{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T14:05:50Z","timestamp":1773324350355,"version":"3.50.1"},"reference-count":81,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T00:00:00Z","timestamp":1665705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31860145"],"award-info":[{"award-number":["31860145"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["[2020]TG06"],"award-info":[{"award-number":["[2020]TG06"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2020BS05"],"award-info":[{"award-number":["2020BS05"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Special Funds of The Central Government","award":["31860145"],"award-info":[{"award-number":["31860145"]}]},{"name":"Special Funds of The Central Government","award":["[2020]TG06"],"award-info":[{"award-number":["[2020]TG06"]}]},{"name":"Special Funds of The Central Government","award":["2020BS05"],"award-info":[{"award-number":["2020BS05"]}]},{"name":"Talent Introduction Project of Xinjiang University","award":["31860145"],"award-info":[{"award-number":["31860145"]}]},{"name":"Talent Introduction Project of Xinjiang University","award":["[2020]TG06"],"award-info":[{"award-number":["[2020]TG06"]}]},{"name":"Talent Introduction Project of Xinjiang University","award":["2020BS05"],"award-info":[{"award-number":["2020BS05"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Vegetation coverage information is an important indicator of desert ecological environments. Accurately grasping vegetation coverage changes in desert areas can help in assessing the quality of ecosystems and maintaining their functions. Improving remote sensing methods to detect the vegetation coverage in areas of low vegetation coverage is an important challenge for the remote sensing of vegetation in deserts. In this study, based on the fusion of MOD09GA and MOD09GQ data, 2019\u20132021 low-altitude unmanned aerial vehicle (UAV) remote sensing data, and other factors (such as geographical, topographic, and meteorological factors), three types of inversion models for vegetation coverage were constructed: a multivariate parametric regression model, a support vector machine (SVM) regression model, and a back-propagation neural network (BPNN) regression model. The optimal model was then used to map the spatial distribution of vegetation coverage and its dynamic change in the Junggar Basin of Xinjiang, China, over 22 years (from 2000 to 2021). The results show that: (1) The correlation between enhanced vegetation index (EVI) obtained from image fusion and vegetation coverage in desert areas is the highest (r = 0.72). (2) Among the geographical and topographic factors, only longitude and latitude were significantly correlated with vegetation coverage (p &lt; 0.05). The average monthly temperature and precipitation from the previous six months were correlated with the vegetation coverage (p &lt; 0.05), but the vegetation coverage of the current month had the highest correlation with the average temperature (r = \u22120.27) and precipitation (r = 0.33) of the previous month. (3) Among the multivariate parametric models established by selecting the five aforementioned factors, the multiple linear regression model performed the best (R2 = 0.64). (4) The SVM regression model was superior to the other regression models (R2 = 0.80, mean squared error = 8.35%). (5) The average vegetation coverage in the desert area of the Junggar Basin was 7.36%, and from 2000\u20132021, the vegetation coverage in 54.59% of the desert area increased.<\/jats:p>","DOI":"10.3390\/rs14205146","type":"journal-article","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T03:43:58Z","timestamp":1665978238000},"page":"5146","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Vegetation Coverage in the Desert Area of the Junggar Basin of Xinjiang, China, Based on Unmanned Aerial Vehicle Technology and Multisource Data"],"prefix":"10.3390","volume":"14","author":[{"given":"Yuhao","family":"Miao","sequence":"first","affiliation":[{"name":"College of Ecology and Environment, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renping","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Ecology and Environment, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Guo","sequence":"additional","affiliation":[{"name":"Xinjiang Academy Forestry, Urumqi 830000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuhua","family":"Yi","sequence":"additional","affiliation":[{"name":"Institute of Fragile Ecosystem and Environment, School of Geographic Sciences, Nantong University, 999 Tongjing Road, Nantong 226007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5732-0094","authenticated-orcid":false,"given":"Baoping","family":"Meng","sequence":"additional","affiliation":[{"name":"Institute of Fragile Ecosystem and Environment, School of Geographic Sciences, Nantong University, 999 Tongjing Road, Nantong 226007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaqing","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Ecology and Environment, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.ecolind.2011.08.011","article-title":"Trend analysis of vegetation dynamics in Qinghai\u2013Tibet Plateau using Hurst Exponent","volume":"14","author":"Peng","year":"2012","journal-title":"Ecol. Indic."