Leaf chlorophyll and nitrogen dynamics and their relationship to lowland rice yield for site-specific paddy management

Gholizadeh, Asa; Saberioon, Mohammadmehdi; Borůvka, Luboš; Wayayok, Aimrun; Soom, Mohd Amin Mohd

doi:10.1016/j.inpa.2017.08.002

Cited by 72 publications

(51 citation statements)

References 35 publications

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“…This should enable to better represent the covariations of V cmax and leaf chlorophyll content, and hence the impact of C ab on the SIF signal. Indeed, leaf chlorophyll content is related to leaf nitrogen content (Gholizadeh et al, ; Yoder & Pettigrew‐Crosby, ) with a dependence that may however vary depending on the season, species, and allocation of nitrogen for different plant processes (Croft et al, ; Ghimire et al, ; Kalacska et al, ). In parallel, a more direct relationship between C ab and V cmax shall be implemented to represent the dynamic of leaf chlorophyll content (Croft et al, ; Houborg et al, ; Migliavacca et al, ).…”

Section: Discussionmentioning

confidence: 99%

Improving Estimates of Gross Primary Productivity by Assimilating Solar‐Induced Fluorescence Satellite Retrievals in a Terrestrial Biosphere Model Using a Process‐Based SIF Model

et al. 2019

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Over the last few years, solar-induced chlorophyll fluorescence (SIF) observations from space have emerged as a promising resource for evaluating the spatio-temporal distribution of gross primary productivity (GPP) simulated by global terrestrial biosphere models. SIF can be used to improve GPP simulations by optimizing critical model parameters through statistical Bayesian data assimilation techniques. A prerequisite is the availability of a functional link between GPP and SIF in terrestrial biosphere models. Here we present the development of a mechanistic SIF observation operator in the ORCHIDEE (Organizing Carbon and Hydrology In Dynamic Ecosystems) terrestrial biosphere model. It simulates the regulation of photosystem II fluorescence quantum yield at the leaf level thanks to a novel parameterization of non-photochemical quenching as a function of temperature, photosynthetically active radiation, and normalized quantum yield of photochemistry. It emulates the radiative transfer of chlorophyll fluorescence to the top of the canopy using a parametric simplification of the SCOPE (Soil Canopy Observation Photosynthesis Energy) model. We assimilate two years of monthly OCO-2 (Orbiting Carbon Observatory-2) SIF product at 0.5°(2015-2016) to optimize ORCHIDEE photosynthesis and phenological parameters over an ensemble of grid points for all plant functional types. The impact on the simulated GPP is considerable with a large decrease of the global scale budget by 28 GtC/year over the period 1990-2009. The optimized GPP budget (134/136 GtC/year over ) remarkably agrees with independent GPP estimates, FLUXSAT (137 GtC/year over 2001) in particular and FLUXCOM (121 GtC/year over 1990. Our results also suggest a biome dependency of the SIF-GPP relationship that needs to be improved for some plant functional types.

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Section: Discussionmentioning

confidence: 99%

Improving Estimates of Gross Primary Productivity by Assimilating Solar‐Induced Fluorescence Satellite Retrievals in a Terrestrial Biosphere Model Using a Process‐Based SIF Model

et al. 2019

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“…The Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) enzyme, which relates to V max and leaf-level carbon fixation, correlates to leaf nitrogen (N) content [11]. Since leaf N also consists of chlorophyll, plant chlorophyll is strongly correlated to leaf N [12][13][14] including that for winter wheat [15].LAI and chlorophyll content are important factors determining crop reflectance [11] and can, therefore, be estimated from optical Earth observation satellite sensors, which provide synoptic and repetitive coverages over large areas [16]. The retrieval of these variables from Earth observation multispectral data has extensively been carried out empirically through the statistical relationship between spectral vegetation indices (VI), typically the Normalised Difference Vegetation Index [17], to ground measurements.…”

