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Vegetation Carbon Dynamics and Climate


1. Vegetation and its role in atmosphere-land interaction.

The atmosphere–land interactions is crucial to the climate and earth system through the exchange of energy, water, momentum and carbon among vegetation and atmosphere. Vegetation dynamics plays a significant role in regulating the climate system through these fluxes between land and atmosphere (Friedl et al., 2002; Fuchs et al., 2015; Fuchs et al., 2016; IPCC, 2019). However, in recent times, a great deal of variability in anthropogenic land use along with climate variability has greatly altered the terrestrial biosphere all around the globe (Zhu et al., 2016; Chen et al., 2019; IPCC, 2019). Futhermore, changes in air quality alters the surface solar radiation and affects the photosynthetic activity and vegetation (Niyogi et al., 2004; Cirino et al., 2014; Ezhova et al., 2018; Zhang et al., 2021). Changes in the fluxes of momentum, water and energy in the earth system among land surface and atmosphere in the recent decades, has led to significant variability in the vegetation and carbon cycle dynamics (Dan and Jinjun, 2007; Tagliabue et al., 2019).

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2. Vegetation dynamics and vegetation-climate interaction

Climate change is one of the most discussed topics of current scientific research, and changes in terrestrial vegetation serve a key indicator for climate change. Land use change and climate variability have been affecting the terrestrial biosphere through changing the energy balance for the past few decades (Tucker et al. 2001).The global change in the terrestrial ecosystems is highly influenced by vegetation dynamics, which affects the local and regional climate (Eugster et al. 2000; Suzuki et al. 2007). Monitoring vegetation change is of great significance because of the unprecedented rise of human population, exorbitant resource consumption and accelerated environmental degradation (Zhu et al. 2016). Climate has a very strong relationship with terrestrial ecosystem and carbon cycle (Melillo et al., 1993; Ito and Oikawa, 2000; Bala et al., 2013).

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The assessment of the climate feedback mechanism and vegetation growth pattern is often utilized to monitor the forests, grasslands, and agriculture, and long-term trends such as “declining or browning” and “increasing or greening” have been deployed to analyse inter-annual variations ofvegetation productivity (Mabuchi et al., 2005; de Jong et al., 2012; Wang et. al; 2017; Zhu et al., 2016). The vegetation indices such as normalized difference vegetation index (NDVI) and leaf area index (LAI) and their trends typically reflect trends in photosynthetic activity of land-surface vegetation (Parida et al., 2021). Therefore, NDVI and LAI trends are used as a proxy to study photosynthetic trends (Konwar et al., 2012). Numerous vegetation indices have been developed in the past decade, but NDVI is one of the most widely used for analysing vegetation trends (Mabuchi et al., 2005; Parida et al., 2020). NDVI is commonly employed to monitor vegetation growth and is also known as the indicator of plant photosynthetic activity measurement (Myneni et al., 1997). NDVI is related to the plant’s structural and chemical properties, which include chlorophyll content, foliar nitrogen, LAI, green biomass, and productivity (Huete et al., 2002; Lyapustin et al., 2014; Ranjan and Parida, 2019, 2020). Gradual or abrupt or, more rarely, nonexistent, the shift from one to another was computed using the breakpoints (change points) in NDVI and LAI trends (Sarmah et al., 2018) to analyze changes in vegetation growth pattern in response to climatic factors (Parida et al., 2020).

3. Vegetation and Carbon Cycle

From atmosphere to ocean to soils and even in the Earth’s crust, carbon is distributed everywhere. The terrestrial biosphere includes carbon in plants, both dead and alive, animals, soil and the microorganisms. The carbon cycle plays a key role in regulating the earth’s climate by controlling the concentration of carbon dioxide in the atmosphere. Carbon dioxide is one of the main greenhouse gases, contributing to the global warming. Primary productivity is a key component of carbon cycle and are important to understand sink and source capacity of the terrestrial ecosystems.

A basic parameter used for estimation of the fundamental variable in estimating vegetation carbon cycling and crop yield is GPP (Ballantyne et al., 2012). It is defined as the rate at which vegetation captures and stores carbon dioxide in a given period of time through the photosynthetic process (Roxburgh et al. 2005). NPP is defined as the difference between GPP and autotrophic respiration (Ra), and is often measured as net production or accumulation of dry matter in vegetation in a year (Roxburgh et al. 2005). The amount of dry organic material produced by ecosystem’s green plants in unit time and area is NPP. The feedback between terrestrial ecosystems and atmosphere is based on NPP, which is a key variable for the global carbon cycle estimates. The ability of plants to absorb atmospheric carbon dioxide is reflected by NPP (Cramer et al., 1999; Dan et al., 2007). Terrestrial ecosystem carbon sink estimates, natural resource management and ecological studies are impacted by the spatio-temporal variability of productivity such as GPP and NPP (Cao et al., 2004). Carbon use efficiency (CUE) is a metric, which is the measure of the ability of plant to sequester atmospheric carbon in an ecosystem at any point of time and is estimated as the ratio of Net primary productivity (NPP) to Gross primary productivity (GPP) (De Lucia et al., 2007; He et al., 2018; Gang et al., 2022).

