An improved indicator of simulated grassland production based on MODIS NDVI and GPP data: A case study in the Sichuan province, China

Fu, Xiao‐An; Tang, Chuanjiang; Zhang, Xuxiao; Fu, Jingying; Jiang, Dong

doi:10.1016/j.ecolind.2014.01.015

Cited by 29 publications

(18 citation statements)

References 28 publications

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“…Because variations in the surface vegetation coverage affect the balance of regional ecosystems, studies on the variation in vegetation coverage is the basis of the protection of the ecological environment (Fan et al, 2012;Peng et al, 2012;Zhang et al, 2013). With wide coverage, high temporal resolution rate, and free data, sensors such as National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiameter (NOAA/AVHRR), Systeme Probatoire d'Observation dela Tarre/VEGETATION (SPOT/VGT), and moderate resolution imaging spectroradiometer (MODIS) can provide large amounts of data for monitoring the variations of vegetation coverage over long time periods (Rigina et al, 1996;Tucker et al, 2005;Ma et al, 2006;Fensholt et al, 2009Fensholt et al, , 2012aFu et al, 2014). NDVI is functionally correlated with leaf area index (LAI) and vegetation coverage (Baret et al, 1991;Gutman et al, 1998); the higher the NDVI, the larger the LAI, and the higher the vegetation coverage.…”

Section: Introductionmentioning

confidence: 98%

Spatio-temporal analysis of vegetation variation in the Yellow River Basin

Jiang

Yuan

Wang

et al. 2015

Ecological Indicators

355

169

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Section: Introductionmentioning

confidence: 98%

Spatio-temporal analysis of vegetation variation in the Yellow River Basin

Jiang

Yuan

Wang

et al. 2015

Ecological Indicators

355

169

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“…The work of Qian, et al [23], Fu, et al [34], Huang and Wen [25], and Liu, et al [35] attributed the reduction of light rain days over eastern China to increases in lower-tropospheric temperatures that were capable of one or more of the following: (1) increasing the dew-point temperature; (2) causing a rise in the condensation height of precipitable clouds and reducing cloudage; and (3) weakening atmospheric stability and strengthening upward motion such that precipitation intensity increased and light rain events decreased. Nevertheless, universal increases in lower-tropospheric temperature cannot explain increasing trends in light rain events over western China.…”

Section: Discussionmentioning

confidence: 99%

Opposite Trends in Light Rain Days over Western and Eastern China from 1960 to 2014

Song

Jing

2017

Atmosphere

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Abstract:In this work, we examined spatial and temporal trends for light rain days based on daily precipitation measurements, obtained from 1960 to 2014, from 590 meteorological stations in China. For the analyzed time interval, light rain days over eastern China were determined to decrease by 0.23 days·year −1 . In western China, they increased by 0.3 days·year −1 . To detect underlying causes for changes in light rain days, lower-tropospheric relative humidity was set as a proxy for light rain days. We then calculated the respective impacts of lower-tropospheric temperature and specific humidity on changes in light rain days. A comparison of the contributions of temperature and specific humidity resulted in the identification of the main cause of changes. Our results indicated that increases in lower-tropospheric temperatures reduced light rain days over the entire country, while variations in specific humidity dominated regional differences for light rain day trends.

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“…Grassland, defined as permanent vegetation of herbaceous plant communities, provides significant ecosystem services, carbon pooling, and forage production [1][2][3]. Aboveground biomass (AGB), which is defined as the total mass of plant material per unit area, is an important indicator of vegetation production.…”

Section: Introductionmentioning

confidence: 99%

“…Various methods have been developed for estimating grassland AGB from optical remote sensing data, and most of them are by nature empirical, and based on transfer functions between AGB and remote sensing observation [1,[8][9][10][11]. Transfer functions can be parametric (e.g., linear [2,12], exponential [8], and power fitting [5]) or non-parametric (e.g., support vector machine [13] and artificial neural network [14]). Field measurements are also indispensable in these empirical methods to calibrate the transfer functions.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Grassland Aboveground Biomass through Inversion of the PROSAIL Model with MODIS Imagery

Yin

et al. 2019

Remote Sensing

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The estimation of aboveground biomass (AGB), an important indicator of grassland production, is crucial for evaluating livestock carrying capacity, understanding the response and feedback to climate change, and achieving sustainable development. Most existing grassland AGB estimation studies were based on empirical methods, in which field measurements are indispensable, hindering their operational use. This study proposed a novel physically-based grassland AGB retrieval method through the inversion of PROSAIL model against MCD43A4 imagery. This method relies on the basic understanding that grassland is herbaceous, and therefore AGB can be represented as the product of leaf dry matter content (Cm) and leaf area index (LAI), i.e., AGB = Cm × LAI. First, the PROSAIL model was parameterized according to the literature regarding grassland parameters retrieval, then Cm and LAI were retrieved using a lookup table (LUT) algorithm, finally, the retrieved Cm and LAI were multiplied to obtain the AGB. The method was assessed in Zoige Plateau, China. Results show that it could reproduce the reference AGB map, which is generated by upscaling the field measurements, in terms of magnitude (with RMSE and R-RMSE of 60.06 g·m−2 and 18.1%, respectively) and spatial distribution. The estimated AGB time series also agreed reasonably well with the expected temporal dynamic trends of the grassland in our study area. The greatest advantage of our method is its fully physical nature, i.e., no field measurement is needed. Our method has the potential for operational monitoring of grassland AGB at regional and even larger scales.

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An improved indicator of simulated grassland production based on MODIS NDVI and GPP data: A case study in the Sichuan province, China

Cited by 29 publications

References 28 publications

Spatio-temporal analysis of vegetation variation in the Yellow River Basin

Spatio-temporal analysis of vegetation variation in the Yellow River Basin

Opposite Trends in Light Rain Days over Western and Eastern China from 1960 to 2014

Retrieval of Grassland Aboveground Biomass through Inversion of the PROSAIL Model with MODIS Imagery

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