Publications & Conferences
Internal soil standard as a tool to assess and correct spectral variability between laboratories for a practical quantitative utilization
Soil spectroscopy is increasingly used to provide accurate and affordable prediction of various soil properties. However, variability in instrument characteristics and operational protocols still hinders the integration and harmonization of Visible-Near InfraRed- Short Wave InfraRed (VNIR–SWIR) Soil Spectral Libraries (SSLs). This study examines the use of 99% pure silica sand, Lucky Bay (LB), as an...
A soil satellite spectral service (Sat4): A strategy for stakeholders
Soil spectral libraries (SSLs) are commonly built with expensive proximal sensors, while satellite spectral data are free and widely available. This study investigates how service providers can construct a satellite-based SSL to deliver soil information to stakeholders. We propose the Soil Satellite Spectral Service (Sat4-Service) strategy to estimate clay content and soil organic carbon (SOC)...
Recent Advances in Remote Sensing of Soil Science
Soil is the foundation of terrestrial life, underpinning ecosystem services, food production, and climate regulation. The accurate characterization of soil properties at regional to global scales is both a scientific and societal priority. Remote sensing (RS) provides the observational backbone for such characterization. When combined with artificial intelligence (AI) and machine learning (ML) algorithms, it...
SKIN MICROVASCULAR FUNCTION PREDICTS 10-YEAR CARDIOVASCULAR RISK IN TREATMENT-NAÏVE HYPERTENSIVE AND NORMOTENSIVE INDIVIDUALS
Objective:Skin microvascular dysfunction has been documented both in patients with established cardiovascular disease (CVD) and individuals at elevated cardiovascular risk. The aim of this study was to investigate whether markers of skin microvascular function correlate with the estimated 10-year cardiovascular risk in a population without established CVD.Design and method:We recruited newly diagnosed, untreated hypertensives (UHTs)...
GeoGPT for action ready flood and disaster risk geo-intelligence in Florida
Extreme weather in Florida can result compound impacts, crop damage, prolonged waterlogging, and inundation, that disrupt farm activities and complicate field scale assessment. Following an event, extension agents and growers typically need information on short timelines related to crop damage assessment to prioritize scouting, report impacts, and support recovery decisions, and flood-prone area information to...
Impact of coupling biochar and clay minerals on physicochemical properties of sandy soils and CH4/CO2 flux for agriculture
Sandy soils present key limitations to sustainable agriculture due to low water retention, poor aggregation, and limited nutrient-holding capacity, motivating the use of amendments to overcome these constraints. This study evaluated zeolite, bentonite, and aluminum hydroxide (Al(OH)3) at 2, 5, and 10 wt%, with and without biochar, on the physical, chemical, and gas-exchange properties of a...
A Cloud-Based Hyperspectral Framework for Scalable and Sustainable Soil Analysis
Assessing soils over large areas like Brazil faces challenges due to limited laboratory infrastructure, high costs, and environmental concerns linked to excessive chemical use. Reflectance spectroscopy and cloud computing offer quick, scalable, and environmentally friendly alternatives. This study reviews the Brazilian Soil Spectral Service (BraSpecS), a cloud-based hyperspectral platform designed to predict soil attributes through...
Federated earth-observation models for collaborative farm-scale soil mapping
Accurate, privacy-respecting soil information is essential for site-specific nutrient management and carbon accounting, yet the cost of laboratory analyses limits many farms to relatively sparse sampling grids. We propose a collaborative framework that couples a national Sentinel-2 bare-soil composite with FL to produce high-resolution clay and soil organic carbon (SOC) maps while keeping all local...
Soil laboratory and satellite spectral data filtering: A Spectral Quality Protocol (SQuaP)
Soil spectroscopy is a powerful technique for soil monitoring. Hundreds of soil spectral datasets, both open-access and private, are used for various applications, despite potential errors. This study presents a filtering protocol to enhance the quality of soil spectral datasets from laboratory and satellite sources, here termed the Spectral Quality Protocol (SQuaP). At the beginning,...
Applications of Artificial Intelligence in Water Resources Forecasting: SL537/SS752, 12/2025
The advent of artificial intelligence (AI) and machine learning has permeated every aspect of our lives. Water resource management is becoming increasingly important due to the impacts of climate change and human activities. This publication provides examples of how AI can potentially improve the forecasting of water resource variables at both the field and regional...
