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portfolio
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publications
Wireless sensor network for in situ soil moisture monitoring
Published in Proceedings of the Tenth International Conference on Sensor Networks (SENSORNETS 2021), 2021
We discuss the history and lessons learned from a series of deployments of environmental sensors measuring soil parameters and CO2 fluxes over the last fifteen years, in an outdoor environment. We present the hardware and software architecture of our current Gen-3 system, and then discuss how we are simplifying the user facing part of the software, to make it easier and friendlier for the environmental scientist to be in full control of the system. Finally, we describe the current effort to build a large-scale Gen-4 sensing platform consisting of hundreds of nodes to track the environmental parameters for urban green spaces in Baltimore, Maryland.
Recommended citation: Fang, J., Hu, C., Smaoui, N., Carlson, D., Gupchup, J., Musaloiu-E, R., ... & Szalay, A. S. (2021). Wireless sensor network for in situ soil moisture monitoring. Proceedings of the Tenth International Conference on Sensor Networks (SENSORNETS 2021). https://www.scitepress.org/Papers/2021/102615/102615.pdf
Challenges in Reconciling Satellite-Based and Locally Reported Estimates of Wetland Change: A Case of Topographically Constrained Wetlands on the Eastern Tibetan Plateau
Published in Remote Sensing, 2021
The coupling of rapid warming and wetland degradation on the Tibetan Plateau has motivated studies of climate influence on wetland change in the region. These studies typically examine large, topographically homogeneous regions, whereas conservation efforts sometimes require fine-grained information in rugged terrain. This study addresses topographically constrained wetlands on the Eastern Tibetan, where herders report significant wetland degradation. We used Landsat images to examine changes in wetland areas and Sentinel-1 SAR images to investigate water level and vegetation structure. We also analyzed trends in precipitation, growing season length, and reference evapotranspiration in weather station records. Snow cover and the vegetation growing season were quantified using MODIS observations. We analyzed estimates of actual evapotranspiration using the Atmosphere-Land Exchange Inverse model (ALEXI) and the Simplified Surface Energy Balance model (SSEBop). Satellite-informed analyses failed to confirm herders’ accounts of reduced wetland function, as no coherent trends were found in wetland area, water content, or vegetation structure. An analysis of meteorological records did indicate a warming-induced increase in reference evapotranspiration, and both meteorological records and satellites suggest that the growing season had lengthened, potentially increasing water demand and driving wetland change. The discrepancies between the satellite data and local observations pointed to temporal, spatial, and epistemological gaps in combining scientific data with empirical evidence in understanding wetland change on the Tibetan Plateau.
Recommended citation: Fang, J., & Zaitchik, B. (2021). Challenges in reconciling satellite-based and locally reported estimates of wetland change: A case of topographically constrained wetlands on the Eastern Tibetan Plateau. Remote Sensing, 13(8), 1484. https://www.mdpi.com/2072-4292/13/8/1484
Satellite solar‐induced chlorophyll fluorescence tracks physiological drought stress development during 2020 southwest US drought
Published in Global Change Biology, 2023
Monitoring and estimating drought impact on plant physiological processes over large regions remains a major challenge for remote sensing and land surface modeling, with important implications for understanding plant mortality mechanisms and predicting the climate change impact on terrestrial carbon and water cycles. The Orbiting Carbon Observatory 3 (OCO-3), with its unique diurnal observing capability, offers a new opportunity to track drought stress on plant physiology. Using radiative transfer and machine learning modeling, we derive a metric of afternoon photosynthetic depression from OCO-3 solar-induced chlorophyll fluorescence (SIF) as an indicator of plant physiological drought stress. This unique diurnal signal enables a spatially explicit mapping of plants’ physiological response to drought. Using OCO-3 observations, we detect a widespread increasing drought stress during the 2020 southwest US drought. Although the physiological drought stress is largely related to the vapor pressure deficit (VPD), our results suggest that plants’ sensitivity to VPD increases as the drought intensifies and VPD sensitivity develops differently for shrublands and grasslands. Our findings highlight the potential of using diurnal satellite SIF observations to advance the mechanistic understanding of drought impact on terrestrial ecosystems and to improve land surface modeling.
