Remote Sensing in Agriculture

Distinct NIR Reflectance Spectra Associated with Foliar Symptoms of Beech Leaf Disease

This study uses multispectral NIR imaging to identify distinct reflectance patterns in beech leaves with BLD symptoms, linking increased reflectance to structural changes in leaf tissue.

Phenomic Prediction I: Plot-Level Prediction of Lodging Severity in Sorghum Breeding Trials Using UAV-Based Photogrammetric Height Data

This study develops an ensemble model using UAV-derived photogrammetric height data to predict plot-level lodging severity in sorghum breeding trials, achieving high accuracy and robustness across sites.

Investigating climate-phenology relationships among the most common Italian forest species using Sentinel-2-derived vegetation phenology and productivity products

This study examines climate-phenology relationships in Italian forest species using remote sensing and machine learning, finding growing season lengthening and species-specific responses to climate factors.

Detection of Red Crown Rot of Soybean in Illinois Fields Using High-Resolution Satellite Imagery and Machine Learning

This paper uses high-resolution satellite imagery and machine learning to map Red Crown Rot in soybean fields, achieving high accuracy in detecting affected canopies.

Assessing the impact of data uncertainty on remote sensing estimation of plant functional diversity

This study evaluates how different types of uncertainty affect remote sensing estimates of plant functional diversity, showing random errors have a greater impact than systematic errors.

Drought and Productivity Trends in Freshwater Flooded Forest Swamps in the North American Coastal Plain

This study examines the relationship between drought and productivity in North American Coastal Plain wetlands using satellite data and drought indices, finding spatially variable impacts.

An open field phenomics resource for multimodal maize yield prediction across divergent environments

Drone imagery from 356 flights in 19 Genomes to Fields maize environments; vegetation-index functional principal components complement genomic kernels, predicting yield of held-out hybrids at r up to 0.501.

Reflectance spectra capture temporal variation in functional traits and their integration across leaf phenology

Weekly leaf spectra and traits in seven temperate species reveal phenophase-dependent trait correlations; peak-season PLSR models fail at senescence while full-season models track trait dynamics.

PhenoStream: A Cyberinfrastructure for Automated and AI-Based Crop Trait Extraction from Aerial Imagery

PhenoStream automates crop trait extraction from aerial imagery, enabling high-throughput phenotyping with reduced latency and manual effort.

Combining 3D-multispectral and hyperspectral imaging to identify environmental stress treatments imposed during plant growth

What do satellite vegetation indices actually measure? Photosynthetic capacity rather than transient physiological function across 328 global FLUXNET sites

This paper investigates what satellite vegetation indices measure by analyzing their relationship with GPP across 328 FLUXNET sites, finding they reflect structural capacity rather than dynamic physiological function.

Spectral network analysis illuminates coordinated planttraits across a climate gradient

This study uses hyperspectral data and network analysis to show how plant populations adapt to climate change through coordinated trait networks rather than isolated traits.

AngleCam V2: Predicting leaf inclination angles across taxa from daytime and nighttime photos

AngleCam V2 estimates leaf inclination angles from daytime and nighttime photos across species, showing improved accuracy and generalization compared to its predecessor.

Robot-based 3D-multispectral monitoring of soybean in a spatially heterogenous agrivoltaic environment

The paper uses a robot-mounted 3D-multispectral imaging system to monitor soybean growth and spectral reflectance in an agrivoltaic environment, revealing growth variations and stress symptoms in different genotypes.

Turning a new leaf: PhenoVision provides leaf phenology data at the global scale

PhenoVision-Leaf uses a Vision Transformer model trained on crowdsourced data to generate global-scale leaf phenology observations from iNaturalist records.

Global variations of Light Use Efficiency in Forests Jointly Driven by Plant Traits and Climatic Conditions

The paper analyzes global light use efficiency (LUE) in forests using remote sensing data, finding it is driven by plant traits and climate, with evergreen broadleaf forests having the highest LUE.

Repeatability and Heritability of UAV-Derived Canopy Traits in a Cassava Breeding Population Using Time-Series Data from Two Consecutive Growing Seasons

This study evaluates UAV-derived canopy traits in cassava to assess their repeatability and heritability across two growing seasons, finding high repeatability but variable heritability among traits.

Quantifying growth and lodging in Tef (Eragrostis tef) with Uncrewed Aerial Systems (UAS)

This paper uses UAS to measure tef growth and lodging, showing early height can predict lodging and that low-cost UAS can accurately measure lodging severity.

Prediction of late blight severity in a large panel of potato genotypes using low-altitude aerial images and machine learning methods

This study uses UAV-derived multispectral imagery and machine learning to predict late blight severity in potato genotypes, showing improved accuracy over traditional methods.

Rocking around the pheno-clock: bridging vegetation phenology and chronobiology

This paper develops a pheno-clock framework to translate plant phenology into chrono-ecological properties using satellite data and actigraphy-inspired metrics, revealing asymmetries in spring and autumn phenology and distinct pheno-chronotypes across regions.

Benchmarking remote sensing methods to capture plant functional diversity from space

This study benchmarks remote sensing methods to estimate plant functional diversity using synthetic data from BOSSE, identifying optimal metrics, variables, and validation strategies.

Mapping canopy foliar functional traits in a mixed temperate forest using imaging spectroscopy

The paper maps 12 foliar functional traits in a mixed temperate forest using airborne imaging spectroscopy and develops models to predict these traits from reflectance spectra.

Sentinel-2 based estimates of rangeland fractional cover and canopy gap class for the western United States

The paper estimates rangeland fractional cover and canopy gap classes using Sentinel-2 satellite data for the western United States from 2018 to 2024.

Benchmarking phenology with a land surface model: an in- silico experiment for temperate forests

This study benchmarks phenological detection methods using a land surface model to simulate temperate forest dynamics under climate perturbations, finding that low thresholds and inflection points best match model-predicted start of season.

Challenges and opportunities in detecting leaf water and carotenoid content across biomes from satellite multispectral indices

This study evaluates the sensitivity of multispectral vegetation indices to leaf water and carotenoid content across biomes and finds that leaf area index explains most of the variability.

Estimating resource acquisition and water-use traits in wine grapes using reflectance spectroscopy

This study uses reflectance spectroscopy to estimate resource acquisition and water-use traits in wine grapes, showing it can detect intraspecific variation across cultivars.

Asymmetric Spring and Autumn Phenology Control Growing Season Length in Temperate Deciduous Forests

The paper analyzes phenological dynamics across four decades and 10 tree species to show that spring and autumn phenology asymmetrically control growing season length, with spring driving interannual variation and autumn influencing long-term trends.

Prediction of harvest-related traits in barley using high-throughput phenotyping data and machine learning

This study uses high-throughput phenotyping and machine learning to predict harvest-related traits in barley under drought conditions, achieving high accuracy with early and late-stage predictors.

The Fallacy and Bias of Averages on Vegetation Indices based Plant Phenotyping

This paper shows that averaging vegetation indices can bias plant phenotyping results under nonlinear relationships and proposes a correction method to improve accuracy.

Disentangling heritability and plasticity effects on Populus fremontii leaf reflectance across a temperature gradient

This study quantifies genetic and environmental effects on leaf reflectance of Populus fremontii across temperature gradients using common gardens and variance partitioning.