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.gloplacha.2018.06.005","article-title":"Temporal-spatial variations and influencing factors of vegetation cover in Xinjiang from 1982 to 2013 based on GIMMS-NDVI3g","volume":"169","author":"Liu","year":"2018","journal-title":"Glob. Planet. Change"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Maturino, A., Mart\u00ednez-Guerrero, J.H., Chairez-Hern\u00e1ndez, I., Pereda-Solis, M.E., Villarreal-Guerrero, F., Renteria-Villalobos, M., and Pinedo-Alvarez, A. (2017). Mapping land cover and estimating the grassland structure in a priority area of the Chihuahuan desert. Land, 6.","DOI":"10.3390\/land6040070"},{"key":"ref_4","first-page":"747","article-title":"Effects of climate change on vegetations on Qinghai-Tibet Plateau: A review","volume":"28","author":"Yu","year":"2009","journal-title":"Chin. J. Ecol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/S0034-4257(01)00289-9","article-title":"Novel algorithms for remote estimation of vegetation fraction","volume":"80","author":"Gitelson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_6","first-page":"163","article-title":"Review of vegetation covering and its measuring and calculating method","volume":"34","author":"Wei","year":"2006","journal-title":"J. Northwest Sci.-Tech. Univ. Agric. For."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/j.ecoleng.2015.04.022","article-title":"Impacts of climate change and human activities on vegetation cover in hilly southern China","volume":"81","author":"Wang","year":"2015","journal-title":"Ecol. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s11430-007-0137-2","article-title":"Spatiotemporal variations of vegetation cover on the Chinese Loess Plateau (1981\u20132006): Impacts of climate changes and human activities","volume":"51","author":"Xin","year":"2008","journal-title":"Sci. China Ser. D Earth Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.quaint.2013.08.032","article-title":"Climate change and the ecological responses in Xinjiang, China: Model simulations and data analyses","volume":"311","author":"Fang","year":"2013","journal-title":"Quat. Int."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.catena.2013.11.020","article-title":"Effects of ecological restoration projects on land use and land cover change and its influences on territorial NPP in Xinjiang, China","volume":"115","author":"Yang","year":"2014","journal-title":"Catena"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/0034-4257(86)90012-X","article-title":"Sample size for ground and remotely sensed data","volume":"20","author":"Curran","year":"1986","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.rse.2016.02.019","article-title":"Fractional vegetation cover estimation algorithm for Chinese GF-1 wide field view data","volume":"177","author":"Jia","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1435","DOI":"10.1080\/01431168608948946","article-title":"Analysis of the dynamics of African vegetation using the normalized difference vegetation index","volume":"7","author":"Townshend","year":"1986","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","first-page":"49","article-title":"Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas","volume":"14","author":"Barati","year":"2011","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.rse.2011.12.004","article-title":"Landsat remote sensing approaches for monitoring long-term tree cover dynamics in semi-arid woodlands: Comparison of vegetation indices and spectral mixture analysis","volume":"119","author":"Yang","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zeng, L., Wardlow, B.D., Hu, S., Zhang, X., Zhou, G., Peng, G., Xiang, D., Wang, R., Meng, R., and Wu, W. (2021). A novel strategy to reconstruct NDVI time-series with high temporal resolution from MODIS multi-temporal composite products. Remote Sens., 13.","DOI":"10.3390\/rs13071397"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1016\/j.proenv.2010.10.108","article-title":"An assessment of correlation on MODIS-NDVI and EVI with natural vegetation coverage in Northern Hebei Province, China","volume":"2","author":"Li","year":"2010","journal-title":"Procedia Environ. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5270","DOI":"10.3390\/s8095270","article-title":"Deriving vegetation dynamics of natural terrestrial ecosystems from MODIS NDVI\/EVI data over Turkey","volume":"8","author":"Evrendilek","year":"2008","journal-title":"Sensors"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.1016\/S0273-1177(97)00248-2","article-title":"Extraction of vegetation cover in an arid area based on satellite data","volume":"19","author":"Ishiyama","year":"1997","journal-title":"Adv. Space Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.ecolind.2018.06.029","article-title":"Suitability of NDVI and OSAVI as estimators of green biomass and coverage in a semi-arid rangeland","volume":"94","author":"Fern","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3519","DOI":"10.1080\/014311698213795","article-title":"Relationships between percent vegetation cover and vegetation