{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T04:52:56Z","timestamp":1780462376055,"version":"3.54.1"},"reference-count":103,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T00:00:00Z","timestamp":1659052800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41801254"],"award-info":[{"award-number":["41801254"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Satellite-based normalized difference vegetation index (NDVI) time series data are useful for monitoring the changes in vegetation ecosystems in the context of global climate change. However, most of the current NDVI products cannot effectively reconcile high spatial resolution and continuous observations in time. Here, to produce a global-scale, long-term, and high-resolution NDVI database, we developed a simple and new data downscaling approach. The downscaling algorithm considers the pixel-wise ratios of the coefficient of variation (CV) between the coarse- and fine-resolution NDVI data and relative changes in the NDVI against a baseline period. The algorithm successfully created a worldwide monthly NDVI database with 250 m resolution from 1982 to 2018 by translating the fine spatial information from MODIS (Moderate-resolution Imaging Spectroradiometer) data and the long-term temporal information from AVHRR (Advanced Very High Resolution Radiometer) data. We employed the evaluation indices of root mean square error (RMSE), mean absolute error (MAE), and Pearson\u2019s correlation coefficient (Pearson\u2019s R) to assess the accuracy of the downscaled data against the MODIS NDVI. Both the RMSE and MAE values at the regional and global scales are typically between 0 and 0.2, whereas the Pearson\u2019s R values are mostly above 0.7, which implies that the downscaled NDVI product is similar to the MODIS NDVI product. We then used the downscaled data to monitor the NDVI changes in different plant types and places with significant vegetation heterogeneity, as well as to investigate global vegetation trends over the last four decades. The Google Earth Engine platform was used for all the data downscaling processes, and here we provide a code for users to easily acquire data corresponding to any part of the world. The downscaled global-scale NDVI time series has high potential for the monitoring of the long-term temporal and spatial dynamics of terrestrial ecosystems under changing environments.<\/jats:p>","DOI":"10.3390\/rs14153639","type":"journal-article","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T04:04:00Z","timestamp":1659326640000},"page":"3639","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["A Global 250-m Downscaled NDVI Product from 1982 to 2018"],"prefix":"10.3390","volume":"14","author":[{"given":"Zhimin","family":"Ma","sequence":"first","affiliation":[{"name":"School of Civil Engineering, Sun Yat-sen University, Zhuhai 519082, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5721-9247","authenticated-orcid":false,"given":"Chunyu","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Sun Yat-sen University, Zhuhai 519082, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"},{"name":"Professorship in Hydrology and Climatology, Institute of Geography, Heidelberg University, 69120 Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6533-3357","authenticated-orcid":false,"given":"Kairong","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Sun Yat-sen University, Zhuhai 519082, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Sun Yat-sen University, Zhuhai 519082, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8344-7016","authenticated-orcid":false,"given":"Jianfeng","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Sun Yat-sen University, Zhuhai 519082, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dingshen","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Sun Yat-sen University, Zhuhai 519082, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0809-5297","authenticated-orcid":false,"given":"Xiaohong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Sun Yat-sen University, Zhuhai 519082, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.scitotenv.2008.04.050","article-title":"The response of terrestrial ecosystems to global climate change: Towards an integrated approach","volume":"404","author":"Rustad","year":"2008","journal-title":"Sci. Total Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1038\/386698a0","article-title":"Increased plant growth in the northern high latitudes from 1981 to 1991","volume":"386","author":"Myneni","year":"1997","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1038\/ngeo2681","article-title":"Anthropogenic carbon release rate unprecedented during the past 66 million years","volume":"9","author":"Zeebe","year":"2016","journal-title":"Nat. Geosci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6307","DOI":"10.1029\/2018JD029522","article-title":"Improvements in the GISTEMP Uncertainty Model","volume":"124","author":"Lenssen","year":"2019","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1111\/nph.15290","article-title":"Climate change leads to accelerated transformation of high-elevation vegetation in the central Alps","volume":"220","author":"Lamprecht","year":"2018","journal-title":"New Phytol."