Reanalysis datasets outperform other gridded climate products in vegetation change analysis in peripheral conservation areas of Central Asia

Abstract

Global environmental research requires long-term climate data. Yet, meteorological infrastructure is missing in the vast majority of the world’s protected areas. Therefore, gridded products are frequently used as the only available climate data source in peripheral regions. However, associated evaluations are commonly biased towards well observed areas and consequently, station-based datasets. As evaluations on vegetation monitoring abilities are lacking for regions with poor data availability, we analyzed the potential of several state-of-the-art climate datasets (CHIRPS, CRU, ERA5-Land, GPCC-Monitoring-Product, IMERG-GPM, MERRA-2, MODIS-MOD10A1) for assessing NDVI anomalies (MODIS-MOD13Q1) in two particularly suitable remote conservation areas. We calculated anomalies of 156 climate variables and seasonal periods during 2001–2018, correlated these with vegetation anomalies while taking the multiple comparison problem into consideration, and computed their spatial performance to derive suitable parameters. Our results showed that four datasets (MERRA-2, ERA5-Land, MOD10A1, CRU) were suitable for vegetation analysis in both regions, by showing significant correlations controlled at a false discovery rate < 5% and in more than half of the analyzed areas. Cross-validated variable selection and importance assessment based on the Boruta algorithm indicated high importance of the reanalysis datasets ERA5-Land and MERRA-2 in both areas but higher differences and variability between the regions with all other products. CHIRPS, GPCC and the bias-corrected version of MERRA-2 were unsuitable and not important in both regions. We provide evidence that reanalysis datasets are most suitable for spatiotemporally consistent environmental analysis whereas gauge- or satellite-based products and their combinations are highly variable and may not be applicable in peripheral areas.

Using of Google Earth Engine in monitoring systems

“…Hydrological and climatic data were selected from ERA5-Land Monthly Averaged . Reanalysis datasets are most suitable for spatiotemporally consistent environmental analysis [8]. The ERA5 Land dataset is actively used in climate change research [9][10][11].…”

Contributions to Satellite-Based Land Cover Classification, Vegetation Quantification and Grassland Monitoring in Central Asian Highlands Using Sentinel-2 and MODIS Data

“…This area, located at altitudes between 2,900 m and 6,300 m, is at the junction of the Pamir, Hindukush and Karakoram mountain ranges and is characterized by an arid to semi-arid cold climate. Yearly average temperatures range between −1 °C and −3 °C, and precipitation sums are around 200 mm in the valleys (Pohl et al, 2015;Zandler et al, 2019). Winter temperatures are cold with subzero temperatures from October until March, with averages around −15 °C to −5 °C and absolute extremes reaching down to −60 °C, whereas in summer, averages are around 10 °C and mean maxima reaching about 26 °C (State Administration for Hydrometeorology of the Republic of Tajikistan 2013; Metrak et al, 2015;Zandler et al, 2019).…”

“…However, remote sensing faces major challenges in these regions due to low vegetation cover and signal to noise ratio, high shares of non-photosynthetic plant tissue and soil background reflectance, and large spatial heterogeneity (Eisfelder et al, 2012;Smith et al, 2019;Zhang et al, 2019). Additionally, reliable spatial climate datasets and variables are necessary to assess potential drivers of vegetation change, but the scarcity of climate infrastructure in respective regions leads to limitations in the availability of long term climate data for vegetation monitoring (Zandler et al, 2019(Zandler et al, , 2020. Despite the high ecological and societal relevance of these drylands, research and remote sensing algorithms that are adapted to the specific situation in this particular regions are still limited (Smith et al, 2019;, and studies in Asia's cold grasslands are particularly scarce (Hu and Hu 2019).…”

“…In addition to these variables that are designed to directly detect biophysical vegetation properties, snow is an important plant moisture source in cold drylands (Peng et al, 2010). Therefore, remotely sensed snow variables may be important vegetation proxies and have the potential to contribute to vegetation analysis and monitoring in respective regions (Wang et al, 2013, Wang et al, 2018Qiu et al, 2019;Zandler et al, 2020). However, only a limited number of studies tested the potential of respective variables for both classification and modeling.…”

Time Series Analysis of Land Cover Change in Dry Mountains: Insights from the Tajik Pamirs

“…The data were downloaded from the Climate Data Online (CDO) archive of the National Centers for Environmental Information [107]. Furthermore, we utilized reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5-Land (ERA5-Land) dataset with a spatial resolution of 0.1 • [108], and the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) dataset with a spatial resolution of 0.5 • × 0.625 • [109], as these products proved to be the most suitable for vegetation analysis in mountain regions with limited data availability [110].…”