Recommended citation: Zhang, Y., Fang, J., Smith, W. K., Wang, X., Gentine, P., Scott, R. L., ... & Zhou, S. (2023). Satellite solar‐induced chlorophyll fluorescence tracks physiological drought stress development during 2020 southwest US drought. Global Change Biology, 29(12), 3395-3408. https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.16683
Diminishing carryover benefits of earlier spring vegetation growth
Published in Nature Ecology & Evolution, 2024
Spring vegetation growth can benefit summer growth by increasing foliage area and carbon sequestration potential, or impair it by consuming additional resources needed for sustaining subsequent growth. However, the prevalent driving mechanism and its temporal changes remain unknown. Using satellite observations and long-term atmospheric CO2 records, here we show a weakening trend of the linkage between spring and summer vegetation growth/productivity in the Northern Hemisphere during 1982–2021. This weakening is driven by warmer and more extreme hot weather that becomes unfavourable for peak-season growth, shifting peak plant functioning away from earlier periods. This is further exacerbated by seasonally growing ecosystem water stress due to reduced water supply and enhanced water demand. Our finding suggests that beneficial carryover effects of spring growth on summer growth are diminishing or even reversing, acting as an early warning sign of the ongoing shift of climatic effects from stimulating to suppressing plant photosynthesis during the early to peak seasons.
Recommended citation: Lian, X., Peñuelas, J., Ryu, Y., Piao, S., Keenan, T. F., Fang, J., ... & Gentine, P. (2024). Diminishing carryover benefits of earlier spring vegetation growth. Nature ecology & evolution, 8(2), 218-228. https://www.nature.com/articles/s41559-023-02272-w
Differentiable Land Model Reveals Global Environmental Controls on Ecological Parameters
Published in Under Review. Preprint available on arXiv, 2024
Accurate modeling of terrestrial carbon and water exchange requires robust ecological parameters that capture vegetation responses and adaptations to the local environment. The current generation of land models use Plant Functional Types (PFTs) to discretize vegetation functional diversity, but these coarse categorizations often overlook fine-scale variations shaped by local climate, soil, and forest age factors. The lack of governing equations for plant adaptation demands a paradigm shift in how we integrate diverse Earth observations to uncover ecological functional dependence on changing environments. To address this challenge, we developed DifferLand, a differentiable, hybrid physics and machine learning model that infers the spatial distributions of ecological parameters and their relationships with environmental factors constrained by satellite and in-situ observations. Our model unifies top-down and bottom-up observational constraints with process-based knowledge to generate a global analysis of ecological functions and their adaptation to environmental gradients. We found PFTs account for less than half of the explainable spatial parameter variations controlling carbon fluxes and vegetation states. The remaining parameter variability is largely driven by local climate and forest demography factors, and the learned environment-parameter relationships lead to enhanced spatial generalization at unseen locations. DifferLand identified growing season length, leaf economics, and agricultural intensity as the three orthogonal spatial gradients underlying parameter variations. Our novel framework can lead to new insights on global carbon cycling by learning directly from data and expanding our understanding of local responses of ecosystems to environmental drivers.
