Propastin, Pavel A.

[["dc.bibliographiccitation.firstpage","138"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Basic and Applied Dryland Research"],["dc.bibliographiccitation.lastpage","154"],["dc.bibliographiccitation.volume","1"],["dc.contributor.author","Propastin, Pavel"],["dc.contributor.author","Kappas, Martin"],["dc.contributor.author","Erasmi, Stefan"],["dc.contributor.author","Muratova, Nadia R."],["dc.date.accessioned","2020-05-12T08:32:47Z"],["dc.date.available","2020-05-12T08:32:47Z"],["dc.date.issued","2007"],["dc.description.abstract","We combined Normalized Difference Vegetation Index (NDVI) datasets derived from the Advanced Very High Resolution Radiometer (AVHRR) and climate records to analyse within-season temporal relationships between vegetation activity and two eco-climatic parameters (precipitation and temperature) in an arid region of Central Kazakhstan. Assessments of these relationships were performed by calculating correlation coefficients between 10-day values of NDVI and the both climatic parameters throughout the growing season (April-October). The correlations were calculated for every pixel as well as for the aggregated datasets representing different land cover types and the entire study area. The results indicate that strong significant positive correlations exist between NDVI and each of the explanatory climatic parameters at all spatial scales. Temperature was considered to be the leading climatic factor controlling intra-annual NDVI dynamics. The correlation coefficients between NDVI-rainfall and NDVI-temperature exhibit a clear structure in terms of spatial distribution. The results indicate that the response of vegetation to climatic factors increases in order from shrubs and desert vegetation to semi-desert, short grassland and to steppe vegetation."],["dc.description.abstract","Die Arbeit untersucht zeitliche Zusammenhänge zwischen Vegetationsdynamik und Dynamik von Klimaelementen (Temperatur und Niederschlag) in einem Trockengebiet des Zentral-Kasachstans. Die Datengrundlagen der Arbeit umfassten den Normalized Difference Vegetation Index (NDVI) von dem Advanced Very High Resolution Radiometer (AVHRR) sowie Messwerten der Klimastationen für Niederschlag und Temperatur. Die Schätzung der Stärke des Zusammenhanges erfolgte durch Berechnung des Koeffizienten der Korrelation und Kreuzkorrelation zwischen den Zeitreihen der Dekadenwerte der NDVI und der beiden Klimaelemente während der Pflanzenwachstumsperiode. Die statistischen Zusammenhänge wurden auf verschiedenen Skalen räumlicher Generalisierung betrachtet: von dem gesamten Gebiet bis zu einzelnem Pixel. Die Ergebnisse beweisen, dass auf allen Betrachtungsskalen strenge Interrelationen zwischen der Dynamik des NDVI auf einer Seite und den beiden Klimaelementen auf der anderen Seite bestehen. Temperatur erwies sich als der Hauptfaktor für die Kontrolle der Vegetationsdynamik durch das Klima. Die räumliche Verbreitung der Werte des Korrelationskoeffizienten zeigte ein deutliches Muster. Dieses Muster spiegelt die Unterschiede zwischen einzelnen Vegetationstypen in bezug auf ihre Reaktionskraft und Reaktionsgeschwindigkeit zu der Einwirkung der Klimaelemente wider."],["dc.identifier.doi","10.1127/badr/1/2007/138"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/65138"],["dc.language.iso","en"],["dc.relation.issn","1864-3191"],["dc.title","Remote sensing based study on intra-annual dynamics of vegetation and climate in drylands of Kazakhastan"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","582159"],["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.journal","Journal of Sensors"],["dc.bibliographiccitation.lastpage","11"],["dc.bibliographiccitation.volume","2012"],["dc.contributor.author","Kappas, Martin W."],["dc.contributor.author","Propastin, Pavel A."],["dc.date.accessioned","2018-11-07T09:16:17Z"],["dc.date.accessioned","2020-05-08T09:43:33Z"],["dc.date.available","2018-11-07T09:16:17Z"],["dc.date.available","2020-05-08T09:43:33Z"],["dc.date.issued","2012"],["dc.description.abstract","Leaf area index (LAI) is a key biophysical variable for environmental process modelling. Remotely sensed data have become the primary source for estimation of LAI at the scales from local to global. A summary of existing LAI data sets and a discussion of their appropriateness for the formerly Soviet Central Asia, especially Kazakhstan, which is known for its huge grassland area (about 2 million km(2)), are valuable for environmental modelling in this region. The paper gives a brief review of existing global LAI products, such as AVHRR LAI, MODIS LAI, and SPOT-VEGETATION LAI, and shows that validation of these products in Kazakhstan as well as in other countries of the formerly Soviet Central Asia has not been carried out yet. Apart from the global LAI products, there are just a few data sets retrieved by remote sensing methods at subregional and regional scales in Kazakhstan. More research activities are needed to focus on the validation of the available global LAI products over the formerly Soviet Central Asia and developing new LAI data sets suitable for application in environmental modelling at different scales in this region."