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confidence: 99%

The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development

et al. 2019

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Leaf Area Index (LAI) and chlorophyll content are strongly related to plant development and productivity. Spatial and temporal estimates of these variables are essential for efficient and precise crop management. The availability of open-access data from the European Space Agency's (ESA) Sentinel-2 satellite-delivering global coverage with an average 5-day revisit frequency at a spatial resolution of up to 10 metres-could provide estimates of these variables at unprecedented (i.e., sub-field) resolution. Using synthetic data, past research has demonstrated the potential of Sentinel-2 for estimating crop variables. Nonetheless, research involving a robust analysis of the Sentinel-2 bands for supporting agricultural applications is limited. We evaluated the potential of Sentinel-2 data for retrieving winter wheat LAI, leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC). In coordination with destructive and non-destructive ground measurements, we acquired multispectral data from an Unmanned Aerial Vehicle (UAV)-mounted sensor measuring key Sentinel-2 spectral bands (443 to 865 nm). We applied Gaussian processes regression (GPR) machine learning to determine the most informative Sentinel-2 bands for retrieving each of the variables. We further evaluated the GPR model performance when propagating observation uncertainty. When applying the best-performing GPR models without propagating uncertainty, the retrievals had a high agreement with ground measurements-the mean R 2 and normalised root-mean-square error (NRMSE) were 0.89 and 8.8%, respectively. When propagating uncertainty, the mean R 2 and NRMSE were 0.82 and 11.9%, respectively. When accounting for measurement uncertainty in the estimation of LAI and CCC, the number of most informative Sentinel-2 bands was reduced from four to only two-the red-edge (705 nm) and near-infrared (865 nm) bands. This research demonstrates the value of the Sentinel-2 spectral characteristics for retrieving critical variables that can support more sustainable crop management practices. the value of LAI data for updating state variables in process-based agroecosystem models in order to improve estimates of crop yield [3][4][5][6] and land-atmosphere carbon dioxide exchanges [7,8]. On the other hand, chlorophyll is a key driver of plant light absorption and conversion to chemical energy and is, therefore, an indicator of plant health and potential gross primary productivity [9,10]. In particular, leaf chlorophyll content is linked to leaf photosynthetic capacity via the maximum rate of carboxylation (V max ). The Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) enzyme, which relates to V max and leaf-level carbon fixation, correlates to leaf nitrogen (N) content [11]. Since leaf N also consists of chlorophyll, plant chlorophyll is strongly correlated to leaf N [12][13][14] including that for winter wheat [15].LAI and chlorophyll content are important factors determining crop reflectance [11] and can, therefore, be estimated from optical Earth observation satellit...

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“…The central tendency of collected data of each variable was measured by mean and median, while the variability was measured using variance, standard deviation, coefficient of variation (CV), and range. Evaluation of CV of the data was based on 3 categories, namely: low (less than 15%), medium (15-35%), and high (>35%) (Gholizadeh et al, 2017). Correlation between variables were tested by 2 tail Pearson Correlation Test with error probability less than 5% (P<0.05).…”

Section: Methodsmentioning

confidence: 99%

Evaluation of the Use of SPAD-502 Chlorophyll meter for non-destructive estimation of nitrogen status of tea leaf (Camellia sinensis L. Kuntze)

Wahono

Indradewa

Sunarminto

et al. 2019

IJARe

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Efficient nutrient management requires estimating factual fertilizer requirements. This study was aimed to test the use of chlorophyll meter SPAD-502 to estimate the nitrogen status of tea maintenance leaf. The test was carried out by correlating the SPAD readings with destructively measured leaf nitrogen content using samples oGbtained from nitrogen fertilizer dosage experiments. Observations were made at 15, 32, 45 and 62 days after the application of N fertilizer treatments. The results showed that the SPAD readings and total nitrogen leaf content correlated significantly with the time of observation. Estimation of leaf N content based on the SPAD readings follows linear line equation y = 0.0311x + 1.5856 with coefficient determinant (R²) = 0.62 significantly at P<0.01. It was concluded that SPAD-502 chlorophyll meter is reliable to assess the leaf nitrogen content of tea maintenance leaf and is adequate to predict future nitrogen fertilizer requirements.

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Leaf chlorophyll and nitrogen dynamics and their relationship to lowland rice yield for site-specific paddy management

Cited by 72 publications

References 35 publications

Improving Estimates of Gross Primary Productivity by Assimilating Solar‐Induced Fluorescence Satellite Retrievals in a Terrestrial Biosphere Model Using a Process‐Based SIF Model

Improving Estimates of Gross Primary Productivity by Assimilating Solar‐Induced Fluorescence Satellite Retrievals in a Terrestrial Biosphere Model Using a Process‐Based SIF Model

The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development

Evaluation of the Use of SPAD-502 Chlorophyll meter for non-destructive estimation of nitrogen status of tea leaf (Camellia sinensis L. Kuntze)

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