Tim J. Green

Leaf area index (LAI) is a key biophysical variable which quantifies the surface area for light capture and photosynthetic activity per unit ground area, giving a first order constraint on potential photosynthesis. LAI is tightly coupled to the carbon, energy, and water cycles of the global terrestrial system. Numerous Earth observation (EO) products provide estimates of LAI over time (LAI_EO), delivering valuable information on plant phenology and canopy dynamics. Widely-used LAI_EO, however, consistently exhibit unrealistic seasonality in evergreen needleleaf forests at the northern latitudes. Taking a model-data fusion approach, we show that naïvely assimilating biased, whole-year LAI_EO (i.e., the business-as-usual (BAU) approach) at three well-studied evergreen needleleaf forests in Fennoscandia implies an ecosystem carbon cycle which is unrealistic and inconsistent with independent lines of evidence. We further demonstrate that the model-data fusion framework, CARDAMOM, is capable of diagnosing realistic seasonal amplitudes of LAI by assimilating localised information on leaf lifespan coupled with summer-only LAI_EO (i.e., the alternative (ALT) approach). Important differences arise from the BAU and ALT experiments. The BAU experiment showed highly seasonal canopy dynamics and diagnostic leaf traits erroneously consistent with deciduous species. Conversely, the ALT experiment displayed canopy dynamics and functional characteristics more reflective of evergreen needleleaf species. For BAU, biases in LAI_EO propagated throughout the carbon cycle, especially in the southern, more productive sites. This investigation highlights the need for improved LAI_EO estimates in northern evergreen forests to enhance understanding of carbon cycle processes in this region of rapid warming and large carbon stores, and provides a mechanism for improvement using independent leaf trait data.

Leaf area index (LAI) is a key biophysical variable which quantifies the surface area for light capture and photosynthetic activity per unit ground area, giving a first order constraint on potential photosynthesis. LAI is tightly coupled to the carbon, energy, and water cycles of the global terrestrial system. Numerous Earth observation (EO) products provide estimates of LAI over time (LAI_EO), delivering valuable information on plant phenology and canopy dynamics. Widely-used LAI_EO, however, consistently exhibit unrealistic seasonality in evergreen needleleaf forests at the northern latitudes. Taking a model-data fusion approach, we show that naïvely assimilating biased, whole-year LAI_EO (i.e., the business-as-usual (BAU) approach) at three well-studied evergreen needleleaf forests in Fennoscandia implies an ecosystem carbon cycle which is unrealistic and inconsistent with independent lines of evidence. We further demonstrate that the model-data fusion framework, CARDAMOM, is capable of diagnosing realistic seasonal amplitudes of LAI by assimilating localised information on leaf lifespan coupled with summer-only LAI_EO (i.e., the alternative (ALT) approach). Important differences arise from the BAU and ALT experiments. The BAU experiment showed highly seasonal canopy dynamics and diagnostic leaf traits erroneously consistent with deciduous species. Conversely, the ALT experiment displayed canopy dynamics and functional characteristics more reflective of evergreen needleleaf species. For BAU, biases in LAI_EO propagated throughout the carbon cycle, especially in the southern, more productive sites. This investigation highlights the need for improved LAI_EO estimates in northern evergreen forests to enhance understanding of carbon cycle processes in this region of rapid warming and large carbon stores, and provides a mechanism for improvement using independent leaf trait data.

Leaf area index (LAI) is a key biophysical variable which quantifies the surface area for light capture and photosynthetic activity per unit ground area, giving a first order constraint on potential photosynthesis. LAI is tightly coupled to the carbon, energy, and water cycles of the global terrestrial system. Numerous Earth observation (EO) products provide estimates of LAI over time (LAI_EO), delivering valuable information on plant phenology and canopy dynamics. Widely-used LAI_EO, however, consistently exhibit unrealistic seasonality in evergreen needleleaf forests at the northern latitudes. Taking a model-data fusion approach, we show that naïvely assimilating biased, whole-year LAI_EO (i.e., the business-as-usual (BAU) approach) at three well-studied evergreen needleleaf forests in Fennoscandia implies an ecosystem carbon cycle which is unrealistic and inconsistent with independent lines of evidence. We further demonstrate that the model-data fusion framework, CARDAMOM, is capable of diagnosing realistic seasonal amplitudes of LAI by assimilating localised information on leaf lifespan coupled with summer-only LAI_EO (i.e., the alternative (ALT) approach). Important differences arise from the BAU and ALT experiments. The BAU experiment showed highly seasonal canopy dynamics and diagnostic leaf traits erroneously consistent with deciduous species. Conversely, the ALT experiment displayed canopy dynamics and functional characteristics more reflective of evergreen needleleaf species. For BAU, biases in LAI_EO propagated throughout the carbon cycle, especially in the southern, more productive sites. This investigation highlights the need for improved LAI_EO estimates in northern evergreen forests to enhance understanding of carbon cycle processes in this region of rapid warming and large carbon stores, and provides a mechanism for improvement using independent leaf trait data.