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2011"],["dc.identifier.doi","10.1155/2012/582159"],["dc.identifier.isi","000208865000030"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/7593"],["dc.identifier.scopus","2-s2.0-84861049895"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/27904"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/64966"],["dc.identifier.url","http://www.scopus.com/inward/record.url?eid=2-s2.0-84861049895&partnerID=MN8TOARS"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.eissn","1687-7268"],["dc.relation.issn","1687-725X"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Review of Available Products of Leaf Area Index and Their Suitability over the Formerly Soviet Central Asia"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1465"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","International Journal of Remote Sensing"],["dc.bibliographiccitation.lastpage","1487"],["dc.bibliographiccitation.volume","33"],["dc.contributor.author","Propastin, Pavel A."],["dc.contributor.author","Kappas, Martin W."],["dc.contributor.author","Herrmann, Stefanie M."],["dc.contributor.author","Tucker, Compton J."],["dc.date.accessioned","2018-11-07T09:15:27Z"],["dc.date.accessioned","2020-05-08T09:43:25Z"],["dc.date.available","2018-11-07T09:15:27Z"],["dc.date.available","2020-05-08T09:43:25Z"],["dc.date.issued","2012"],["dc.description.abstract","A modified light use efficiency (LUE) model was tested in the grasslands of central Kazakhstan in terms of its ability to characterize spatial patterns and interannual dynamics of net primary production (NPP) at a regional scale. In this model, the LUE of the grassland biome (epsilon(n)) was simulated from ground-based NPP measurements, absorbed photosynthetically active radiation (APAR) and meteorological observations using a new empirical approach. Using coarse-resolution satellite data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), monthly NPP was calculated from 1998 to 2008 over a large grassland region in Kazakhstan. The modelling results were verified against scaled up plot-level observations of grassland biomass and another available NPP data set derived from a field study in a similar grassland biome. The results indicated the reliability of productivity estimates produced by the model for regional monitoring of grassland NPP. The method for simulation of epsilon(n) suggested in this study can be used in grassland regions where no carbon flux measurements are accessible."],["dc.identifier.doi","10.1080/01431161.2011.577105"],["dc.identifier.isi","000302161600008"],["dc.identifier.scopus","2-s2.0-84857931757"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/64965"],["dc.identifier.url","http://www.scopus.com/inward/record.url?eid=2-s2.0-84857931757&partnerID=MN8TOARS"],["dc.language.iso","en"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.eissn","1366-5901"],["dc.relation.issn","0143-1161"],["dc.title","Modified light use efficiency model for assessment of carbon sequestration in grasslands of Kazakhstan: Combining ground biomass data and remote-sensing"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","957"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Journal of Advances in Modeling Earth Systems"],["dc.bibliographiccitation.lastpage","978"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Chen, Yizhao"],["dc.contributor.author","Ju, Weimin"],["dc.contributor.author","Mu, Shaojie"],["dc.contributor.author","Fei, Xinran"],["dc.contributor.author","Cheng, Yuan"],["dc.contributor.author","Propastin, Pavel"],["dc.contributor.author","Zhou, Wei"],["dc.contributor.author","Liao, Cuijuan"],["dc.contributor.author","Chen, Luxiao"],["dc.contributor.author","Tang, Rongjun"],["dc.contributor.author","Qi, Jiaguo"],["dc.contributor.author","Li, Jianlong"],["dc.contributor.author","Ruan, Honghua"],["dc.date.accessioned","2020-12-10T18:09:23Z"],["dc.date.available","2020-12-10T18:09:23Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1029/2018MS001352"],["dc.identifier.eissn","1942-2466"],["dc.identifier.issn","1942-2466"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/73635"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Explicit Representation of Grazing Activity in a Diagnostic Terrestrial Model: A Data‐Process Combined