indices","volume":"19","author":"Purevdorj","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1016\/j.rse.2017.10.011","article-title":"Modeling grassland above-ground biomass based on artificial neural network and remote sensing in the Three-River Headwaters Region","volume":"204","author":"Yang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.rse.2018.09.019","article-title":"Modeling alpine grassland cover based on MODIS data and support vector machine regression in the headwater region of the Huanghe River, China","volume":"218","author":"Ge","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Meng, B., Gao, J., Liang, T., Cui, X., Ge, J., Yin, J., Feng, Q., and Xie, H. (2018). Modeling of alpine grassland cover based on unmanned aerial vehicle technology and multi-factor methods: A case study in the east of Tibetan Plateau, China. Remote Sens., 10.","DOI":"10.3390\/rs10020320"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1186\/s13007-021-00796-5","article-title":"Improving the estimation of alpine grassland fractional vegetation cover using optimized algorithms and multi-dimensional features","volume":"17","author":"Lin","year":"2021","journal-title":"Plant Methods"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/S0034-4257(02)00079-2","article-title":"Towards an operational MODIS continuous field of percent tree cover algorithm: Examples using AVHRR and MODIS data","volume":"83","author":"Hansen","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1016\/j.rse.2007.07.023","article-title":"Use of a dark object concept and support vector machines to automate forest cover change analysis","volume":"112","author":"Huang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"111716","DOI":"10.1016\/j.rse.2020.111716","article-title":"Deep learning in environmental remote sensing: Achievements and challenges","volume":"241","author":"Yuan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1922","DOI":"10.1080\/01431161.2016.1165884","article-title":"Improving estimates of fractional vegetation cover based on UAV in alpine grassland on the Qinghai\u2013Tibetan Plateau","volume":"37","author":"Chen","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, H., Sun, Y., Chang, L., Qin, Y., Chen, J., Qin, Y., Du, J., Yi, S., and Wang, Y. (2018). Estimation of grassland canopy height and aboveground biomass at the quadrat scale using unmanned aerial vehicle. Remote Sens., 10.","DOI":"10.3390\/rs10060851"},{"key":"ref_31","first-page":"e01517","article-title":"Using UAVs to assess the relationship between alpine meadow bare patches and disturbance by pikas in the source region of Yellow River on the Qinghai-Tibetan Plateau","volume":"26","author":"Zhang","year":"2021","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, X., Yuan, Y., Zhu, Z., Ma, Q., Yu, H., Li, M., Ma, J., Yi, S., He, X., and Sun, Y. (2021). Predicting the Distribution of Oxytropis ochrocephala Bunge in the Source Region of the Yellow River (China) Based on UAV Sampling Data and Species Distribution Model. Remote Sens., 13.","DOI":"10.3390\/rs13245129"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kaivosoja, J., Hautsalo, J., Heikkinen, J., Hiltunen, L., Ruuttunen, P., N\u00e4si, R., Niemel\u00e4inen, O., Lemsalu, M., Honkavaara, E., and Salonen, J. (2021). Reference measurements in developing UAV Systems for detecting pests, weeds, and diseases. Remote Sens., 13.","DOI":"10.3390\/rs13071238"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Abdollahnejad, A., Panagiotidis, D., Surov\u00fd, P., and Modlinger, R. (2021). Investigating the Correlation between Multisource Remote Sensing Data for Predicting Potential Spread of Ips typographus L. Spots in Healthy Trees. Remote Sens., 13.","DOI":"10.3390\/rs13234953"},{"key":"ref_35","first-page":"1181189","article-title":"Image Fusion and Stylization Processing Based on Multiscale Transformation and Convolutional Neural Network","volume":"2022","author":"Xu","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2250","DOI":"10.1109\/TGRS.2012.2208467","article-title":"Fusion of MODIS images using kriging with external drift","volume":"51","author":"Sales","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Monsalve-Tellez, J.M., Torres-Le\u00f3n, J.L., and Garc\u00e9s-G\u00f3mez, Y.A. (2022). Evaluation of SAR and Optical Image Fusion Methods in Oil Palm Crop Cover Classification Using the Random Forest Algorithm. Agriculture, 12.","DOI":"10.3390\/agriculture12070955"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"19","DOI":"10.5721\/EuJRS20144702","article-title":"Spectral and spatial quality analysis of pan-sharpening algorithms: A case study in Istanbul","volume":"47","author":"Sarp","year":"2014","journal-title":"Eur. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Liu, Q. (2018, January 28\u201330). Sharpening the WBSI imagery of Tiangong-II: Gram-Schmidt and principal components transform in comparison. Proceedings of the 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Huangshan, China.","DOI":"10.1109\/FSKD.2018.8687270"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yang, J., Ren, G., Ma, Y., and Fan, Y. (2016, January 10\u201315). Coastal wetland classification based on high resolution SAR and optical image fusion. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729224"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"867","DOI":"10.46488\/NEPT.2022.v21i02.050","article-title":"Adopting Gram-Schmidt and Brovey Methods for Estimating Land Use and Land Cover Using Remote Sensing and Satellite Images","volume":"21","author":"Hashim","year":"2022","journal-title":"Nat. Environ. Pollut. Technol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.chnaes.2016.01.003","article-title":"Spatio-temporal distribution pattern of vegetation coverage in Junggar Basin, Xinjiang","volume":"36","author":"Cheng","year":"2016","journal-title":"Acta Ecol. Sin."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Xie, C., Wu, S., Zhuang, Q., Zhang, Z., Hou, G., Luo, G., and Hu, Z. (2022). Where Anthropogenic Activity Occurs, Anthropogenic Activity Dominates Vegetation Net Primary Productivity Change. Remote Sens., 14.","DOI":"10.3390\/rs14051092"},{"key":"ref_44","first-page":"134","article-title":"Multivariate characterization of vegetation in Junnger basin","volume":"13","author":"Jun","year":"2005","journal-title":"Acta Agrestia Sin."},{"key":"ref_45","first-page":"364","article-title":"Temperature Regulation Effect of Desert Vegetation in Minqin Desert Area","volume":"8","author":"Chang","year":"2016","journal-title":"Anim. Husb. Feed Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e10650","DOI":"10.7717\/peerj.10650","article-title":"Response of net primary productivity to grassland phenological changes in Xinjiang, China","volume":"9","author":"Zhang","year":"2021","journal-title":"PeerJ"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2686","DOI":"10.1080\/01431161.2016.1253898","article-title":"FragMAP: A tool for long-term and cooperative monitoring and analysis of small-scale habitat fragmentation using an unmanned aerial vehicle","volume":"38","author":"Yi","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Tang, L., He, M., and Li, X. (2020). Verification of fractional vegetation coverage and NDVI of desert vegetation via UAVRS technology. Remote Sens., 12.","DOI":"10.3390\/rs12111742"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1080\/01431168608948944","article-title":"Satellite remote sensing of primary production","volume":"7","author":"Tucker","year":"1986","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/0034-4257(94)90018-3","article-title":"Development of vegetation and soil indices for MODIS-EOS","volume":"49","author":"Huete","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/S0034-4257(97)00114-4","article-title":"The sensitivity of the OSAVI vegetation index to observational parameters","volume":"63","author":"Steven","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A modified soil adjusted vegetation index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Luo, N., Mao, D., Wen, B., and Liu, X. (2020). Climate change affected vegetation dynamics in the northern Xinjiang of China: Evaluation by SPEI and NDVI. Land, 9.","DOI":"10.3390\/land9030090"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Yang, L., Jia, K., Liang, S., Liu, J., and Wang, X. (2016). Comparison of four machine learning methods for generating the GLASS fractional vegetation cover product from MODIS data. Remote Sens., 8.","DOI":"10.3390\/rs8080682"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1109\/LGRS.2006.871748","article-title":"Robust support vector regression for biophysical variable estimation from remotely sensed images","volume":"3","author":"Bruzzone","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.rse.2012.12.027","article-title":"GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production","volume":"137","author":"Baret","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1886","DOI":"10.1016\/j.rse.2009.04.004","article-title":"Evaluation of earth observation based long term vegetation trends\u2014Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data","volume":"113","author":"Fensholt","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1071\/RJ14061","article-title":"Analysis of vegetation change associated with human disturbance using MODIS data on the rangelands of the Qinghai-Tibet Plateau","volume":"37","author":"Zhao","year":"2015","journal-title":"Rangel. J."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2377","DOI":"10.1080\/01431160903002409","article-title":"Comparison and conversion of AVHRR GIMMS and SPOT VEGETATION NDVI data in China","volume":"31","author":"Song","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Lin, H., Zhao, Y., and Kalhoro, G.M. (2022). Ecological Response of the Subsidy and Incentive System for Grassland Conservation in China. Land, 11.","DOI":"10.3390\/land11030358"},{"key":"ref_62","first-page":"693","article-title":"Analysis on response of vegetation index to climate change and its prediction in the three-rivers-source region","volume":"38","author":"Zhu","year":"2019","journal-title":"Plateau Meteorol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.rse.2016.01.002","article-title":"The spatiotemporal patterns of vegetation coverage and biomass of the temperate deserts in Central Asia and their relationships with climate