Species information in multi-temporal Sentinel-2 data improves forest canopy height estimation - Changhyun Choi; Youngryel Ryu; Benjamin Dechant; Tackang Yang; Liang Wan; Sungchan Jeong; Jin Wu (Agricultural and Forest Meteorology, 2026)

GPP-net: a robust high-resolution GPP estimation network for Sentinel-2 using only surface reflectance and photosynthetically active radiation - Shaoyu Wang; Youngryel Ryu; Benjamin Dechant; Helin Zhang; Huaize Feng; Jeongho Lee; Changhyun Choi (Remote Sensing of Environment, 2026)

Development of an automated hyperspectral system to monitor hyperspectral reflectance and sun-induced chlorophyll fluorescence with directional and hemispheric view geometries simultaneously - Jeongho Lee; Youngryel Ryu; Jongmin Kim; Benjamin Dechant; Sangjun Lee; Joongbin Lim (Remote Sensing of Environment, 2026)

Uncovering the importance of spatiotemporal resolution in satellite-based rice yield estimation using a simple but effective proxy - Juwon Kong; Youngryel Ryu; Benjamin Dechant; Sungchan Jeong; Jongmin Kim; Kyungdo Lee; Bonil Ku; Rasmus Houborg (Agricultural and Forest Meteorology, 2026)

GEOSIF: A continental-scale sub-daily reconstructed solar-induced fluorescence derived from OCO-3 and GK-2A over Eastern Asia and Oceania - Sungchan Jeong; Youngryel Ryu; Xing Li; Benjamin Dechant; Jiangong Liu; Juwon Kong; Wonseok Choi; Jianing Fang; Xu Lian; Pierre Gentine (Remote Sensing of Environment, 2024)

Persistent global greening over the last four decades using novel long-term vegetation index data with enhanced temporal consistency - Sungchan Jeong; Youngryel Ryu; Pierre Gentine; Xu Lian; Jianing Fang; Xing Li; Benjamin Dechant; Juwon Kong; Wonseok Choi; Chongya Jiang; Trevor F. Keenan; Sandy P. Harrison; et al. (Remote Sensing of Environment, 2024)

Intercomparison of global foliar trait maps reveals fundamental differences and limitations of upscaling approaches - Benjamin Dechant; Jens Kattge; Ryan Pavlick; Fabian D. Schneider; Francesco M. Sabatini; Álvaro Moreno-Martínez; Ethan E. Butler; Peter M. van Bodegom; Helena Vallicrosa; Teja Kattenborn; Coline C.F. Boonman; Nima Madani; et al. (Remote Sensing of Environment, 2024)

Correcting confounding canopy structure, biochemistry and soil background effects improves leaf area index estimates across diverse ecosystems from Sentinel-2 imagery - Liang Wan; Youngryel Ryu; Benjamin Dechant; Yorum Hwang; Huaize Feng; Yanghui Kang; Sungchan Jeong; Jeongho Lee; Changhyun Choi; Joonhwan Bae (Remote Sensing of Environment, 2024)

Improving retrieval of leaf chlorophyll content from Sentinel-2 and Landsat-7/8 imagery by correcting for canopy structural effects - Liang Wan; Youngryel Ryu; Benjamin Dechant; Jeongho Lee; Zilong Zhong; Huaize Feng (Remote Sensing of Environment, 2024)

A multi-objective optimization approach to simultaneously halve water consumption, CH4, and N2O emissions while maintaining rice yield - Yulin Yan; Youngryel Ryu; Bolun Li; Benjamin Dechant; Sheir Afgen Zaheer; Minseok Kang (Agricultural and Forest Meteorology, 2024)

BESSv2.0: A satellite-based and coupled-process model for quantifying long-term global land–atmosphere fluxes - Bolun Li; Youngryel Ryu; Chongya Jiang; Benjamin Dechant; Jiangong Liu; Yulin Yan; Xing Li (Remote Sensing of Environment, 2023)

Global leaf‐trait mapping based on optimality theory - Ning Dong; Benjamin Dechant; Han Wang; Ian J. Wright; Iain Colin Prentice (Global Ecology and Biogeography, 2023)
Aim Leaf traits are central to plant function, and key variables in ecosystem models. However recently published global trait maps, made by applying statistical or machine‐learning techniques to large compilations of trait and environmental data, differ substantially from one another. This paper aims to demonstrate the potential of an alternative approach, based on eco‐evolutionary optimality theory, to yield predictions of spatio‐temporal patterns in leaf traits that can be independently evaluated. Innovation Global patterns of community‐mean specific leaf area (SLA) and photosynthetic capacity ( V cmax ) are predicted from climate via existing optimality models. Then leaf nitrogen per unit area ( N area ) and mass ( N mass ) are inferred using their (previously derived) empirical relationships to SLA and V cmax . Trait data are thus reserved for testing model predictions across sites. Temporal trends can also be predicted, as consequences of environmental change, and compared to those inferred from leaf‐level measurements and/or remote‐sensing methods, which are an increasingly important source of information on spatio‐temporal variation in plant traits. Main conclusions Model predictions evaluated against site‐mean trait data from > 2,000 sites in the Plant Trait database yielded R 2 = 73% for SLA, 38% for N mass and 28% for N area . Declining species‐level N mass , and increasing community‐level SLA, have both been recently reported and were both correctly predicted. Leaf‐trait mapping via optimality theory holds promise for macroecological applications, including an improved understanding of community leaf‐trait responses to environmental change.

Tracking diurnal to seasonal variations of gross primary productivity using a geostationary satellite, GK-2A advanced meteorological imager - Sungchan Jeong; Youngryel Ryu; Benjamin Dechant; Xing Li; Juwon Kong; Wonseok Choi; Minseok Kang; Jongmin Yeom; Joongbin Lim; Keunchang Jang; Junghwa Chun (Remote Sensing of Environment, 2023)

TRY plant trait database – enhanced coverage and open access - Jens Kattge; Gerhard Bönisch; Sandra Díaz; Sandra Lavorel; Iain Colin Prentice; Paul Leadley; Susanne Tautenhahn; Gijsbert D. A. Werner; Tuomas Aakala; Mehdi Abedi; Alicia T. R. Acosta; George C. Adamidis; et al. (Global Change Biology, 2020)
Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.