Annual and seasonal variations in gross primary productivity across the agro-climatic regions in India

Varghese, Roma

doi:10.1007/s10661-019-7796-2

Cited by 11 publications

(5 citation statements)

References 68 publications

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“…The CNRM1‐ESM2‐1 and IPSL‐CM6A‐LR are the two models that rank precipitation higher than temperature as an important feature. For the region covering the Indian subcontinent (SAS), precipitation is considered most important in the UKESM1‐0‐LL and CanESM5 models, consistent with previous studies (Varghese & Behera, 2019; Verma et al., 2022) while all three other models favor temperature as the key factor. In East Asia (EAS) temperature is considered the most important driver for GPP followed by precipitation and radiation in some regions (Bo et al., 2022; Yao et al., 2018) and all models except UKESM1‐0‐LL (precipitation) are in agreement.…”

Section: Resultssupporting

confidence: 87%

A Machine Learning Framework to Evaluate Vegetation Modeling in Earth System Models

Swaminathan,

Quaife,

Allan

2024

J Adv Model Earth Syst

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Vegetation gross primary productivity (GPP) is the single largest carbon flux of the terrestrial biosphere which, in turn, is responsible for sequestering 25%–30% of anthropogenic carbon dioxide emissions. The ability to model GPP is therefore critical for calculating carbon budgets as well as understanding climate feedbacks. Earth system models (ESMs) have the capability to simulate GPP but vary greatly in their individual estimates, resulting in large uncertainties. We describe a machine learning (ML) approach to investigate two key factors responsible for differences in simulated GPP quantities from ESMs: the relative importance of different atmospheric drivers and differences in the representation of land surface processes. We describe the different steps in the development of our interpretable ML framework including the choice of algorithms, parameter tuning, training and evaluation. Our results show that ESMs largely agree on the physical climate drivers responsible for GPP as seen in the literature, for instance drought variables in the Mediterranean region or radiation and temperature in the Arctic region. However differences do exist since models don't necessarily agree on which individual variable is most relevant for GPP. We also explore a distance measure to attribute GPP differences to climate influences versus process differences and provide examples for where our methods work (South Asia, Mediterranean) and where they are inconclusive (Eastern North America).

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Section: Resultssupporting

confidence: 87%

A Machine Learning Framework to Evaluate Vegetation Modeling in Earth System Models

Swaminathan,

Quaife,

Allan

2024

J Adv Model Earth Syst

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“…As we discussed earlier the Indian region lacks sufficient ground measurement records, we have used NASA's Earth observing system (EOS) data for the model validation. This is a standard practice followed in other similar studies (Anav et al, 2015;Hu et al, 2022;Rao et al, 2019;Varghese & Behera, 2019). MODIS is one of the fundamental sensors on EOS satellites that provide widely accepted global GPP products.…”

Section: Moderate Resolution Imaging Spectroradiometer (Modis)mentioning

confidence: 99%

Investigating Terrestrial Carbon Uptake Over India Using Multimodel Simulations of Gross Primary Productivity and Satellite‐Based Biophysical Product

Uchale,

Deb Burman,

Tiwari

et al. 2023

JGR Biogeosciences

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Terrestrial ecosystems play a central role in the global carbon cycle and climate mitigation due to their offering of a large carbon sink. More than one‐fifth of the geographical area of India, one of the largest nations on the Earth, is forested, which is highly diverse in vegetation and climate types, offers huge potential for carbon sequestration, but remains vulnerable to climate change. Hence, it is imperative to know the future changes in the terrestrial carbon budget over this region. Gross primary productivity (GPP) represents the carbon uptake by terrestrial ecosystems. The multimodel ensembles of GPP simulated by the sixth phase of the Coupled Model Intercomparison Project (CMIP6) provide a useful means in this regard. In this work, we study the strength and variability of GPP over India in the near‐past and future using these simulations. In future, all the models show an increasing trend in GPP, however, with widely varying trends. The preferred month of carbon uptake differs among the models. A comparison with a satellite biophysical record shows the models underestimated the GPP during the near‐past over India. The carbon uptake in the Eastern Himalaya dominates the Western Himalaya and central Indian regions. Specifically, till 2100, the growth rate of GPP varies from 4.9 to 16.69 gC m−2 y−2, from 2.47 to 18.91 gC m−2 y−2, and from 0.32 to 21.95 gC m−2 y−2 over these three regions, respectively.

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“…Researchers have demonstrated the potential use of the red edge wavelength or red edge-based indices to estimate or predict the chlorophyll content of mangrove species or other vegetation types [10,39,40]. The vegetation indices are used as a proxy, statistically linked to field-measured biophysical characteristics and further used for spatio-temporal extrapolation [41]. Suitable vegetation indices employing spectral bands sensitive to chlorophyll pigmentation are widely used in evaluating leaf chlorophyll content, such as the Red Edge Chlorophyll Index (ReCI), Red Edge Normalised Vegetation Index (ReNDVI), and Pigment Specific Normalised Difference for chlorophyll a and b (PSNDa and PSNDb) (Table 1) [42][43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Species-Level Classification and Mapping of a Mangrove Forest Using Random Forest—Utilisation of AVIRIS-NG and Sentinel Data

et al. 2021

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Although studies on species-level classification and mapping using multisource data and machine learning approaches are plenty, the use of data with ideal placement of central wavelength and bandwidth at appropriate spatial resolution, for the classification of mangrove species is underreported. The species composition of a mangrove forest has been estimated utilising the red-edge spectral bands and chlorophyll absorption information from AVIRIS-NG and Sentinel-2 data. In this study, three dominant species, Heritiera fomes, Excoecaria agallocha and Avicennia officinalis, have been classified using the random forest (RF) model for a mangrove forest in Bhitarkanika Wildlife Sanctuary, India. Various combinations of reflectance/backscatter bands and vegetation indices derived from Sentinel-2, AVIRIS-NG, and Sentinel-1 were used for species-level discrimination and mapping. The RF model showed maximum accuracy using Sentinel-2, followed by the AVIRIS-NG, in discriminating three dominant species and two mixed compositions. This study indicates the potential of Sentinel-2 data for discriminating various mangrove species owing to the appropriate placement of central wavelength and bandwidth in Sentinel-2 at ≥10 m spatial resolution. The variable importance plots proved that species-level classification could be attempted using red edge and chlorophyll absorption information. This study has wider applicability in other mangrove forests around the world.

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Annual and seasonal variations in gross primary productivity across the agro-climatic regions in India

Cited by 11 publications

References 68 publications

A Machine Learning Framework to Evaluate Vegetation Modeling in Earth System Models

A Machine Learning Framework to Evaluate Vegetation Modeling in Earth System Models

Investigating Terrestrial Carbon Uptake Over India Using Multimodel Simulations of Gross Primary Productivity and Satellite‐Based Biophysical Product

Species-Level Classification and Mapping of a Mangrove Forest Using Random Forest—Utilisation of AVIRIS-NG and Sentinel Data

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