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4. Role of Remote Sensing in studying vegetation dynamics

Satellite remote sensing offers a unique and unmatched platform for earth observations across domains of time with great benefits for nature as well as mankind (Crowther et al., 2015; Parida and Mandal, 2020). Satellite remote sensing has been widely utilized for synoptic monitoring of biosphere functioning with global coverage and monitoring inter-annual and intra-seasonal vegetation activity (Myneni et al., 1997). There are several global NDVI datasets available including Advanced Very High-Resolution Radiometer (AVHRR); Generation of Global Inventory Modelling and Mapping Studies, Third Version (GIMMS3g); Land Term Data Record, Fourth Version (LTDR4), moderate resolution imaging spectroradiometer (MODIS), and many others. An evaluation study on all the NDVI datasets showed that except MODIS-based NDVI datasets, all the other NDVI datasets were compromised by temporal inconsistency for long-term trend analysis because of sensor differences and sensor shifts among the platforms (Hayes et al., 2013; Godfray et al., 2010). The time series analysis of satellite data acts as a potential system to monitor vegetation repeatably, revealing climate as well as anthropogenic changes causing a great deal of variability (Kuenzer et al., 2015; Becker-Reshef et al., 2010). The initial step for assessing the environmental impact of these changes is detecting changes within the time series (Wood et al., 2011).

Direct measurement of GPP based on instrument at landscape, ecosystem and canopy is still a challenging task. Quantification of global GPP highly relies on remote sensing measurements from space (Ballantyne et al., 2012; Garbulsky et al., 2014; Tagliabue et al., 2019).  The vegetation indices such as the SR (simple ratio), NDVI (normalized difference vegetation index), and some red edge-based vegetation indices are good for detection of chlorophyll content. (Gitelson et al., 2006). Sakamoto et al. (2011). The product of the solar radiation (including SWR and PAR) and canopy chlorophyll content/vegetation indices (VIs) sensitive to the chlorophyll content was used for GPP computation. (Gitelson et al., 2006, 2008; Peng et al., 2011). During the process of photosynthesis, vegetation emits a signal called solar-induced chlorophyll fluorescence (SIF) which has come up as a good proxy for GPP. (Guanter et al., 2014; Gu et al., 2019).  Being a very weak signal, accurate and high-resolution products of SIF is still very limited. Some studies show a strong relationship between GPP and the photosynthetically active radiation absorbed by the leaf chlorophyll in crops (Gitelson et al., 2003, 2006, 2016). Quantification of global GPP relies on remote sensing technology. (Ballantyne et al., 2012; Garbulsky et al., 2014; Ryu et al., 2019). LUE model approach is the most commonly used approach for GPP computation based on remote sensing approach (Monteith, 1972 & 1977). LUE is the rate at which absorbed radiation is converted into dry matter. It can be estimated with various methods: a constant conversion efficiency or the product of a maximum (optimum) constant adjusted by environmental stress scalars (Ruimy et al. 1999). LUE uses FAPAR is the fraction of PAR absorbed by vegetation; APAR, defined as the product of PAR and FAPAR, is the plant-absorbed PAR; and LUE is the light-use efficiency calculated using the ratio of GPP to APAR.

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5. Changing climate and Food Security- current scenario

In the recent times, higher temperature and evapotranspiration accompanied by insufficient precipitation has depleted soil moisture (Li et al., 2017; Won et al., 2022). The scarcity of moisture adversely affects the photosynthesis of the vegetation hampering the croplands and forests (Lesk et al., 2016; Korgan et al., 2019; West et al., 2019). The croplands and forests in south Asia, today are the most vulnerable ecosystems to the recent climate change (Watson et al., 2013; IPCC, 2019). The vegetated land comprising of croplands and forests accounts for around 80% of geographical area of India. Croplands accounts for about 55% and forests about 24.56% the vegetated land in India (Konwar et al., 2012). Among the drivers of food insecurity, climate change and land degradation are crucial (Mabuchi et al., 2005).