Internal soil standard as a tool to assess and correct spectral variability between laboratories for a practical quantitative utilization
Soil spectroscopy is increasingly used to provide accurate and affordable prediction of various soil properties. However, variability in instrument characteristics and operational protocols still hinders the integration and harmonization of Visible-Near InfraRed- Short Wave InfraRed (VNIR–SWIR) Soil Spectral Libraries (SSLs). This study examines the use of 99% pure silica sand, Lucky Bay (LB), as an...
A soil satellite spectral service (Sat4): A strategy for stakeholders
Soil spectral libraries (SSLs) are commonly built with expensive proximal sensors, while satellite spectral data are free and widely available. This study investigates how service providers can construct a satellite-based SSL to deliver soil information to stakeholders. We propose the Soil Satellite Spectral Service (Sat4-Service) strategy to estimate clay content and soil organic carbon (SOC)...
Recent Advances in Remote Sensing of Soil Science
Soil is the foundation of terrestrial life, underpinning ecosystem services, food production, and climate regulation. The accurate characterization of soil properties at regional to global scales is both a scientific and societal priority. Remote sensing (RS) provides the observational backbone for such characterization. When combined with artificial intelligence (AI) and machine learning (ML) algorithms, it...
SKIN MICROVASCULAR FUNCTION PREDICTS 10-YEAR CARDIOVASCULAR RISK IN TREATMENT-NAÏVE HYPERTENSIVE AND NORMOTENSIVE INDIVIDUALS
Objective:Skin microvascular dysfunction has been documented both in patients with established cardiovascular disease (CVD) and individuals at elevated cardiovascular risk. The aim of this study was to investigate whether markers of skin microvascular function correlate with the estimated 10-year cardiovascular risk in a population without established CVD.Design and method:We recruited newly diagnosed, untreated hypertensives (UHTs)...
GeoGPT for action ready flood and disaster risk geo-intelligence in Florida
Extreme weather in Florida can result compound impacts, crop damage, prolonged waterlogging, and inundation, that disrupt farm activities and complicate field scale assessment. Following an event, extension agents and growers typically need information on short timelines related to crop damage assessment to prioritize scouting, report impacts, and support recovery decisions, and flood-prone area information to...
Impact of coupling biochar and clay minerals on physicochemical properties of sandy soils and CH4/CO2 flux for agriculture
Sandy soils present key limitations to sustainable agriculture due to low water retention, poor aggregation, and limited nutrient-holding capacity, motivating the use of amendments to overcome these constraints. This study evaluated zeolite, bentonite, and aluminum hydroxide (Al(OH)3) at 2, 5, and 10 wt%, with and without biochar, on the physical, chemical, and gas-exchange properties of a...
A Cloud-Based Hyperspectral Framework for Scalable and Sustainable Soil Analysis
Assessing soils over large areas like Brazil faces challenges due to limited laboratory infrastructure, high costs, and environmental concerns linked to excessive chemical use. Reflectance spectroscopy and cloud computing offer quick, scalable, and environmentally friendly alternatives. This study reviews the Brazilian Soil Spectral Service (BraSpecS), a cloud-based hyperspectral platform designed to predict soil attributes through...
Federated earth-observation models for collaborative farm-scale soil mapping
Accurate, privacy-respecting soil information is essential for site-specific nutrient management and carbon accounting, yet the cost of laboratory analyses limits many farms to relatively sparse sampling grids. We propose a collaborative framework that couples a national Sentinel-2 bare-soil composite with FL to produce high-resolution clay and soil organic carbon (SOC) maps while keeping all local...
Soil laboratory and satellite spectral data filtering: A Spectral Quality Protocol (SQuaP)
Soil spectroscopy is a powerful technique for soil monitoring. Hundreds of soil spectral datasets, both open-access and private, are used for various applications, despite potential errors. This study presents a filtering protocol to enhance the quality of soil spectral datasets from laboratory and satellite sources, here termed the Spectral Quality Protocol (SQuaP). At the beginning,...
Applications of Artificial Intelligence in Water Resources Forecasting: SL537/SS752, 12/2025
The advent of artificial intelligence (AI) and machine learning has permeated every aspect of our lives. Water resource management is becoming increasingly important due to the impacts of climate change and human activities. This publication provides examples of how AI can potentially improve the forecasting of water resource variables at both the field and regional...