},{"key":"ref_6","unstructured":"Field, C.B., and Barros, V.R. (2014). Climate Change 2014\u2013Impacts, Adaptation and Vulnerability: Regional Aspects, Cambridge University Press."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4881","DOI":"10.1073\/pnas.1618082114","article-title":"Quantifying the influence of global warming on unprecedented extreme climate events","volume":"114","author":"Diffenbaugh","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4901","DOI":"10.1029\/2018WR024067","article-title":"Global and Regional Increase of Precipitation Extremes Under Global Warming","volume":"55","author":"Papalexiou","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/s10584-010-9923-5","article-title":"Increasing impacts of climate change upon ecosystems with increasing global mean temperature rise","volume":"106","author":"Warren","year":"2011","journal-title":"Clim. Chang."},{"key":"ref_10","unstructured":"Hoegh-Guldberg, O., Jacob, D., Bindi, M., Brown, S., Camilloni, I., Diedhiou, A., Djalante, R., Ebi, K., Engelbrecht, F., and Guiot, J. (2018). Impacts of 1.5 \u00b0C global warming on natural and human systems. Global Warming of 1.5 \u00b0C, World Meteorological Organization."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4095","DOI":"10.1111\/gcb.14327","article-title":"Vulnerability of the global terrestrial ecosystems to climate change","volume":"24","author":"Li","year":"2018","journal-title":"Glob. Chang. Biol."},{"key":"ref_12","first-page":"71","article-title":"Drought monitoring with NDVI-based Standardized Vegetation Index","volume":"68","author":"Peters","year":"2002","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3489","DOI":"10.3390\/rs70403489","article-title":"Downscaling 250-m MODIS Growing Season NDVI Based on Multiple-Date Landsat Images and Data Mining Approaches","volume":"7","author":"Gu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7141","DOI":"10.1080\/01431160802238435","article-title":"Neural networks as a tool for constructing continuous NDVI time series from AVHRR and MODIS","volume":"29","author":"Brown","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.rse.2007.05.008","article-title":"Multiscale geostatistical analysis of AVHRR, SPOT-VGT, and MODIS global NDVI products","volume":"112","author":"Tarnavsky","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2111","DOI":"10.1016\/j.rse.2010.04.016","article-title":"Dryland observation at local and regional scale\u2014Comparison of Landsat TM\/ETM+ and NOAA AVHRR time series","volume":"114","author":"Stellmes","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2739","DOI":"10.1080\/01431160600981517","article-title":"Spatial and temporal probabilities of obtaining cloud-free Landsat images over the Brazilian tropical savanna","volume":"28","author":"Sano","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"112189","DOI":"10.1016\/j.rse.2020.112189","article-title":"Interoperability of ECOSTRESS and Landsat for mapping evapotranspiration time series at sub-field scales","volume":"252","author":"Anderson","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_21","unstructured":"Didan, K., Munoz, A.B., Solano, R., and Huete, A. (2015). MODIS Vegetation Index User\u2019s Guide (MOD13 Series), University of Arizona, Vegetation Index and Phenology Lab."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.rse.2005.10.002","article-title":"Multi-sensor NDVI data continuity: Uncertainties and implications for vegetation monitoring applications","volume":"100","author":"Orr","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"7865","DOI":"10.3390\/rs70607865","article-title":"An Improved Method for Producing High Spatial-Resolution NDVI Time Series Datasets with Multi-Temporal MODIS NDVI Data and Landsat TM\/ETM+ Images","volume":"7","author":"Rao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2006.06.018","article-title":"Land-cover change detection using multi-temporal MODIS NDVI data","volume":"105","author":"Lunetta","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"633020","DOI":"10.3389\/fenvs.2021.633020","article-title":"Temporal and Spatial Variations in NDVI and Analysis of the Driving Factors in the Desertified Areas of Northern China From 1998 to 2015","volume":"9","author":"Wang","year":"2021","journal-title":"Front. Environ. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.isprsjprs.2014.08.014","article-title":"Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series","volume":"102","author":"Zhu","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"111511","DOI":"10.1016\/j.rse.2019.111511","article-title":"A review of vegetation phenological metrics extraction using time-series, multispectral satellite data","volume":"237","author":"Zeng","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1002\/hyp.6098","article-title":"A downscaling method of topographic index distribution for matching the scales of model application and parameter identification","volume":"20","author":"Pradhan","year":"2006","journal-title":"Hydrol. Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.rse.2015.06.003","article-title":"Downscaling MODIS images with area-to-point regression kriging","volume":"166","author":"Wang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2018.05.021","article-title":"A new downscaling-integration framework for high-resolution monthly precipitation estimates: Combining rain gauge observations, satellite-derived precipitation data and geographical ancillary data","volume":"214","author":"Chen","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3156","DOI":"10.1109\/TGRS.2011.2120615","article-title":"Downscaling SMOS-Derived Soil Moisture Using MODIS Visible\/Infrared Data","volume":"49","author":"Piles","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kou, X., Jiang, L., Bo, Y., Yan, S., and Chai, L. (2016). Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method. Remote Sens., 8.","DOI":"10.3390\/rs8020105"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"125616","DOI":"10.1016\/j.jhydrol.2020.125616","article-title":"Inter-comparison of several soil moisture downscaling methods over the Qinghai-Tibet Plateau, China","volume":"592","author":"Qu","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"111806","DOI":"10.1016\/j.rse.2020.111806","article-title":"Validation practices for satellite soil moisture retrievals: What are (the) errors?","volume":"244","author":"Gruber","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"102897","DOI":"10.1016\/j.earscirev.2019.102897","article-title":"Principles and methods of scaling geospatial Earth science data","volume":"197","author":"Ge","year":"2019","journal-title":"Earth-Sci. Rev."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1080\/01431169308904402","article-title":"Linear mixing and the estimation of ground cover proportions","volume":"14","author":"Settle","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1080\/01431169508954478","article-title":"NOAA-AVHRR NDVI decomposition and subpixel classification using linear mixing in the Argentinean Pampa","volume":"16","author":"Kerdiles","year":"1995","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1016\/j.rse.2011.03.003","article-title":"Endmember variability in Spectral Mixture Analysis: A review","volume":"115","author":"Somers","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TGRS.2006.872081","article-title":"On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance","volume":"44","author":"Feng","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1016\/j.rse.2009.03.007","article-title":"A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS","volume":"113","author":"Hilker","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.1016\/j.rse.2010.05.032","article-title":"An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions","volume":"114","author":"Zhu","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Cui, J., Zhang, X., and Luo, M. (2018). Combining Linear Pixel Unmixing and STARFM for Spatiotemporal Fusion of Gaofen-1 Wide Field of View Imagery and MODIS Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10071047"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Xue, J., Leung, Y., and Fung, T. (2019). An Unmixing-Based Bayesian Model for Spatio-Temporal Satellite Image Fusion in Heterogeneous Landscapes. Remote Sens., 11.","DOI":"10.3390\/rs11030324"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1080\/07038992.2020.1865141","article-title":"Deep Learning-Based Spatiotemporal Fusion Approach for Producing High-Resolution NDVI Time-Series Datasets","volume":"47","author":"Htitiou","year":"2021","journal-title":"Can. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Nomura, R., and Oki, K. (2021). Downscaling of MODIS NDVI by Using a Convolutional Neural Network-Based Model with Higher Resolution SAR Data. Remote Sens., 13.","DOI":"10.3390\/rs13040732"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"966","DOI":"10.3390\/make3040048","article-title":"Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey","volume":"3","author":"Buhrmester","year":"2021","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Colin Koeniguer, E., and Nicolas, J.