PHASE: A geostatistical model for the Kriging-based spatial prediction of crop phenology using public phenological and climatological observations

Gerstmann, Henning; Doktor, Daniel; Gläßer, Cornelia; Möller, Markus

doi:10.1016/j.compag.2016.07.032

Cited by 34 publications

(21 citation statements)

References 59 publications

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“…The interpolated phenological maps indicated a coherent spatial distribution of the interpolated phenological DOYs from prespring to full autumn across the Bavaria region and its elevational gradients. Comparable spatial differences were modeled by Gerstmann et al (2016) for shooting (DOYs 100-130) and yellow ripening (DOYs 180-220) of winter wheat across Germany where these Kriging-interpolated phenological phases also showed delays of plant development in mountainous and coastal regions. Since the interpolation method applied uses regression coefficients from multiple linear regression models which most importantly account for the elevation, this spatial difference proves to be reasonable.…”

Section: Spatial Variability In Map Interpolationmentioning

confidence: 99%

“…Except for late summer (n = 652/135) and full autumn (n = 613/103), there are throughout 800-1000 phenological observations in Germany and 160-200 in Bavaria for spatial interpolation, respectively. Besides, a lack of phenological data for regions above 1000 m is also critical for the uncertainty assessment since interpolation in mountainous regions was evaluated to be associated with high uncertainty according to Gerstmann et al (2016).…”

Section: Spatial Variability In Map Interpolationmentioning

confidence: 99%

“…In order to evaluate consequences from decreasing numbers of observations, mapping phenology via spatial interpolation could be considered, which allows visualization of phenological changes from the point to regional (continental) perspective (Gerstmann et al 2016). Regarding individual ground observations, spatial interpolation or extrapolation must be performed when analyzing the phenological phases in space due to the importance of regional peculiarities (Dose and Menzel 2004;Jochner et al 2013).…”

Section: Introductionmentioning

confidence: 99%

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Maps, trends, and temperature sensitivities—phenological information from and for decreasing numbers of volunteer observers

et al. 2021

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Phenology serves as a major indicator of ongoing climate change. Long-term phenological observations are critically important for tracking and communicating these changes. The phenological observation network across Germany is operated by the National Meteorological Service with a major contribution from volunteering activities. However, the number of observers has strongly decreased for the last decades, possibly resulting in increasing uncertainties when extracting reliable phenological information from map interpolation. We studied uncertainties in interpolated maps from decreasing phenological records, by comparing long-term trends based on grid-based interpolated and station-wise observed time series, as well as their correlations with temperature. Interpolated maps in spring were characterized by the largest spatial variabilities across Bavaria, Germany, with respective lowest interpolated uncertainties. Long-term phenological trends for both interpolations and observations exhibited mean advances of −0.2 to −0.3 days year−1 for spring and summer, while late autumn and winter showed a delay of around 0.1 days year−1. Throughout the year, temperature sensitivities were consistently stronger for interpolated time series than observations. Such a better representation of regional phenology by interpolation was equally supported by satellite-derived phenological indices. Nevertheless, simulation of observer numbers indicated that a decline to less than 40% leads to a strong decrease in interpolation accuracy. To better understand the risk of declining phenological observations and to motivate volunteer observers, a Shiny app is proposed to visualize spatial and temporal phenological patterns across Bavaria and their links to climate change–induced temperature changes.

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Section: Spatial Variability In Map Interpolationmentioning

confidence: 99%

Section: Spatial Variability In Map Interpolationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Maps, trends, and temperature sensitivities—phenological information from and for decreasing numbers of volunteer observers

et al. 2021

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“…To match the spatial resolution of other environmental factors, DEM was resampled to 1 km for the extraction of plan and profile curvatures. The resolution of the underlying DEM suppressed small-scale variations in elevation, thereby reducing the accuracies of morphometric parameters (Gerstmann, Doktor, Gläßer, & Möller, 2016). Grohmann (2015) pointed out that morphometric parameters get smaller when derived by lower resampling of DEM.…”

Section: Uncertainty Estimationmentioning

confidence: 99%

Comparison of interpolation methods for soil moisture prediction on China's Loess Plateau

Xie

Jia

Qin

et al. 2020

Vadose Zone Journal

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Due to limited in situ observations, prediction of large-scale soil moisture content (SMC) for deep soil layers via interpolation is usually very challenging. This is especially true for regions with high spatial variations of terrain features. For precise prediction at a regional scale, SMC data for the 0-to 500-cm soil profile across China's Loess Plateau (CLP) region were collected and interpolated using four different methods. The methods included inverse distance weighting (IDW), ordinary kriging (OK), multiple linear regression with residual kriging (MLR-RK), and radial basis function neural network with residual kriging (RBFNN-RK). The objective of the study was to determine the optimal interpolation method for predicting regional SMC at various soil layers. The study showed that the performances of IDW, OK, and RBFNN-RK in predicting SMC were generally much better than that of MLR-RK. Specifically, IDW performed best for soil depths of 200-300 and 400-500 cm. This was attributed to the more uniform distribution (smoother change of spatial clusters) of SMC in these two layers. The OK method performed best for the 10-to 40-and 40-to 100-cm soil layers, which was due to the strong spatial dependence of the two layers. The RBFNN-RK performed best for the 0-to 10-, 100-to 200-, and 300-to 400-cm soil layers, because RBFNN-RK captures nonlinear relations of SMC with environmental factors. Ordinary kriging, IDW, and RBFNN-RK interpolation can therefore be used to predict regional SMC for different soil layers in CLP region. The RBFNN-RK method was recommended for predicting regional SMC in complex topographic hilly-gully regions where there is nonlinear relation between SMC and environmental variables.

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“…PM derived information has a large potential for different environmental applications such tacking the rhythm of seasons (Morisette et al, 2009), estimating carbon sequestration potential of forests (Leinonen and Kramer, 2002), agriculture and natural resource management (Schwartz et al, 2013;Gerstmann et al, 2016;Nissanka et al, 2017). Moreover, phenological model outputs are used to reconstruct and qualify ground- (Chuine et al, 2004;Menzel, 2005) and satellite-based Macbean et al, 2015) time-series of VPOs, and to estimate species-specific phenology (Krinner et al, 2005;Chuine et al, 2013).…”

Section: Phenological Modelsmentioning

confidence: 99%

Eocomputational workflows for analysing spring plant phenology in space and time

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PHASE: A geostatistical model for the Kriging-based spatial prediction of crop phenology using public phenological and climatological observations

Cited by 34 publications

References 59 publications

Maps, trends, and temperature sensitivities—phenological information from and for decreasing numbers of volunteer observers

Maps, trends, and temperature sensitivities—phenological information from and for decreasing numbers of volunteer observers

Comparison of interpolation methods for soil moisture prediction on China's Loess Plateau

Eocomputational workflows for analysing spring plant phenology in space and time

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