Leaf area index (LAI) is a key biophysical variable which quantifies the surface area for light capture and photosynthetic activity per unit ground area, giving a first order constraint on potential photosynthesis. LAI is tightly coupled to the carbon, energy, and water cycles of the global terrestrial system. Numerous Earth observation (EO) products provide estimates of LAI over time (LAI_EO), delivering valuable information on plant phenology and canopy dynamics. Widely-used LAI_EO, however, consistently exhibit unrealistic seasonality in evergreen needleleaf forests at the northern latitudes. Taking a model-data fusion approach, we show that naïvely assimilating biased, whole-year LAI_EO (i.e., the business-as-usual (BAU) approach) at three well-studied evergreen needleleaf forests in Fennoscandia implies an ecosystem carbon cycle which is unrealistic and inconsistent with independent lines of evidence. We further demonstrate that the model-data fusion framework, CARDAMOM, is capable of diagnosing realistic seasonal amplitudes of LAI by assimilating localised information on leaf lifespan coupled with summer-only LAI_EO (i.e., the alternative (ALT) approach). Important differences arise from the BAU and ALT experiments. The BAU experiment showed highly seasonal canopy dynamics and diagnostic leaf traits erroneously consistent with deciduous species. Conversely, the ALT experiment displayed canopy dynamics and functional characteristics more reflective of evergreen needleleaf species. For BAU, biases in LAI_EO propagated throughout the carbon cycle, especially in the southern, more productive sites. This investigation highlights the need for improved LAI_EO estimates in northern evergreen forests to enhance understanding of carbon cycle processes in this region of rapid warming and large carbon stores, and provides a mechanism for improvement using independent leaf trait data.

Leaf area index (LAI) is a key biophysical variable which quantifies the surface area for light capture and photosynthetic activity per unit ground area, giving a first order constraint on potential photosynthesis. LAI is tightly coupled to the carbon, energy, and water cycles of the global terrestrial system. Numerous Earth observation (EO) products provide estimates of LAI over time (LAI_EO), delivering valuable information on plant phenology and canopy dynamics. Widely-used LAI_EO, however, consistently exhibit unrealistic seasonality in evergreen needleleaf forests at the northern latitudes. Taking a model-data fusion approach, we show that naïvely assimilating biased, whole-year LAI_EO (i.e., the business-as-usual (BAU) approach) at three well-studied evergreen needleleaf forests in Fennoscandia implies an ecosystem carbon cycle which is unrealistic and inconsistent with independent lines of evidence. We further demonstrate that the model-data fusion framework, CARDAMOM, is capable of diagnosing realistic seasonal amplitudes of LAI by assimilating localised information on leaf lifespan coupled with summer-only LAI_EO (i.e., the alternative (ALT) approach). Important differences arise from the BAU and ALT experiments. The BAU experiment showed highly seasonal canopy dynamics and diagnostic leaf traits erroneously consistent with deciduous species. Conversely, the ALT experiment displayed canopy dynamics and functional characteristics more reflective of evergreen needleleaf species. For BAU, biases in LAI_EO propagated throughout the carbon cycle, especially in the southern, more productive sites. This investigation highlights the need for improved LAI_EO estimates in northern evergreen forests to enhance understanding of carbon cycle processes in this region of rapid warming and large carbon stores, and provides a mechanism for improvement using independent leaf trait data.

Leaf area index (LAI) is a key biophysical variable which quantifies the surface area for light capture and photosynthetic activity per unit ground area, giving a first order constraint on potential photosynthesis. LAI is tightly coupled to the carbon, energy, and water cycles of the global terrestrial system. Numerous Earth observation (EO) products provide estimates of LAI over time (LAI_EO), delivering valuable information on plant phenology and canopy dynamics. Widely-used LAI_EO, however, consistently exhibit unrealistic seasonality in evergreen needleleaf forests at the northern latitudes. Taking a model-data fusion approach, we show that naïvely assimilating biased, whole-year LAI_EO (i.e., the business-as-usual (BAU) approach) at three well-studied evergreen needleleaf forests in Fennoscandia implies an ecosystem carbon cycle which is unrealistic and inconsistent with independent lines of evidence. We further demonstrate that the model-data fusion framework, CARDAMOM, is capable of diagnosing realistic seasonal amplitudes of LAI by assimilating localised information on leaf lifespan coupled with summer-only LAI_EO (i.e., the alternative (ALT) approach). Important differences arise from the BAU and ALT experiments. The BAU experiment showed highly seasonal canopy dynamics and diagnostic leaf traits erroneously consistent with deciduous species. Conversely, the ALT experiment displayed canopy dynamics and functional characteristics more reflective of evergreen needleleaf species. For BAU, biases in LAI_EO propagated throughout the carbon cycle, especially in the southern, more productive sites. This investigation highlights the need for improved LAI_EO estimates in northern evergreen forests to enhance understanding of carbon cycle processes in this region of rapid warming and large carbon stores, and provides a mechanism for improvement using independent leaf trait data.

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.

Advances in Earth observation capabilities mean that there is now a multitude of spatially resolved data sets available that can support the quantification of water and carbon pools and fluxes at the land surface. However, such quantification ideally requires efficient synergistic exploitation of those data, which in turn requires carbon and water land-surface models with the capability to simultaneously assimilate several such data streams. The present article discusses the requirements for such a model and presents one such model based on the combination of the existing Data Assimilation Linked Ecosystem Carbon (DALEC) land vegetation carbon cycle model with the Biosphere Energy-Transfer HYdrology (BETHY) land-surface and terrestrial vegetation scheme. The resulting D&B model, made available as a community model, is presented together with a comprehensive evaluation for two selected study sites of widely varying climate. We then demonstrate the concept of land-surface modelling aided by data streams that are available from satellite remote sensing. Here we present D&B with four observation operators that translate model-derived variables into measurements available from such data streams, namely fraction of photosynthetically active radiation (FAPAR), solar-induced chlorophyll fluorescence (SIF), vegetation optical depth (VOD) at microwave frequencies and near-surface soil moisture (also available from microwave measurements). As a first step, we evaluate the combined model system using local observations and finally discuss the potential of the system presented for multi-stream data assimilation in the context of Earth observation systems.