-M. (2020). Change Detection Based on the Coefficient of Variation in SAR Time-Series of Urban Areas. Remote Sens., 12.","DOI":"10.3390\/rs12132089"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, R., Gamon, J.A., Emmerton, C.A., Li, H., Nestola, E., Pastorello, G.Z., and Menzer, O. (2016). Integrated Analysis of Productivity and Biodiversity in a Southern Alberta Prairie. Remote Sens., 8.","DOI":"10.3390\/rs8030214"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Tian, F., Wu, B., Zeng, H., Zhang, X., and Xu, J. (2019). Efficient Identification of Corn Cultivation Area with Multitemporal Synthetic Aperture Radar and Optical Images in the Google Earth Engine Cloud Platform. Remote Sens., 11.","DOI":"10.3390\/rs11060629"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.isprsjprs.2018.07.006","article-title":"Cloud\/shadow detection based on spectral indices for multi\/hyperspectral optical remote sensing imagery","volume":"144","author":"Zhai","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Chen, Y., Sun, K., Chen, C., Bai, T., Park, T., Wang, W., Nemani, R.R., and Myneni, R.B. (2019). Generation and Evaluation of LAI and FPAR Products from Himawari-8 Advanced Himawari Imager (AHI) Data. Remote Sens., 11.","DOI":"10.3390\/rs11131517"},{"key":"ref_53","unstructured":"Vermote, E., Justice, C., Csiszar, I., Eidenshink, J., Myneni, R., Baret, F., Masuoka, E., Wolfe, R., and Claverie, M. (2014). NOAA Climate Data Record (CDR) of Normalized Difference Vegetation Index (NDVI), Version 4, NOAA\u2019s National Climatic Data Center."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"356","DOI":"10.3390\/agriengineering1030027","article-title":"Remotely Sensed Boro Rice Production Forecasting Using MODIS-NDVI: A Bangladesh Perspective","volume":"1","author":"Faisal","year":"2019","journal-title":"AgriEngineering"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zhai, Y., Qu, Z., and Hao, L. (2018). Land Cover Classification Using Integrated Spectral, Temporal, and Spatial Features Derived from Remotely Sensed Images. Remote Sens., 10.","DOI":"10.3390\/rs10030383"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.isprsjprs.2014.12.005","article-title":"Development of a spatio-temporal disaggregation method (DisNDVI) for generating a time series of fine resolution NDVI images","volume":"101","author":"Bindhu","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.isprsjprs.2021.08.003","article-title":"Improving the accuracy of spring phenology detection by optimally smoothing satellite vegetation index time series based on local cloud frequency","volume":"180","author":"Tian","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Gu, Y., Wylie, B.K., Boyte, S.P., Picotte, J., Howard, D.M., Smith, K., and Nelson, K.J. (2016). An Optimal Sample Data Usage Strategy to Minimize Overfitting and Underfitting Effects in Regression Tree Models Based on Remotely-Sensed Data. Remote Sens., 8.","DOI":"10.3390\/rs8110943"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"30063","DOI":"10.1073\/pnas.1907378117","article-title":"Benign overfitting in linear regression","volume":"117","author":"Bartlett","year":"2020","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"245","DOI":"10.2307\/1907187","article-title":"Nonparametric Tests Against Trend","volume":"13","author":"Mann","year":"1945","journal-title":"Econometrica"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1080\/01621459.1968.10480934","article-title":"Estimates of the Regression Coefficient Based on Kendall\u2019s Tau","volume":"63","author":"Sen","year":"1968","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s00704-003-0022-7","article-title":"Spatial and temporal variability of winter and summer precipitation over Serbia and Montenegro","volume":"77","year":"2004","journal-title":"Theor. Appl. Climatol."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Theobald, D.M., Harrison-Atlas, D., Monahan, W.B., and Albano, C.M. (2015). Ecologically-relevant maps of landforms and physiographic diversity for climate adaptation planning. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0143619"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1002\/joc.5879","article-title":"Downscaling satellite-derived daily precipitation products with an integrated framework","volume":"39","author":"Chen","year":"2019","journal-title":"Int. J. Climatol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1038\/s41597-021-00861-7","article-title":"Worldwide continuous gap-filled MODIS land surface temperature dataset","volume":"8","author":"Shiff","year":"2021","journal-title":"Sci. Data"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"5874","DOI":"10.1111\/gcb.15279","article-title":"Climate regime shift and forest loss amplify fire in Amazonian forests","volume":"26","author":"Xu","year":"2020","journal-title":"Glob. Chang. Biol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1038\/s41893-022-00854-3","article-title":"Doubling of annual forest carbon loss over the tropics during the early twenty-first century","volume":"5","author":"Feng","year":"2022","journal-title":"Nat. Sustain."