{"type": "FeatureCollection", "features": [{"id": "10.5194/gmd-10-1945-2017", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:21:48Z", "type": "Journal Article", "created": "2017-05-17", "title": "A non-linear Granger-causality framework to investigate climate\u2013vegetation dynamics", "description": "<p>Abstract. Satellite Earth observation has led to the creation of global climate data records of many important environmental and climatic variables. These come in the form of multivariate time series with different spatial and temporal resolutions. Data of this kind provide new means to further unravel the influence of climate on vegetation dynamics. However, as advocated in this article, commonly used statistical methods are often too simplistic to represent complex climate\uffe2\uff80\uff93vegetation relationships due to linearity assumptions. Therefore, as an extension of linear Granger-causality analysis, we present a novel non-linear framework consisting of several components, such as data collection from various databases, time series decomposition techniques, feature construction methods, and predictive modelling by means of random forests. Experimental results on global data sets indicate that, with this framework, it is possible to detect non-linear patterns that are much less visible with traditional Granger-causality methods. In addition, we discuss extensive experimental results that highlight the importance of considering non-linear aspects of climate\uffe2\uff80\uff93vegetation dynamics.                     </p>", "keywords": ["QE1-996.5", "0207 environmental engineering", "TIME-SERIES", "Geology", "02 engineering and technology", "15. Life on land", "SOIL-MOISTURE", "SAMPLE TESTS", "SURFACE-TEMPERATURE", "01 natural sciences", "RANDOM FORESTS", "CARBON-DIOXIDE", "NDVI DATA", "13. Climate action", "Earth and Environmental Sciences", "PRECIPITATION", "GLOBAL TERRESTRIAL ECOSYSTEMS", "SDG 13 - Climate Action", "SATELLITE", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://gmd.copernicus.org/articles/10/1945/2017/gmd-10-1945-2017.pdf"}, {"href": "https://doi.org/10.5194/gmd-10-1945-2017"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoscientific%20Model%20Development", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/gmd-10-1945-2017", "name": "item", "description": "10.5194/gmd-10-1945-2017", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/gmd-10-1945-2017"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-05-17T00:00:00Z"}}, {"id": "10.1088/1748-9326/aa7145", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:18:10Z", "type": "Journal Article", "created": "2017-05-05", "title": "Vegetation anomalies caused by antecedent precipitation in most of the world", "description": "Quantifying environmental controls on vegetation is critical to predict the net effect of climate change on global ecosystems and the subsequent feedback on climate. Following a non-linear Granger causality framework based on a random forest predictive model, we exploit the current wealth of multi-decadal satellite data records to uncover the main drivers of monthly vegetation variability at the global scale. Results indicate that water availability is the most dominant factor driving vegetation globally: about 61% of the vegetated surface was primarily water-limited during 1981\u20132010. This included semiarid climates but also transitional ecoregions. Intra-annually, temperature controls Northern Hemisphere deciduous forests during the growing season, while antecedent precipitation largely dominates vegetation dynamics during the senescence period. The uncovered dependency of global vegetation on water availability is substantially larger than previously reported. This is owed to the ability of the framework to (1) disentangle the co-linearities between radiation/temperature and precipitation, and (2) quantify non-linear impacts of climate on vegetation. Our results reveal a prolonged effect of precipitation anomalies in dry regions: due to the long memory of soil moisture and the cumulative, non-linear, response of vegetation, water-limited regions show sensitivity to the values of precipitation occurring three months earlier. Meanwhile, the impacts of temperature and radiation anomalies are more immediate and dissipate shortly, pointing to a higher resilience of vegetation to these anomalies. Despite being infrequent by definition, hydro-climatic extremes are responsible for up to 10% of the vegetation variability during the 1981\u20132010 period in certain areas, particularly in water-limited ecosystems. Our approach is a first step towards a quantitative comparison of the resistance and resilience signature of different ecosystems, and can be used to benchmark Earth system models in their representations of past vegetation sensitivity to changes in climate.", "keywords": ["Science", "QC1-999", "water", "TROPICAL FORESTS", "0207 environmental engineering", "02 engineering and technology", "SOIL-MOISTURE", "Environmental technology. Sanitary engineering", "01 natural sciences", "stress", "water stress", "global vegetation", "AMAZON", "FORESTS", "CLIMATE EXTREMES", "hydro-climatic extremes", "ecosystem resilience", "DRY-SEASON", "GE1-350", "TEMPERATURE", "SATELLITE", "TD1-1066", "0105 earth and related environmental sciences", "Physics", "Q", "Biology and Life Sciences", "15. Life on land", "6. Clean water", "Environmental sciences", "NDVI DATA", "13. Climate action", "Earth and Environmental Sciences", "GROWING-SEASON", "Granger causality", "CARBON-CYCLE"]}, "links": [{"href": "https://doi.org/10.1088/1748-9326/aa7145"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Research%20Letters", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1088/1748-9326/aa7145", "name": "item", "description": "10.1088/1748-9326/aa7145", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1088/1748-9326/aa7145"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-07-01T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=NDVI+DATA&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=NDVI+DATA&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=NDVI+DATA&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=NDVI+DATA&offset=2", "hreflang": "en-US"}], "numberMatched": 2, "numberReturned": 2, "distributedFeatures": [], "timeStamp": "2026-07-26T22:53:50.847371Z"}