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A high-resolution daily gridded meteorological dataset for Serbia made by Random Forest Spatial Interpolation.

ABSTRACT: We produced the first daily gridded meteorological dataset at a 1-km spatial resolution across Serbia for 2000-2019, named MeteoSerbia1km. The dataset consists of five daily variables: maximum, minimum and mean temperature, mean sea-level pressure, and total precipitation. In addition to daily summaries, we produced monthly and annual summaries, and daily, monthly, and annual long-term means. Daily gridded data were interpolated using the Random Forest Spatial Interpolation methodology, based on using the nearest observations and distances to them as spatial covariates, together with environmental covariates to make a random forest model. The accuracy of the MeteoSerbia1km daily dataset was assessed using nested 5-fold leave-location-out cross-validation. All temperature variables and sea-level pressure showed high accuracy, although accuracy was lower for total precipitation, due to the discontinuity in its spatial distribution. MeteoSerbia1km was also compared with the E-OBS dataset with a coarser resolution: both datasets showed similar coarse-scale patterns for all daily meteorological variables, except for total precipitation. As a result of its high resolution, MeteoSerbia1km is suitable for further environmental analyses.

SUBMITTER: Sekulic A

PROVIDER: S-EPMC8087659 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Changes in the timing of plant phenological phases are important proxies in contemporary climate research. However, most of the commonly used traditional phenological observations do not give any coherent spatial information. While consistent spatial data can be obtained from airborne sensors and preprocessed gridded meteorological data, not many studies robustly benefit from these data sources. Therefore, the main aim of this study is to create and evaluate different statistical models for reconstructing, predicting, and improving quality of phenological phases monitoring with the use of satellite and meteorological products. A quality-controlled dataset of the 13 BBCH plant phenophases in Poland was collected for the period 2007-2014. For each phenophase, statistical models were built using the most commonly applied regression-based machine learning techniques, such as multiple linear regression, lasso, principal component regression, generalized boosted models, and random forest. The quality of the models was estimated using a k-fold cross-validation. The obtained results showed varying potential for coupling meteorological derived indices with remote sensing products in terms of phenological modeling; however, application of both data sources improves models' accuracy from 0.6 to 4.6 day in terms of obtained RMSE. It is shown that a robust prediction of early phenological phases is mostly related to meteorological indices, whereas for autumn phenophases, there is a stronger information signal provided by satellite-derived vegetation metrics. Choosing a specific set of predictors and applying a robust preprocessing procedures is more important for final results than the selection of a particular statistical model. The average RMSE for the best models of all phenophases is 6.3, while the individual RMSE vary seasonally from 3.5 to 10 days. Models give reliable proxy for ground observations with RMSE below 5 days for early spring and late spring phenophases. For other phenophases, RMSE are higher and rise up to 9-10 days in the case of the earliest spring phenophases.

Dataset Information

A high-resolution daily gridded meteorological dataset for Serbia made by Random Forest Spatial Interpolation.

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