Scheme"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","2234"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","Remote Sensing of Environment"],["dc.bibliographiccitation.lastpage","2242"],["dc.bibliographiccitation.volume","113"],["dc.contributor.author","Propastin, Pavel A."],["dc.date.accessioned","2018-11-07T11:24:05Z"],["dc.date.available","2018-11-07T11:24:05Z"],["dc.date.issued","2009"],["dc.description.abstract","The technique of Geographically Weighted Regression (GWR) was used for estimation of Leaf Area Index (LAI) from remote sensing-based multi-spectral vegetation indices (VI) such as Normalized Difference Vegetation Index (NDVI), the mid-infrared corrected Normalized Difference Vegetation Index (NDVIc), Simple Ratio (SR), Soil-Adjusted Vegetation Index (SAVI) and Reduced Simple Ratio (RSR) in a region of equatorial rainforest in Central Sulavesi, Indonesia. The linear regressions between NDVI, NDVIc, SR, SAVI and RSR as explanatory variables and ground measurements of LAI at 166 plots as a dependent variable were produced using common modelling approach - Ordinary Least Squares (OLS) regression fitted to all data points, as well as GWR. Accuracy and precision statistics indicate that the GWR method made significantly better predictions of LAI in all simulations than OLS did. The relationships between LAI and the explanatory variables were found to be significantly spatially variable and scale-dependent. GWR has the potential to reveal local patterns in the spatial distribution of parameter estimates. it demonstrated sensitivity of the model's accuracy and performance to scale variation. The GWR approach enables finding the most appropriate scale for data analysis. This scale was different for each VI. The results suggest that spatial non-stationarity and scale-dependency in the relationship between LAI and remote sensing data has important implications for estimations of LAI based on empirical transfer functions. (C) 2009 Elsevier Inc. All rights reserved."],["dc.description.sponsorship","SFB [552]; Deutsche Forschungsgemeinschaft (DFG)"],["dc.identifier.doi","10.1016/j.rse.2009.06.007"],["dc.identifier.isi","000269277200018"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/56325"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Elsevier Science Inc"],["dc.relation.issn","0034-4257"],["dc.title","Spatial non-stationarity and scale-dependency of prediction accuracy in the remote estimation of LAI over a tropical rainforest in Sulawesi, Indonesia"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","S83"],["dc.bibliographiccitation.issue","SUPPL. 1"],["dc.bibliographiccitation.journal","International Journal of Applied Earth Observation and Geoinformation"],["dc.bibliographiccitation.lastpage","S89"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Propastin, Pavel A."],["dc.contributor.author","Fotso, Lucien"],["dc.contributor.author","Kappas, Martin"],["dc.date.accessioned","2018-11-07T08:46:17Z"],["dc.date.accessioned","2020-05-08T09:41:39Z"],["dc.date.available","2018-11-07T08:46:17Z"],["dc.date.available","2020-05-08T09:41:39Z"],["dc.date.issued","2010"],["dc.description.abstract","The study investigates the vulnerability of vegetation over Africa to El-Nino Southern Oscillation (ENSO) events using the moving window statistical correlation analysis technique The correlation analysis was done between Normalized Difference Vegetation Index (NDVI) data from Advanced Very High Resolution Radiometer (AVHRR) and an ENSO index, namely Multivariate ENSO Index (MEI) The Study develops a new monitoring approach (ENSO vulnerability assessment system) to quantify the relationships between monthly maximum NDVI anomalies and their month-to-month correlations with the ENSO indices for the vegetative land areas of Africa The data Used in this Study was for the period 1982-2006 The new monitoring approach was used in the assessment of the long-time vegetation sensitivity to the ENSO warm events that Occurred during the Study period A map of Africa indicating the vegetation Vulnerability to ENSO is produced Different areas of vegetation vulnerability are Identified within the main vegetation cover classes For the African vegetative land. 16% of the total area was characterized by moderate vulnerability of vegetation to El-Nino, whereas 1.18% showed high Vulnerability Results suggest that the vulnerability of vegetative land surfaces across Africa to climate extremes, such as ENSO depends considerably oil the vegetation type In Particular, results show that areas of equatorial rainforest are more resistant to drought stress than the wooded and non-wooded vegetation categories. (C) 2009 Elsevier B V All rights