Temporal relationships between daily precipitation and NDVI time series in Mexico

Colditz, René R.; Villanueva, Violeta Leticia Arriola; Tecuapetla-Gómez, Inder; Mendoza, Leticia Gómez

doi:10.1109/multi-temp.2017.8035202

Cited by 8 publications

(7 citation statements)

References 9 publications

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“…Thirdly, the seasonal and lagged effects of temperature and precipitation on the VEQ were not considered. However, there were differences in the seasonal and lagged response of VEQ to precipitation and temperature in China, which chiefly exhibited significant differences among disparate regions, terrains, and other factors [4,5]. The seasonality and lags of VEQ to climatic factors (e.g., temperature and precipitation) will be considered in the future.…”

Section: Impact Of Meteorological Factors On the Veq And Research Def...mentioning

confidence: 99%

“…The MOD09A1 was utilized to calculate the moisture indicator of terrestrial vegetation in China based on the improved MODIS tassel cap transformation [31]. The formula is as follows: WET = 0.1147ρ1 + 0.2489ρ2 + 0.2408ρ3 + 0.3132ρ4 − 0.3122ρ5 − 0.6416ρ6 − 0.5087ρ7 (5) where ρi…”

Section: Indicators Used In Veqimentioning

confidence: 99%

“…The ecological environment is composed of living and non-living organisms [3]. Eco-environmental quality assessment is used to quantify the regional quality of the ecological environment based on multiple selected indicators taking evaluation methods comprehensively [4,5]. However, the vast land area and complex terrain of China make it difficult to obtain data on species structure (e.g., uniformity and abundance of animal and microbial factors).…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Pattern of Vegetation Ecology Quality and Its Response to Climate Change between 2000–2017 in China

Luo

et al. 2021

Sustainability

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Vegetation ecology quality (VEQ) is an important indicator for evaluating environmental quality and ecosystem balance. The VEQ in China has changed significantly with global warming and gradual intensification of human activities. It is crucial to research the spatiotemporal characteristics of VEQ and its response to climate change in China. However, most previous studies used a single indicator to reflect VEQ in China, which needs to combine the effects of multiple indicators to reveal its variation characteristics. Based on the six remote sensing indicators, fractional vegetation cover, leaf area index, net primary productivity, vegetation wetness, land surface temperature, and water use efficiency of vegetation, the vegetation ecology quality index (VEQI) was constructed by principal component analysis in this paper. The spatio-temporal distribution and trend characteristic of VEQ within disparate ecosystems in China from 2000 to 2017 were studied. How continuous climate change affected VEQ over time was also analyzed. The results showed that the differences in spatial distribution between the excellent and poor VEQ regions were significant, with the proportion of excellent regions being much larger than that of poor regions. The VEQ has been ameliorated continuously during the past 18 years. Simultaneously, the VEQ would be ameliorated persistently in the future. Differences in the distribution and variation trend of VEQ occurred in disparate ecosystems. The VEQ of broadleaved forest was the best, while that of shrubs and arctic grassland ecosystem was the worst. The VEQ characteristics were different in disparate climate zones, with the best VEQ in the tropical monsoon climate zone and the worst in the plateau mountain climate zone. Except for desert vegetation and paddy field-dominated vegetation, VEQ of other ecosystems were significantly negatively correlated with altitude. Generally, moderate precipitation and temperature were favorable to improve VEQ in China. VEQ during the peak growing season was negatively correlated with temperature and positively correlated with precipitation, and the influence of precipitation on VEQ was stronger than that of temperature. Our results can be used to enact relevant management measures and policies.

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Section: Impact Of Meteorological Factors On the Veq And Research Def...mentioning

confidence: 99%

Section: Indicators Used In Veqimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Pattern of Vegetation Ecology Quality and Its Response to Climate Change between 2000–2017 in China

Luo

et al. 2021

Sustainability

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“…The commonly used environmental variables in regression models include elevation, slope, aspect, NDVI, landsurface temperature (LST), and geolocation (longitude and latitude) [6,23,27]. Although the vegetation index has been widely selected as the key independent variable in many downscaling studies, researchers also find that the response of vegetation to precipitation normally lags by 2-3 months that the lag effect behaves differently in various kind study areas [28]. Additionally, because vegetation growth is suppressed and promoted in some land covers types, such as water bodies, urban areas, and farmlands [29], vegetation data of these land covers must be excluded and masked when the vegetation index is adopted in research.…”

Section: Introductionmentioning

confidence: 99%

“…Residual correction is a necessary step of the hybrid downscaling method. Zhang et al [28] presented a regression model with a residual correction method. In the experiment, five interpolation methods (IDW, spline regularization, spline tension, ordinary Kriging, and simple Kriging) were applied to the residuals, and the best interpolation method was selected through cross-validation.…”

Section: Introductionmentioning

confidence: 99%

Spatial Downscaling Model Combined with the Geographically Weighted Regression and Multifractal Models for Monthly GPM/IMERG Precipitation in Hubei Province, China

Sun

Wang²,

Zhang³

et al. 2022

Atmosphere

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High spatial resolution (1 km or finer) precipitation data fields are crucial for understanding the Earth’s water and energy cycles at the regional scale for applications. The spatial resolution of the Global Precipitation Measurement (GPM) mission (IMERG) satellite precipitation products is 0.1° (latitude) × 0.1° (longitude), which is too coarse for regional-scale analysis. This study combined the Geographically Weighted Regression (GWR) and the Multifractal Random Cascade (MFRC) model to downscale monthly GPM/IMERG precipitation products from 0.1° × 0.1° (approximately 11 km × 11 km) to 1 km in Hubei Province, China. This work’s results indicate the following: (1) The original GPM product can accurately express the precipitation in the study area, which highly correlates with the site data from 2015 to 2017 (R2 = 0.79) and overall presents the phenomenon of overestimation. (2) The GWR model maintains the precipitation field’s overall accuracy and smoothness, with even improvements in accuracy for specific months. In contrast, the MFRC model causes a slight decrease in the overall accuracy of the precipitation field but performs better in reducing the bias. (3) The GWR-MF combined with the GWR and MFRC model improves the observation accuracy of the downscaling results and reduces the bias value by introducing the MFRC to correct the deviation of GWR. The conclusion and analysis of this paper can provide a meaningful experience for 1 km high-resolution data to support related applications.

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Climatic effects on primary and secondary growth of Pinus oocarpa trees in southern Mexico

Gutiérrez-García,

Ricker

2024

Dendrochronologia

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Temporal relationships between daily precipitation and NDVI time series in Mexico

Cited by 8 publications

References 9 publications

Spatiotemporal Pattern of Vegetation Ecology Quality and Its Response to Climate Change between 2000–2017 in China

Spatiotemporal Pattern of Vegetation Ecology Quality and Its Response to Climate Change between 2000–2017 in China

Spatial Downscaling Model Combined with the Geographically Weighted Regression and Multifractal Models for Monthly GPM/IMERG Precipitation in Hubei Province, China

Climatic effects on primary and secondary growth of Pinus oocarpa trees in southern Mexico

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