Soil and climate differently impact NDVI patterns according to the season and the stand type

Piedallu, Christian; Chéret, Véronique; Denux, Jean-Philippe; Perez, Vincent; Azcona, Jaime Sebastian; Seynave, Ingrid; Gégout, Jean‐Claude

doi:10.1016/j.scitotenv.2018.10.052

Cited by 84 publications

(41 citation statements)

References 72 publications

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“…The results show that the dynamics of vegetation are driven by climatic variability. In winter, low temperatures limit biomass, vegetation growth, resulting in low values [23]. In very particular cases and in conditions of high humidity the VI values decreased, most of the correlations were positive in rainfall and temperature [55].…”

Section: Discussionmentioning

confidence: 99%

Spatio-Temporal Response of Vegetation Indices to Rainfall and Temperature in A Semiarid Region

et al. 2020

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In this research, vegetation indices (VIs) were analyzed as indicators of the spatio-temporal variation of vegetation in a semi-arid region. For a better understanding of this dynamic, interactions between vegetation and climate should be studied more widely. To this end, the following methodology was proposed: (1) acquire the NDVI, EVI, SAVI, MSAVI, and NDMI by classification of vegetation and land cover categories in a monthly period from 2014 to 2018; (2) perform a geostatistical analysis of rainfall and temperature; and (3) assess the application of ordinary and uncertainty least squares linear regression models to experimental data from the response of vegetation indices to climatic variables through the BiDASys (bivariate data analysis system) program. The proposed methodology was tested in a semi-arid region of Zacatecas, Mexico. It was found that besides the high values in the indices that indicate good health, the climatic variables that have an impact on the study area should be considered given the close relationship with the vegetation. A better correlation of the NDMI and EVI with rainfall and temperature was found, and similarly, the relationship between VIs and climatic factors showed a general time lag effect. This methodology can be considered in management and conservation plans of natural ecosystems, in the context of climate change and sustainable development policies.

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Section: Discussionmentioning

confidence: 99%

Spatio-Temporal Response of Vegetation Indices to Rainfall and Temperature in A Semiarid Region

et al. 2020

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“…A commonly used vegetation index in crop nutrient management is the normalized difference vegetation index (NDVI) (Piedallu et al., 2019). Values for NDVI are based on red and near‐infrared spectral band absorption (Tucker, Townshend, & Goff, 1985), and is strongly correlated to the photosynthetically active radiation signifying vegetation productivity (Piedallu et al., 2019). Plant biomass estimate via NDVI collected using optical sensors have improved fertilizer N recommendations (Solie, Monroe, Raun, & Stone, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, Raun et al (2017) suggested the use of active sensors using vegetation indices to recommend in-season fertilizer N recommendation. A commonly used vegetation index in crop nutrient management is the normalized difference vegetation index (NDVI) (Piedallu et al, 2019). Values for NDVI are based on red and near-infrared spectral band absorption (Tucker, Townshend, & Goff, 1985), and is strongly correlated to the photosynthetically active radiation signifying vegetation productivity (Piedallu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Nitrogen management impact on winter wheat grain yield and estimated plant nitrogen loss

et al. 2020

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Method of N application in winter wheat (Triticum aestivum L.) and its impact on estimated plant N loss has not been extensively evaluated. The effects of the pre‐plant N application method, topdress N application method, and their interactions on grain yield, grain protein concentration (GPC), nitrogen fertilizer recovery use efficiency (NFUE), and gaseous N loss was investigated. The trials were set up in an incomplete factorial within a randomized complete block design and replicated three times for 5 site‐years. Data collection included normalized difference vegetation index (NDVI), grain yield, and forage and grain N concentration. The NDVI before and after 90 growing degree days (GDD) were correlated with final grain yield, grain N uptake, GPC, and NFUE. At Efaw location, NDVI after 90 GDDs accounted for 58% of variation in grain yield and 51% variation in grain N uptake. However, NDVI was found to be a poor indicator of both GPC and NFUE. Grain yield was not affected by the method and timing of N application at Efaw. Alternatively, at Perkins, topdress applications resulted in higher yields. The GPC and NFUE were improved with the topdress applications. Generally, topdress application enhanced GPC and NFUE without decreasing the final grain yield. The difference method used in calculating gaseous N loss did not always reveal similar results, and estimated plant N loss was variable by site‐year, and depended on daily fluctuations in the environment.

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“…In crop nutrient management, a commonly used vegetative index is the normalized difference vegetation index (NDVI) (Piedallu et al., 2019). Where NDVI is calculated based on reflectance of red and near‐infrared spectral bands (Tucker, Townshend, & Goff, 1985).…”

Section: Introductionmentioning

confidence: 99%

Applied use of growing degree days to refine optimum times for nitrogen stress sensing in winter wheat

et al. 2020

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Variable influence of the environment on early‐season plant growth leads to similarly variable yield levels from year to year. This study was conducted to determine the ideal point in the growing season when normalized difference vegetation index (NDVI) sensor readings were highly correlated with grain yield. For each site‐year, NDVI readings were collected at least seven times from December through April. Readings were collected from two long‐term experiments where an N response was expected in plots that historically received different N rates. The number of days from planting to sensing where growing degree days (GDD) were more than 0 (GDD > 0) was tabulated by site‐year for all dates when NDVI data were collected. The r2 was computed for NDVI versus final grain yield at all sensing dates and plotted against the respective GDD > 0 when readings were taken. Linear plateau models were used to determine the point when the r2 peaked. Averaged over 3 yr (2016–2018), the optimum GDD > 0 needed to predict grain yield using NDVI in both long‐term trials was between 97 and 112. Use of the GDD > 0 as a numeric metric to delineate the best time and date to collect NDVI readings and predict yield potential can then be used to formulate accurate midseason fertilizer N rates. Adhering to quantitative GDD > 0 data is much more reliable than using subjective morphological scales. These critical GDD values can be reported on a day‐to‐day, by‐location basis (http://mesonet.org) for in‐season producer use.

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Soil and climate differently impact NDVI patterns according to the season and the stand type

Cited by 84 publications

References 72 publications

Spatio-Temporal Response of Vegetation Indices to Rainfall and Temperature in A Semiarid Region

Spatio-Temporal Response of Vegetation Indices to Rainfall and Temperature in A Semiarid Region

Nitrogen management impact on winter wheat grain yield and estimated plant nitrogen loss

Applied use of growing degree days to refine optimum times for nitrogen stress sensing in winter wheat

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