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Agriculture is the prominent source of income in the subcontinent (Gamon et al., 1995). Therefore, in recent times, climate driven changes in croplands and forest ecosystems are emerging as a serious issue in the subcontinent. The changes in surface greenness is used as a proxy for monitoring vegetation growth and terrestrial productivity and trends such as “declining or browning” and “increasing or greening” have extensively used, including assessment of the climate feedback mechanisms (Mabuchi et al., 2005; de Jong et al., 2011; Zhu et al., 2016; Wang et. al; 2017). Some studies report greening trends in Asia dominated by China and India due to changes in climatic conditions, agricultural practices and land management as reported by Kashyap et al. (2022), Parida et al. (2020), and Chen et al. (2019).

The unprecedented growth in human population has led to the scarcity of food grains production and water facilities in the Indian subcontinent which is a matter of immense concern (Myneni et al., 1995; Tian et al., 2015; Praveen 2017; Krishnan et al. 2020). India is experiencing substantial changes in the croplands and forests (Samrah et al., 2018; Chen et al., 2019; Parida et al., 2020). There is a need of better insights in understanding of changes in terrestrial vegetation, the various drivers, mechanisms involved and their implications for exchanges of energy, water and carbon between vegetation and atmosphere.

Important websites:


NASA Earth Observatory:


NASA: Global Climate Change:


NOAA: Global Monitoring Laboratory

Website for data:

MODIS Landcover product is available at:

MODIS NDVI product is available at:

MODIS GPP product is available at:

MODIS NPP product is available at:

MODIS EVI product is available at:

MODIS LAI product is available at:

GPM Level–3 Precipitation data is available at:

GLDAS Temperature data is available at:

GLDAS Soil Moisture data is available at:

GLDAS Soil Temperature data is available at:

FLDAS Soil Heat Flux data is available at:

IMD Precipitation and temperature data is available at:

Soil data is available at:

CMIP 6 GFDL data is available at:

Population Dynamics data is available at:


Important Papers:

Ambika AK, Mishra V. 2020. Substantial decline in atmospheric aridity due to irrigation in India. Environ Res Lett. 15(12):124060

Ambika AK, Wardlow B, Mishra V. 2016. Remotely sensed high resolution irrigated area mapping in India for 2000 to 2015. Sci Data. 3(1):1–14.

Chen C, Park T, Wang X, Piao S, Xu B, Chaturvedi RK, Fuchs R, Brovkin V, Ciais P, Fensholt R, et al. 2019. China and India lead in greening of the world through land-use management. Nat Sustain. 2: 122–129.

de Jong R, de Bruin S, de Wit A, Schaepman ME, Dent DL. 2011. Analysis of monotonic greening and browning trends from global NDVI time–series. Remote Sens Environ. 115(2):692–702.

Harris NL, Goldman E, Gabris C, Nordling J, Minnemeyer S, Ansari S, Lippmann M, Bennett L, Raad M, Hansen M, et al. 2017. Using spatial statistics to identify emerging hot spots of forest loss. Environ Res Lett. 12(2):024012.

Krishnan R, Sanjay J. Gnanaseelan C, Mujumdar M, Kulkarni A, Chakraborty S. 2020. Assessment of climate change over the Indian region: a report of the ministry of earth sciences (MOES), government of India. Springer Nature.

Mishra V, Ambika AK, Asoka A, Aadhar S, Buzan J, Kumar R, Huber M. 2020. Moist heat stress extremes in India enhanced by irrigation. Nat Geosci. 13(11):722–728.

Nemani R, Keeling C, Hashimoto H, Jolly W, Piper S, Tucker C, Myneni R, Running S. 2003. Climate driven increases in global terrestrial net primary production from 1982 to 1999. Science. 300(5625): 1560–1563.

Parida BR, Pandey AC, Patel NR. 2020. Greening and browning trends of vegetation in India and their responses to climate and non–climate drivers. Clim. 8(8):92.

Piao S, Liu Q, Chen A, Janssens IA, Fu Y, Dai J, Liu L, Lian XU, Shen M, Zhu X. 2019. Plant phenology and global climate change: Current progresses and challenges. Glob Change Biol. 25(6):1922–1940.

Revadekar JV, Tiwari YK, Kumar KR. 2012. Impact of climate variability on NDVI over the Indian region during 1981–2010. Int J Remote Sens. 33(22):7132–7150.

Sarmah S, Jia G, Zhang A. 2018. Satellite view of seasonal greenness trends and controls in South Asia. Environ Res Lett. 13(3):034026.

Wang X, Wang T, Liu D, Guo H, Huang H, Zhao Y. 2017. Moisture-induced greening of the South Asia over the past three decades. Glob Chang Biol. 23(11):4995–5005.

Zhu Z, Piao S, Myneni R, Huang M, Zeng Z, Canadell J, Ciais P, Sitch S, Friedlingstein P, Arneth A, et al. 2016. Greening of the Earth and its drivers. Nature Clim Change. 6(8):791–795.

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