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2013.01.010","article-title":"Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements","volume":"132","author":"Hmimina","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.gloplacha.2013.06.012","article-title":"NDVI-based vegetation changes and their responses to climate change from 1982 to 2011: A case study in the Koshi River Basin in the middle Himalayas","volume":"108","author":"Zhang","year":"2013","journal-title":"Glob. Planet. Chang."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"e01917","DOI":"10.1002\/ecs2.1917","article-title":"Present-day and future contribution of climate and fires to vegetation composition in the boreal forest of China","volume":"8","author":"Wu","year":"2017","journal-title":"Ecosphere"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"725427","DOI":"10.1155\/2015\/725427","article-title":"NDVI Variation and Its Responses to Climate Change on the Northern Loess Plateau of China from 1998 to 2012","volume":"2015","author":"Ning","year":"2015","journal-title":"Adv. Meteorol."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Sol\u00f3rzano, J.V., and Gao, Y. (2022). Forest Disturbance Detection with Seasonal and Trend Model Components and Machine Learning Algorithms. Remote Sens., 14.","DOI":"10.3390\/rs14030803"},{"key":"ref_73","first-page":"345","article-title":"Completion of the 2011 National Land Cover Database for the conterminous United States\u2013representing a decade of land cover change information","volume":"81","author":"Homer","year":"2015","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"9299","DOI":"10.1073\/pnas.1504418112","article-title":"Evaporative cooling over the Tibetan Plateau induced by vegetation growth","volume":"112","author":"Shen","year":"2015","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1038\/nclimate3004","article-title":"Greening of the Earth and its drivers","volume":"6","author":"Zhu","year":"2016","journal-title":"Nat. Clim. Chang."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.ecolind.2015.09.041","article-title":"Multiple afforestation programs accelerate the greenness in the \u2018Three North\u2019 region of China from 1982 to 2013","volume":"61","author":"Zhang","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1610","DOI":"10.1111\/gcb.12807","article-title":"Ground- and satellite-based evidence of the biophysical mechanisms behind the greening Sahel","volume":"21","author":"Brandt","year":"2015","journal-title":"Glob. Chang. Biol."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1016\/j.rse.2010.10.011","article-title":"Analysis of monotonic greening and browning trends from global NDVI time-series","volume":"115","author":"Schaepman","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"124005","DOI":"10.1088\/1748-9326\/ab4ffc","article-title":"Increasing interannual variability of global vegetation greenness","volume":"14","author":"Chen","year":"2019","journal-title":"Environ. Res. Lett."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.rse.2018.05.018","article-title":"Increasing global vegetation browning hidden in overall vegetation greening: Insights from time-varying trends","volume":"214","author":"Pan","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"e2020EF001618","DOI":"10.1029\/2020EF001618","article-title":"Nearly Half of Global Vegetated Area Experienced Inconsistent Vegetation Growth in Terms of Greenness, Cover, and Productivity","volume":"8","author":"Ding","year":"2020","journal-title":"Earth\u2019s Future"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"e2020GL091496","DOI":"10.1029\/2020GL091496","article-title":"Where Are Global Vegetation Greening and Browning Trends Significant?","volume":"48","author":"Mahecha","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.rse.2014.02.003","article-title":"Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data","volume":"145","author":"Weng","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1007\/s12665-018-7951-z","article-title":"An efficient knowledge-based approach for random variation interpretation in NDVI time series","volume":"77","author":"Abbes","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1038\/nature06937","article-title":"Attributing physical and biological impacts to anthropogenic climate change","volume":"453","author":"Rosenzweig","year":"2008","journal-title":"Nature"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1111\/j.1466-8238.2010.00558.x","article-title":"Global patterns in the vulnerability of ecosystems to vegetation shifts due to climate change","volume":"19","author":"Gonzalez","year":"2010","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Bartkowiak, P., Castelli, M., and Notarnicola, C. (2019). Downscaling Land Surface Temperature from MODIS Dataset with