Constraining water limitation of photosynthesis in a crop growth model with sun-induced chlorophyll fluorescence

Cannière, Simon De; Herbst, Michael; Vereecken, Harry; Defourny, Pierre; Jonard, François

doi:10.1016/j.rse.2021.112722

Cited by 19 publications

(12 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ΦF declined under drought stress, and the structural component barely showed a reaction. Their results indicated that ΦF was an indicator for instantaneous stress conditions, and it offered enough information to correct crop growth models under water stress [47]. In addition, the total emitted SIF (SIFtot) was calculated as the result of dividing SIF by fesc, and hence SIFtot contained more physiological information compared with SIF.…”

Section: B Differences In Physiological and Non-physiological Informa...mentioning

confidence: 99%

Exploring Physiological and Nonphysiological Responses of Sun-Induced Chlorophyll Fluorescence to Different Levels of Water Stress in Winter Wheat

Lin,

Zhou,

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Sun-induced chlorophyll fluorescence (SIF) has recently been used for the early detection of plant stress. The response of SIF to water stress is complicated by the combination of physiological and non-physiological dynamics in SIF variations and has not been well explored. This paper aims to explore the physiological and non-physiological responses of SIF under different levels of water stress, by analyzing the data from winter wheat with four irrigation treatments (well-watered, moderate, severe, and extreme water stresses). Here the near-infrared radiance of vegetation (NIRvR) and the canopy near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRvP) were selected to represent the non-physiological information of SIF. Then the physiological information (fluorescence yield, ΦF) was extracted by dividing SIF by NIRvP or NIRvR. The results indicate that the physiological and nonphysiological components of SIF were effective indicators for water stress detection. Compared to NDVI, the physiological and non-physiological components exhibited faster responses to different levels of water stress. The non-physiological component explored 68%-77% of SIF under different levels of water stress, and its contributions were firstly decreased and then increased as water stress increased. The stress-induced physiological decrease explored ~50% of SIF decrease under severe water stress and exceeded the non-physiological decrease at the onset of extreme water stress. This study provides direct insights into the physiological and non-physiological dynamics of SIF under different levels of water stress, which contribute to improving the detection of plant stress using SIF.

show abstract

Section: B Differences In Physiological and Non-physiological Informa...mentioning

confidence: 99%

Exploring Physiological and Nonphysiological Responses of Sun-Induced Chlorophyll Fluorescence to Different Levels of Water Stress in Winter Wheat

Lin,

Zhou,

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Such time scale‐dependent SIF response to stress was also evident in Damm et al (2022), which identified a nonlinear response of far‐red SIF (from the airborne HyPlant measurements) to soil moisture deficit in a controlled water experiment for corn, that is, an initial brief increase followed by a subsequent decrease. Unfortunately, such complex SIF‐NPQ‐GPP dynamics under water/heat stress has not been adequately incorporated by the state‐of‐the‐art mechanistic models, for example, SCOPE (Martini et al, 2022; Wohlfahrt et al, 2018), although De Cannière et al (2021) reported improved water and carbon fluxes simulations during water stress if SIF was utilized to constrain the water stress functions in these models. Resolving these discrepancies requires improved theoretical understandings of underlying mechanisms (e.g., stress‐ avoiding or adaptation strategies, Flexas & Medrano, 2002; Rascher et al, 2004) and modeling of such understanding across stress types, severity, and duration (discussion in Sun et al, 2023; Section 3.1.1 above), as well as improved SIF data quality and consistency (Section 4.2).…”

Section: Applicationsmentioning

confidence: 99%

From remotely‐sensed solar‐induced chlorophyll fluorescence to ecosystem structure, function, and service: Part II—Harnessing data

Sun

Wen

et al. 2023

Global Change Biology

View full text Add to dashboard Cite

This paper aims to (1) synthesize the variety, scale, and uncertainty of existing solar-induced chlorophyll fluorescence datasets, (2) synthesize the diverse applications in the sector of ecology, agriculture, hydrology, climate, and socioeconomics, and (3) clarify how such data inconsistency superimposed with the theoretical complexities may impact process interpretation of various applications and contribute to inconsistent findings. We offer our perspectives on innovations needed to help improve informing ecosystem structure, function, and service under climate change.

show abstract

“…For instance, LAI data assimilation of field observations (Tewes et al 2020a) or remotely sensed canopy state variables (Tewes et al 2020b) into agroecosystem models can help to improve model performance. Remote sensing technologies can also help to further understand and estimate biotic (Dutta et al 2008;Yuan et al 2017) and abiotic stresses (De Canniere et al 2021;Liu et al 2019). The addition of soil property input data (soil texture, hydraulic properties), which can be challenging to physically collect in the field.…”

Section: Agriculture 40mentioning

confidence: 99%

Model-based design of crop diversification through new field arrangements in spatially heterogeneous landscapes. A review

Hernández-Ochoa

Gaiser

Kersebaum

et al. 2022

Agron. Sustain. Dev.

View full text Add to dashboard Cite

Intensive agriculture in Germany is not only highly productive but has also led to detrimental effects in the environment. Crop diversification together with new field arrangements considering soil heterogeneities can be an alternative to improve resource use efficiency (RUE), ecosystem services (ESS), and biodiversity. Agroecosystem models are tools that help us to understand and design diversified new field arrangements. The main goal of this study was to review the extent to which agroecosystem models have been used for crop diversification design at field and landscape scale by considering soil heterogeneities and to understand the model requirements for this purpose. We found several agroecosystem models available for simulating spatiotemporal crop diversification at the field scale. For spatial crop diversification, simplified modelling approaches consider crop interactions for light, water, and nutrients, but they offer restricted crop combinations. For temporal crop diversification, agroecosystem models include the major crops (e.g., cereals, legumes, and tuber crops). However, crop parameterization is limited for marginal crops and soil carbon and nitrogen (N). At the landscape scale, decision-making frameworks are commonly used to design diversified cropping systems. Within-field soil heterogeneities are rarely considered in field or landscape design studies. Combining static frameworks with dynamic agroecosystems models can be useful for the design and evaluation of trade-offs for ESS delivery and biodiversity. To enhance modeling capabilities to simulate diversified cropping systems in new field arrangements, it will be necessary to improve the representation of crop interactions, the inclusion of more crop species options, soil legacy effects, and biodiversity estimations. Newly diversified field arrangement design also requires higher data resolution, which can be generated via remote sensing and field sensors. We propose the implementation of a framework that combines static approaches and process-based models for new optimized field arrangement design and propose respective experiments for testing the combined framework.

show abstract

Constraining water limitation of photosynthesis in a crop growth model with sun-induced chlorophyll fluorescence

Cited by 19 publications

References 58 publications

Exploring Physiological and Nonphysiological Responses of Sun-Induced Chlorophyll Fluorescence to Different Levels of Water Stress in Winter Wheat

Exploring Physiological and Nonphysiological Responses of Sun-Induced Chlorophyll Fluorescence to Different Levels of Water Stress in Winter Wheat

From remotely‐sensed solar‐induced chlorophyll fluorescence to ecosystem structure, function, and service: Part II—Harnessing data

Model-based design of crop diversification through new field arrangements in spatially heterogeneous landscapes. A review

Contact Info

Product

Resources

About