reserved"],["dc.identifier.doi","10.1016/j.jag.2009.10.007"],["dc.identifier.isi","000275612600015"],["dc.identifier.scopus","2-s2.0-75549088074"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/64960"],["dc.identifier.url","http://www.scopus.com/inward/record.url?eid=2-s2.0-75549088074&partnerID=MN8TOARS"],["dc.language.iso","en"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.issn","0303-2434"],["dc.title","Assessment of vegetation vulnerability to ENSO warm events over Africa"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","159"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Remote Sensing"],["dc.bibliographiccitation.lastpage","183"],["dc.bibliographiccitation.volume","1"],["dc.contributor.author","Propastin, Pavel A."],["dc.contributor.author","Kappas, Martin"],["dc.date.accessioned","2018-11-07T11:25:29Z"],["dc.date.accessioned","2020-05-08T08:40:17Z"],["dc.date.available","2018-11-07T11:25:29Z"],["dc.date.available","2020-05-08T08:40:17Z"],["dc.date.issued","2009"],["dc.description.abstract","Carbon sequestration was estimated in a semi-arid grassland region in Central Kazakhstan using an approach that integrates remote sensing, field measurements and meteorological data. Carbon fluxes for each pixel of 1 x 1 km were calculated as a product of photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (fPAR), the light use efficiency (LUE) and ecosystem respiration (R-e). The PAR is obtained from a mathematical model incorporating Earth-Sun distance, solar inclination, solar elevation angle, geographical position and cloudiness information of localities. The fPAR was measured in field using hemispherical photography and was extrapolated to each pixel by combination with the Normalized Difference Vegetation Index (NDVI) obtained by the Vegetation instrument on board the Satellite Pour l'Observation de la Terra (SPOT) satellite. Gross Primary Production (GPP) of the aboveground and belowground vegetation of 14 sites along a 230 km west-east transect within the study region were determined at the peak of growing season in different land cover types and linearly related to the amount of PAR absorbed by vegetation (APAR). The product of this relationship is LUE = 0.61 and 0.97 g C/MJ APAR for short grassland and steppe, respectively. The R-e is estimated using complex models driven by climatic data. Growing season carbon sequestration was calculated for the modelling year of 2004. Overall, the short grassland was a net carbon sink, whereas the steppe was carbon neutral. The evaluation of the modelled carbon sequestration against independent reference data sets proved high accuracy of the estimations."],["dc.identifier.doi","10.3390/rs1030159"],["dc.identifier.isi","000208400500003"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/8263"],["dc.identifier.scopus","2-s2.0-79952038615"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/56630"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/64956"],["dc.identifier.url","http://www.scopus.com/inward/record.url?eid=2-s2.0-79952038615&partnerID=MN8TOARS"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.issn","2072-4292"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Modeling Net Ecosystem Exchange for Grassland in Central Kazakhstan by Combining Remote Sensing and Field Data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Propastin, P. A."],["dc.contributor.author","Kappas, Martin"],["dc.date.accessioned","2020-05-11T14:58:20Z"],["dc.date.available","2020-05-11T14:58:20Z"],["dc.date.issued","2009"],["dc.identifier.doi","10.1088/1755-1307/6/24/242022"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/65079"],["dc.language.iso","en"],["dc.relation.ispartof","The Role of Agriculture in Mitigating Climate Change"],["dc.title","Monitoring grassland carbon sequestration in central Kazakhstan with remote sensing and carbon cycle modelling"],["dc.type","book_chapter"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","75"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Journal of Environmental Informatics"],["dc.bibliographiccitation.lastpage","87"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Propastin, Pavel A."],["dc.contributor.author","Kappas, Martin"],["dc.contributor.author","Muratova, N. R."],["dc.date.accessioned","2018-11-07T11:08:38Z"],["dc.date.accessioned","2020-05-11T09:22:16Z"],["dc.date.available","2018-11-07T11:08:38Z"],["dc.date.available","2020-05-11T09:22:16Z"],["dc.date.issued","2008"],["dc.description.abstract","We analyzed inter-annual trends in annual and seasonal vegetation activities in Central Asia from 1982 to 2003 and their correlation to climate variability using the NOAA/AVHRR Normalized Difference Vegetation Index (NDVI) dataset and a gridded climate dataset. The results indicate a significant increase in NDVI with a value of 11.35% over