea levels, wave-drive erosion, and human activities. Accurate and repeatable observations of marsh vegetation distributions are essential for understanding marsh resilience and informing conservation strategies. Mapping the fractional abundance (FA) of each marsh species using standard airborne/satellite sensor pixels can provide accurate information about marsh vegetation distribution and a deeper insight into marsh dynamics. This study explores the feasibility of estimating FA for marsh vegetation species across the diverse pixel sizes of different remote sensors, based on ground truthing information obtained from Unmanned Aerial Vehicle (UAV) data and field surveys. Specifically, the FA values of each species within the pixels from airborne, WorldView-2 (WV2), and Sentinel-2 (SL2) data, with pixel sizes of 0.15 m, 0.5 m, and 10 m, respectively, were estimated by using a Rescaled Random Forest Regression (RRFR) algorithm. Our results suggest that Random Forest Classification can accurately classify marsh vegetation, with extremely high levels of accuracy when applied to UAV data. This demonstrates that UAVs are cost-effective and efficient for acquiring ground truthing to inform FA estimation algorithms. Our analyses also indicate that the RRFR can accurately unmix dominant marsh species (<i>Spartina</i> and <i>Juncus</i>), as well as bare soil, across various spatial resolutions. However, the unmixing of <i>Salicornia</i>, a minor species, proved to be challenging, indicating the need for additional ground truthing data to capture information relating to minor species. The approach proposed in this study can facilitate the analysis and monitoring of marsh vegetation dynamics, supporting effective conservation and management practices to enhance marsh resilience.
DOI: 10.6084/m9.figshare.28652319
title: Inter-annual changes and growth trends mapping of mangrove using Landsat time series imagery

description: Mangroves, as the most prolific but vulnerable ecosystems, necessitate continuous monitoring for effective conservation. However, continuous mangrove mapping is challenging due to extensive land use changes and highly intertidal dynamics. In this study, a novel mangrove index, named the Composite Mangrove Index (CMI), was developed to map and assess the growth trends of mangroves, based on the truth that the spectral-temporal features of mangrove wetland environment related to greenness, moisture, and bare soil. To facilitate long-term mapping of mangroves, the Continuous Change Detection and Classification (CCDC) algorithm was utilized on the Google Earth Engine platform (GEE). The innovative mangrove mapping framework using the CMI based CCDC was applied in three diverse regions (i.e. Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in southern China, Sundarbans in India and Bangladesh, and Gulf of Paria (GP) in northern Venezuela). The results showed that annual mangrove maps of these three typical regions from 2000 to 2020 achieved overall accuracy exceeding 92%, indicating the ability of CMI to capture the temporal inter-annual characteristics of mangroves. The area of mangroves represented a total increase of 700 ha in the GBA, while slightly decreasing in the Sundarbans and the GP. By comparing Fractional Vegetation Cover (FVC) and CMI with two mangrove types, CMI proved to be effective in not only assessing mangrove growth status but also monitoring recovery and degradation growth trends of mangroves, surpassing some existing indices (MI, EVI, TCA). Controlled experiments also demonstrated that CMI outperformed in mangrove classification compared to some mangrove indices (MI, MVI, CMRI). The distinctive CMI values can offer a rapid method to detect and quantify the growth trends for mangrove afforestation, restoration and even degradation. Therefore, the proposed CMI with CCDC provides new slight to monitor the ongoing efforts in mangrove conservation and shed light on understanding the mangrove dynamics.
DOI: 10.6084/m9.figshare.28652319.v1
title: Inter-annual changes and growth trends mapping of mangrove using Landsat time series imagery

description: Mangroves, as the most prolific but vulnerable ecosystems, necessitate continuous monitoring for effective conservation. However, continuous mangrove mapping is challenging due to extensive land use changes and highly intertidal dynamics. In this study, a novel mangrove index, named the Composite Mangrove Index (CMI), was developed to map and assess the growth trends of mangroves, based on the truth that the spectral-temporal features of mangrove wetland environment related to greenness, moisture, and bare soil. To facilitate long-term mapping of mangroves, the Continuous Change Detection and Classification (CCDC) algorithm was utilized on the Google Earth Engine platform (GEE). The innovative mangrove mapping framework using the CMI based CCDC was applied in three diverse regions (i.e. Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in southern China, Sundarbans in India and Bangladesh, and Gulf of Paria (GP) in northern Venezuela). The results showed that annual mangrove maps of these three typical regions from 2000 to 2020 achieved overall accuracy exceeding 92%, indicating the ability of CMI to capture the temporal inter-annual characteristics of mangroves. The area of mangroves represented a total increase of 700 ha in the GBA, while slightly decreasing in the Sundarbans and the GP. By comparing Fractional Vegetation Cover (FVC) and CMI with two mangrove types, CMI proved to be effective in not only assessing mangrove growth status but also monitoring recovery and degradation growth trends of mangroves, surpassing some existing indices (MI, EVI, TCA). Controlled experiments also demonstrated that CMI outperformed in mangrove classification compared to some mangrove indices (MI, MVI, CMRI). The distinctive CMI values can offer a rapid method to detect and quantify the growth trends for mangrove afforestation, restoration and even degradation. Therefore, the proposed CMI with CCDC provides new slight to monitor the ongoing efforts in mangrove conservation and shed light on understanding the mangrove dynamics.
DOI: 10.15468/dl.nqxsa8
title: Occurrence Download

description: A dataset containing 2276 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Epidendrum nocturnum",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 2276 records from 158 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009905-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bn9682
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Brachyglanis microphthalmus",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 16 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009906-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.