Relationships between soil respiration and photosynthesis-related spectral vegetation indices in two cropland ecosystems

Huang, Ni; Niu, Zheng; Zhan, Yulin; Xu, Shan; Tappert, Michelle C.; Wu, Chaoyang; Huang, Wenjiang; Gao, Shuai; Hou, Xiudan; Cai, Dewen

doi:10.1016/j.agrformet.2012.03.005

Cited by 57 publications

(46 citation statements)

References 71 publications

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“…Root exudates are characteristic of low molecular weight and labile organic matter, which serve as the substrates for R rhi and can be rapidly respired by microorganisms in the rhizosphere. This is because available substances are the actual contributors to microbial activity (Kuzyakov and Gavrichkova, 2010), and thus rhizosphere microorganisms can be activated rapidly in the presence of root exudates (Chapin et al, 2011;Huang et al, 2012). Some studies have reported that there is a time lag between GPP and respiration response from the soil.…”

Section: Biological Basis Of the Rersmmentioning

confidence: 99%

“…Because the ReRSM was conducted at the temporal resolution of 8-day average, these kinds of time lag may introduce negligible error into the ReRSMestimated R e . Although the studies sometimes observed a link between GPP-derived respiration and temperature (Moyano et al, 2007(Moyano et al, , 2008, it is to a large extent due to the good relationship between GPP and temperature (Amthor, 2000;Chapin et al, 2011;Kuzyakov and Gavrichkova, 2010;Huang et al, 2012).…”

Section: Biological Basis Of the Rersmmentioning

confidence: 99%

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A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau

Gao

et al. 2015

Ecological Modelling

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a b s t r a c tEcosystem respiration (R e ) is rarely quantified from remote sensing data because satellite technique is incapable of observing the key processes associated with soil respiration. In this study, we develop a Remote Sensing Model for R e (ReRSM) by assuming that one part of R e is derived from current photosynthate with the respiratory rate coupling closely with gross primary production (GPP), and the other part of R e is derived from reserved ecosystem organic matter (including plant biomass, plant residues and soil organic matter) with the respiratory rate responding strongly to temperature change. The ReRSM is solely driven by the Enhanced Vegetation Index (EVI), the Land Surface Water Index (LSWI) and the Land Surface Temperature (LST) from MODIS data. Multi-year eddy CO 2 flux data of five vegetation types in Northern China and the Tibetan Plateau (including temperate mixed forest, temperate steppe, alpine shrubland, alpine marsh and alpine meadow-steppe) were used for model parameterization and validation. In most cases, the simulated R e agreed well with the observed R e in terms of seasonal and interannual variation irrespective of vegetation types. The ReRSM could explain approximately 93% of the variation in the observed R e across five vegetation types, with the root mean square error (RMSE) of 0.04 mol C m −2 d −1 and the modeling efficiency (EF) of 0.93. Model comparison showed that the performance of the ReRSM was comparable with that of the RECO in the studied five vegetation types, while the former had much fewer parameters than the latter. The ReRSM parameters showed good linear relationships with the mean annual satellite indices. With these linear functions, the ReRSM could explain approximately 90% of the variation in the observed R e across five vegetation types, with the RMSE of 0.05 mol C m −2 d −1 and the EF of 0.89. These analyses indicated that the ReRSM is a simple and alternative approach in R e estimation and has the potential of estimating spatial R e . However, the performance of ReRSM in other vegetation types or regions still needs a further study.

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Section: Biological Basis Of the Rersmmentioning

confidence: 99%

Section: Biological Basis Of the Rersmmentioning

confidence: 99%

A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau

Gao

et al. 2015

Ecological Modelling

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“…In addition, NDVI is also used to assess N utilization efficiency and to predict crop yields, and N fertilizer requirements can be obtained via the N fertilization optimization algorithm (Bajwa et al, 2010). Indices involving these spectral bands can be used to estimate Chl levels in the leaves of various plant species (Huang et al, 2012). Inoue et al (1998) examined the relationship between N fertilizer applications and reflectance spectra in field corn and found that vegetation indices of R1100 and R660 had the highest correlations with N application, where NDVI is calculated as (R1100 -R660)/ (R1100 + R660).…”