the growing season during the 22-year period. Totalled over the entire vegetated area, about 35% of all pixels exhibited significant upward trend in growing season NDVI. We found that NDVI increase in spring was the main contributor to the general upward trend, the spring NDVI increased in more than 50% of all pixels and showed an average value of 13.58%. Correlation analysis indicated a gradual rise in temperature as the only factor controlling trend in spring NDVI. Significant increase in vegetation activity was also identified for summer season, but its amplitude (9.23%) and comprising area (25.13% of all vegetated pixels) were less than for spring. Downward trends in growing season NDVI occurred in 2.17% of the total vegetated area. The greening trends of spring, growing season and summer NDVI strongly related with the climatic parameters: for each land cover type, we found significant correlation with spring temperature and total precipitation; 75% of all upward trends in growing season NDVI were explained by the combination of these both variables. We found that the NDVI trends and their climatic correlates demonstrate great spatial variability at the scale of individual land cover types and at per-pixel scale and proofed that the land use change caused by the constitutional change in the 1991 has substantial control on the vegetation trends. Increased vegetation growth indicated through the analysis of NOAA AVHRR NDVI time-series suggests an increasing carbon stock in biomass of ecosystems in Central Asia."],["dc.description.sponsorship","Academy of Science of Kazakhstan"],["dc.identifier.doi","10.3808/jei.200800126"],["dc.identifier.isi","000207622200001"],["dc.identifier.scopus","2-s2.0-57649232315"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/52828"],["dc.identifier.url","http://www.scopus.com/inward/record.url?eid=2-s2.0-57649232315&partnerID=MN8TOARS"],["dc.language.iso","en"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.issn","1726-2135"],["dc.title","Inter-Annual Changes in Vegetation Activities and Their Relationship to Temperature and Precipitation in Central Asia from 1982 to 2003"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","220"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Remote Sensing"],["dc.bibliographiccitation.lastpage","246"],["dc.bibliographiccitation.volume","4"],["dc.contributor.author","Propastin, Pavel A."],["dc.contributor.author","Kappas, Martin"],["dc.date.accessioned","2018-11-07T09:15:01Z"],["dc.date.accessioned","2020-05-11T09:20:50Z"],["dc.date.available","2018-11-07T09:15:01Z"],["dc.date.available","2020-05-11T09:20:50Z"],["dc.date.issued","2012"],["dc.description.abstract","A new multi-decade national-wide coarse-resolution data set of leaf area index (LAI) over the Republic of Kazakhstan has been developed based on data from the Advanced Very High Resolution Radiometer (AVHRR) and in situ measurements of vegetation structure. The Kazakhstan-wide LAI product has been retrieved using an algorithm based on a physical radiative transfer model establishing a relationship between LAI and given patterns of surface reflectance, view-illumination conditions and optical properties of vegetation at the per-pixel scale. The results revealed high consistencies between the produced AVHRR LAI data set and ground truth information and the 30-m resolution Landsat ETM+ LAI estimated using the similar algorithm. Differences in LAI between the AVHRR-based product and the Landsat ETM+-based product are lower than 0.4 LAI units in terms of RMSE. The produced Kazakhstan-wide LAI was also compared with the global 8-km AVHRR LAI (LAI_PAL_BU_V3) and 1-km MODIS LAI (MOD15A2 LAI) products. Results show remarkable consistency of the spatial distribution and temporal dynamics between the new LAI product and both examined global LAI products. However, the results also revealed several discrepancies in LAI estimates when comparing the global and the Kazakhstan-wide products. The discrepancies in LAI estimates were outlined and discussed."],["dc.identifier.doi","10.3390/rs4010220"],["dc.identifier.isi","000306755900012"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/8252"],["dc.identifier.scopus","2-s2.0-84857933030"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/64981"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/27571"],["dc.identifier.url","http://www.scopus.com/inward/record.url?eid=2-s2.0-84857933030&partnerID=MN8TOARS"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.issn","2072-4292"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Retrieval of Coarse-Resolution Leaf Area Index over the Republic of Kazakhstan Using NOAA AVHRR Satellite Data and Ground Measurements"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]