controls","volume":"175","author":"Zhang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/0034-4257(93)90049-4","article-title":"Reflectance of vegetation and soil in Chihuahuan desert plant communities from ground radiometry using SPOT wavebands","volume":"46","author":"Franklin","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/S0034-4257(99)00112-1","article-title":"Hyperspectral mixture modeling for quantifying sparse vegetation cover in arid environments","volume":"72","author":"McGwire","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"083630","DOI":"10.1117\/1.JRS.8.083630","article-title":"Deriving vegetation fraction information for the alpine grassland on the Tibetan plateau using in situ spectral data","volume":"8","author":"Liu","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"7597","DOI":"10.3390\/rs70607597","article-title":"Evaluation of three MODIS-derived vegetation index time series for dryland vegetation dynamics monitoring","volume":"7","author":"Lu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Yu, Y., Pan, Y., Yang, X., and Fan, W. (2022). Spatial Scale Effect and Correction of Forest Aboveground Biomass Estimation Using Remote Sensing. Remote Sens., 14.","DOI":"10.3390\/rs14122828"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Quan, Y., Tong, Y., Feng, W., Dauphin, G., Huang, W., and Xing, M. (2020). A novel image fusion method of multi-spectral and sar images for land cover classification. Remote Sens., 12.","DOI":"10.3390\/rs12223801"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1080\/15481603.2019.1627062","article-title":"Landsat-MODIS image fusion and object-based image analysis for observing flood inundation in a heterogeneous vegetated scene","volume":"56","author":"Dao","year":"2019","journal-title":"GIScience Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Cao, L., Liu, T., and Wei, L. (2013, January 22\u201326). A comparison of multi-resource remote sensing data for vegetation indices. Proceedings of the IOP Conference Series: Earth and Environmental Science, Beijing, China.","DOI":"10.1088\/1755-1315\/17\/1\/012067"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.rse.2006.02.004","article-title":"Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands","volume":"102","author":"Soudani","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Shi, Y., Wang, Z., Liu, L., Li, C., Peng, D., and Xiao, P. (2021). Improving Estimation of Woody Aboveground Biomass of Sparse Mixed Forest over Dryland Ecosystem by Combining Landsat-8, GaoFen-2, and UAV Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13234859"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Tian, H., Wang, Y., Chen, T., Zhang, L., and Qin, Y. (2021). Early-Season Mapping of Winter Crops Using Sentinel-2 Optical Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13193822"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, Z., Feng, L., Du, Q., and Runge, T. (2020). Combining multi-source data and machine learning approaches to predict winter wheat yield in the conterminous United States. Remote Sens., 12.","DOI":"10.3390\/rs12081232"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"108081","DOI":"10.1016\/j.ecolind.2021.108081","article-title":"The use of machine learning methods to estimate aboveground biomass of grasslands: A review","volume":"130","author":"Morais","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"13596","DOI":"10.1002\/ece3.5817","article-title":"Responses of four dominant dryland plant species to climate change in the Junggar Basin, northwest China","volume":"9","author":"Xiao","year":"2019","journal-title":"Ecol. Evol."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Xue, J., Wang, Y., Teng, H., Wang, N., Li, D., Peng, J., Biswas, A., and Shi, Z. (2021). Dynamics of Vegetation Greenness and Its Response to Climate Change in Xinjiang over the Past Two Decades. Remote Sens., 13.","DOI":"10.3390\/rs13204063"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s11707-013-0390-y","article-title":"The impacts of climate change and human activities on grassland productivity in Qinghai Province, China","volume":"8","author":"Yin","year":"2014","journal-title":"Front. Earth Sci."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"2888","DOI":"10.1038\/s41598-018-21089-3","article-title":"Grassland dynamics in response to climate change and human activities in Xinjiang from 2000 to 2014","volume":"8","author":"Zhang","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.catena.2019.03.029","article-title":"Oasification: An unable evasive process in fighting against desertification for the sustainable development of arid and semiarid regions of China","volume":"179","author":"Xue","year":"2019","journal-title":"Catena"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5146\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:54:26Z","timestamp":1760144066000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5146"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,14]]},"references-count":81,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14205146"],"URL":"https:\/\/doi.org\/10.3390\/rs14205146","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,14]]}}}