Random Forest Approach over Alpine Vegetated Areas. Remote Sens., 11.","DOI":"10.3390\/rs11111319"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Tang, K., Zhu, H., and Ni, P. (2021). Spatial Downscaling of Land Surface Temperature over Heterogeneous Regions Using Random Forest Regression Considering Spatial Features. Remote Sens., 13.","DOI":"10.3390\/rs13183645"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Meng, X., Gao, X., Li, S., and Lei, J. (2020). Spatial and Temporal Characteristics of Vegetation NDVI Changes and the Driving Forces in Mongolia during 1982\u20132015. Remote Sens., 12.","DOI":"10.3390\/rs12040603"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"52277","DOI":"10.1007\/s11356-022-19502-6","article-title":"Spatiotemporal variation and driving forces of NDVI from 1982 to 2015 in the Qinba Mountains, China","volume":"29","author":"Zhang","year":"2022","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_91","first-page":"102640","article-title":"High-quality vegetation index product generation: A review of NDVI time series reconstruction techniques","volume":"105","author":"Li","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Yang, Y., Luo, J., Huang, Q., Wu, W., and Sun, Y. (2019). Weighted Double-Logistic Function Fitting Method for Reconstructing the High-Quality Sentinel-2 NDVI Time Series Data Set. Remote Sens., 11.","DOI":"10.3390\/rs11202342"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.agrformet.2012.12.003","article-title":"Assessing the differences in net primary productivity between pre- and post-urban land development in China","volume":"171","author":"Pei","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"145648","DOI":"10.1016\/j.scitotenv.2021.145648","article-title":"Quantifying the contributions of human activities and climate change to vegetation net primary productivity dynamics in China from 2001 to 2016","volume":"773","author":"Ge","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"46603","DOI":"10.1007\/s11356-020-10867-0","article-title":"An improved trend vegetation analysis for non-stationary NDVI time series based on wavelet transform","volume":"28","author":"Rhif","year":"2021","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Chen, Y., Fan, R., Bilal, M., Yang, X., Wang, J., and Li, W. (2018). Multilevel Cloud Detection for High-Resolution Remote Sensing Imagery Using Multiple Convolutional Neural Networks. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7050181"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"112381","DOI":"10.1016\/j.rse.2021.112381","article-title":"A review of geostatistical simulation models applied to satellite remote sensing: Methods and applications","volume":"259","author":"Zakeri","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"3209","DOI":"10.1109\/TGRS.2020.3008015","article-title":"Downscaling Multispectral Satellite Images Without Colocated High-Resolution Data: A Stochastic Approach Based on Training Images","volume":"59","author":"Oriani","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"W10507","DOI":"10.1029\/2012WR012115","article-title":"Spatiotemporal reconstruction of gaps in multivariate fields using the direct sampling approach","volume":"48","author":"Mariethoz","year":"2012","journal-title":"Water Resour. Res."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/S0034-4257(02)00044-5","article-title":"Downscaling of remotely sensed soil moisture with a modified fractal interpolation method using contraction mapping and ancillary data","volume":"83","author":"Kim","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1111\/j.1461-0248.2004.00568.x","article-title":"Uses and abuses of fractal methodology in ecology","volume":"7","author":"Halley","year":"2004","journal-title":"Ecol. Lett."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"096015","DOI":"10.1117\/1.JRS.9.096015","article-title":"Spatial scaling transformation modeling based on fractal theory for the leaf area index retrieved from remote sensing imagery","volume":"9","author":"Wu","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.jhydrol.2018.09.014","article-title":"Temporal downscaling rainfall and streamflow records through a deterministic fractal geometric approach","volume":"568","author":"Maskey","year":"2019","journal-title":"J. Hydrol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3639\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:59:21Z","timestamp":1760140761000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3639"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,29]]},"references-count":103,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14153639"],"URL":"https:\/\/doi.org\/10.3390\/rs14153639","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints202205.0367.v1","asserted-by":"object"}]},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,29]]}}}