Section: Discussionmentioning

confidence: 99%

Using Chlorophyll Fluorescence and Vegetation Indices to Predict the Timing of Nitrogen Demand in Pentas lanceolata

Wu¹,

Lin²,

Lee³

et al. 2015

HST

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The objective of this study was to predict the timing of nitrogen (N) demand through analyzing chlorophyll fluorescence (ChlF), soil-plant analysis development (SPAD), and normalized difference vegetation index (NDVI), which are positively correlated with foliar N concentration in star cluster (Pentas lanceolata). The plants were grown in potting soil under optimal conditions for 30 d, followed by weekly irrigation with five concentrations (0, 4, 8, 16, and 24 mM) of N for an additional 30 d. These five N application levels corresponded to leaf N concentrations of 2.62, 3.48, 4.00, 4.23, and 4.69%, respectively. We measured 13 morphological and physiological parameters, as well as the responses of these parameters to various N-fertilizer treatments. The general increases in Dickson's quality index (DQI), above-ground dry weight (DW), total DW, flowering rate, △F/Fm', and qP in response to treatment with 0 to 8 mM N were similar to those of SPAD, NDVI, and Fv/Fm. Consistent and strong correlations (R 2 = 0.60 to 0.85) were observed between leaf N concentration (%) and SPAD, NDVI, △F/Fm', and above-ground DW. Validation of leaf SPAD, NDVI, and △F/Fm' revealed that these vegetation indices are accurate predictors of leaf N concentration that can be used for non-destructive estimation of the proper timing for N-solution irrigation of P. lanceolata. Moreover, irrigation with 8 mM N-fertilizer is recommended when leaf N concentration, SPAD, NVDI, and △F/Fm' ratios are reduced from their saturation values of 4.00, 50.68, 0.64, and 0.137%, respectively. Additional key words:Dickson quality index (DQI), non-destructive, normalized differentiation vegetation index (NDVI), photosystem II (PSII), reflectance spectra

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“…All values and the coefficients have been optimized for the MODIS EVI product [80]. We therefore spectrally convolved APEX HCRF data to match the spectral resolution of respective MODIS bands and assumed validity of both gain factors (i.e., C 1 and C 2 ) for the higher spatial resolution of our data as demonstrated in [81][82][83]. However, the differences in spatial resolution (i.e., 2 m of APEX vs. 500 m of MODIS) require further consideration.…”

Section: Spatial Modeling Of Forest Productivity Using Apex Datamentioning

confidence: 99%

Tree Density and Forest Productivity in a Heterogeneous Alpine Environment: Insights from Airborne Laser Scanning and Imaging Spectroscopy

Fatehi

Damm

Leiterer

et al. 2017

Forests

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Abstract:We outline an approach combining airborne laser scanning (ALS) and imaging spectroscopy (IS) to quantify and assess patterns of tree density (TD) and forest productivity (FP) in a protected heterogeneous alpine forest in the Swiss National Park (SNP). We use ALS data and a local maxima (LM) approach to predict TD, as well as IS data (Airborne Prism Experiment-APEX) and an empirical model to estimate FP. We investigate the dependency of TD and FP on site related factors, in particular on surface exposition and elevation. Based on reference data (i.e., 1598 trees measured in 35 field plots), we observed an underestimation of ALS-based TD estimates of 40%. Our results suggest a limited sensitivity of the ALS approach to small trees as well as a dependency of TD estimates on canopy heterogeneity, structure, and species composition. We found a weak to moderate relationship between surface elevation and TD (R 2 = 0.18-0.69) and a less pronounced trend with FP (R 2 = 0.0-0.56), suggesting that both variables depend on gradients of resource availability. Further to the limitations faced in the sensitivity of the applied approaches, we conclude that the combined application of ALS and IS data was convenient for estimating tree density and mapping FP in north-facing forested areas, however, the accuracy was lower in south-facing forested areas covered with multi-stemmed trees.

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Relationships between soil respiration and photosynthesis-related spectral vegetation indices in two cropland ecosystems

Cited by 57 publications

References 71 publications

A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau

A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau

Using Chlorophyll Fluorescence and Vegetation Indices to Predict the Timing of Nitrogen Demand in Pentas lanceolata

Tree Density and Forest Productivity in a Heterogeneous Alpine Environment: Insights from Airborne Laser Scanning and Imaging Spectroscopy

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