Recommended citation: Fang, J., Bowman, K., Zhao, W., Lian, X., & Gentine, P. (2024). Differentiable Land Model Reveals Global Environmental Controls on Ecological Parameters. arXiv preprint arXiv:2411.09654. https://arxiv.org/abs/2411.09654
Exploring Optimal Complexity for Water Stress Representation in Terrestrial Carbon Models: A Hybrid‐Machine Learning Model Approach
Published in Journal of Advances in Modeling Earth Systems, 2024
Accurate modeling of terrestrial carbon and water exchange requires robust ecological parameters that capture vegetation responses and adaptations to the local environment. The current generation of land models use Plant Functional Types (PFTs) to discretize vegetation functional diversity, but these coarse categorizations often overlook fine-scale variations shaped by local climate, soil, and forest age factors. The lack of governing equations for plant adaptation demands a paradigm shift in how we integrate diverse Earth observations to uncover ecological functional dependence on changing environments. To address this challenge, we developed DifferLand, a differentiable, hybrid physics and machine learning model that infers the spatial distributions of ecological parameters and their relationships with environmental factors constrained by satellite and in-situ observations. Our model unifies top-down and bottom-up observational constraints with process-based knowledge to generate a global analysis of ecological functions and their adaptation to environmental gradients. We found PFTs account for less than half of the explainable spatial parameter variations controlling carbon fluxes and vegetation states. The remaining parameter variability is largely driven by local climate and forest demography factors, and the learned environment-parameter relationships lead to enhanced spatial generalization at unseen locations. DifferLand identified growing season length, leaf economics, and agricultural intensity as the three orthogonal spatial gradients underlying parameter variations. Our novel framework can lead to new insights on global carbon cycling by learning directly from data and expanding our understanding of local responses of ecosystems to environmental drivers.
Recommended citation: Fang, J., & Gentine, P. (2024). Exploring Optimal Complexity for Water Stress Representation in Terrestrial Carbon Models: A Hybrid‐Machine Learning Model Approach. Journal of Advances in Modeling Earth Systems, 16(12), e2024MS004308. https://doi.org/10.1029/2024MS004308
A long-term reconstruction of a global photosynthesis proxy over 1982–2023
Published in Scientific Data, 2025
Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982–2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001–2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982–2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.
Recommended citation: Fang, J., Lian, X., Ryu, Y., Jeong, S., Jiang, C., & Gentine, P. (2025). A long-term reconstruction of a global photosynthesis proxy over 1982–2023. Scientific data, 12(1), 372. https://www.nature.com/articles/s41597-025-04686-6
Combining Observations and Models: A Review of the CARDAMOM Framework for Data‐Constrained Terrestrial Ecosystem Modeling
Published in Global Change Biology, 2025
The rapid increase in the volume and variety of terrestrial biosphere observations (i.e., remote sensing data and in situ measurements) offers a unique opportunity to derive ecological insights, refine process-based models, and improve forecasting for decision support. However, despite their potential, ecological observations have primarily been used to benchmark process-based models, as many past and current models lack the capability to directly integrate observations and their associated uncertainties for parameterization. In contrast, data assimilation frameworks such as the CARbon DAta MOdel fraMework (CARDAMOM) and its suite of process-based models, known as the Data Assimilation Linked Ecosystem Carbon Model (DALEC), are specifically designed for model-data fusion. This review, motivated by a recent CARDAMOM community workshop, examines the development and applications of CARDAMOM, with an emphasis on its role in advancing ecosystem process understanding. CARDAMOM employs a Bayesian approach, using a Markov Chain Monte Carlo algorithm to enable data-driven calibration of DALEC parameters and initial states (i.e., carbon pool sizes) through observation operators. CARDAMOM’s unique ability to retrieve localized model process parameters from diverse datasets—ranging from in situ measurements to global satellite observations—makes it a highly flexible tool for analyzing spatially variable ecosystem responses to environmental change. However, assimilating these data also presents challenges, including data quality issues that propagate into model skill, as well as trade-offs between model complexity, parameter equifinality, and predictive performance. We discuss potential solutions to these challenges, such as reducing parameter equifinality by incorporating new observations. This review also offers community recommendations for incorporating emerging datasets, integrating machine learning techniques, strengthening collaboration with remote sensing, field, and modeling communities, and expanding CARDAMOM’s relevance for localized ecosystem monitoring and decision-making. CARDAMOM enables a deep, mechanistic understanding of terrestrial ecosystem dynamics that cannot be achieved through empirical analyses of observational datasets or weakly constrained models alone.
Recommended citation: Worden, M. A., Bilir, T. E., Bloom, A. A., Fang, J., Klinek, L. P., Konings, A. G., ... & Zhu, S. (2025). Combining Observations and Models: A Review of the CARDAMOM Framework for Data‐Constrained Terrestrial Ecosystem Modeling. Global Change Biology, 31(8), e70462. https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.70462
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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