{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:03:42Z","timestamp":1771023822416,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"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":["41401233"],"award-info":[{"award-number":["41401233"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["N160102001"],"award-info":[{"award-number":["N160102001"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The surface mining activities in grassland and rangeland zones directly affect the livestock production, forage quality, and regional grassland resources. Mine rehabilitation is necessary for accelerating the recovery of the grassland ecosystem. In this work, we investigate the integration of data obtained via a synthetic aperture radar (Sentinel-1 SAR) with data obtained by optical remote sensing (Worldview-3, WV-3) in order to monitor the conditions of a vegetation area rehabilitated after coal mining in North China. The above-ground biomass (AGB) is used as an indicator of the rehabilitated vegetation conditions and the success of mine rehabilitation. The wavelet principal component analysis is used for the fusion of the WV-3 and Sentinel-1 SAR images. Furthermore, a multiple linear regression model is applied based on the relationship between the remote sensing features and the AGB field measurements. Our results show that WV-3 enhanced vegetation indices (EVI), mean texture from band8 (near infrared band2, NIR2), the SAR vertical and horizon (VH) polarization, and band 8 (NIR2) from the fused image have higher correlation coefficient value with the field-measured AGB. The proposed AGB estimation model combining WV-3 and Sentinel 1A SAR imagery yields higher model accuracy (R2 = 0.79 and RMSE = 22.82 g\/m2) compared to that obtained with any of the two datasets only. Besides improving AGB estimation, the proposed model can also reduce the uncertainty range by 7 g m\u22122 on average. These results demonstrate the potential of new multispectral high-resolution datasets, such as Sentinel-1 SAR and Worldview-3, in providing timely and accurate AGB estimation for mine rehabilitation planning and management.<\/jats:p>","DOI":"10.3390\/rs11232855","type":"journal-article","created":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T10:50:45Z","timestamp":1575283845000},"page":"2855","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery"],"prefix":"10.3390","volume":"11","author":[{"given":"Nisha","family":"Bao","sequence":"first","affiliation":[{"name":"School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China"},{"name":"Science and Technology Innovation Center of Smart Water and Resource Environment, Northeastern University, Shenyang 110819, China"}]},{"given":"Wenwen","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85281, USA"}]},{"given":"Xiaowei","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China"},{"name":"Science and Technology Innovation Center of Smart Water and Resource Environment, Northeastern University, Shenyang 110819, China"}]},{"given":"Yanhui","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105475","DOI":"10.1016\/j.ecolind.2019.105475","article-title":"Drivers of spatio-temporal ecological vulnerability in an arid, coal mining region in Western China","volume":"106","author":"Lv","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.coal.2010.12.009","article-title":"Remote sensing of vegetation health for reclaimed areas of Seyit\u00f6mer open cast coal mine","volume":"86","author":"Erener","year":"2011","journal-title":"Int. J. Coal Geol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2455","DOI":"10.1016\/j.biombioe.2011.02.028","article-title":"A review of remote sensing methods for biomass feedstock production","volume":"35","author":"Ahamed","year":"2011","journal-title":"Biomass Bioenergy"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.rse.2008.08.012","article-title":"Changes in the extent of surface mining and reclamation in the Central Appalachians detected using a 1976\u20132006 Landsat time series","volume":"113","author":"Townsend","year":"2009","journal-title":"Remote. Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.geoderma.2016.10.033","article-title":"Assessing soil organic matter of reclaimed soil from a large surface coal mine using a field spectroradiometer in laboratory","volume":"288","author":"Bao","year":"2017","journal-title":"Geoderma"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1080\/01431160500486732","article-title":"The potential and challenge of remote sensing-based biomass estimation","volume":"27","author":"Lu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/S2095-3119(18)62016-7","article-title":"Research advances of SAR remote sensing for agriculture applications: A review","volume":"18","author":"Liu","year":"2019","journal-title":"J. Integr. Agric."},{"key":"ref_8","first-page":"12","article-title":"Integration of optical and synthetic aperture radar (SAR) images to differentiate grassland and alfalfa in Prairie area","volume":"28","author":"Hong","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.isprsjprs.2008.07.006","article-title":"Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories","volume":"64","author":"McNairn","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","first-page":"1","article-title":"Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data","volume":"53","author":"Zhao","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5201","DOI":"10.1080\/01431160412331270803","article-title":"A review of satellite and airborne sensors for remote sensing based detection of minefields and landmines","volume":"25","author":"Maathuis","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1080\/10106049.2011.628761","article-title":"Use of high-resolution satellite imagery for investigating acid mine drainage from artisanal coal mining in North-Eastern India","volume":"27","author":"Blahwar","year":"2012","journal-title":"Geocarto Int."},{"key":"ref_13","first-page":"100246","article-title":"Performances of WorldView 3, Sentinel 2, and Landsat 8 data in mapping impervious surface","volume":"15","author":"Xian","year":"2019","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lukin, V., Rubel, O., and Kozhemiakin, R. (2018). Despeckling of Multitemporal Sentinel SAR Images and Its Impact on Agricultural Area Classification, IntechOpen.","DOI":"10.5772\/intechopen.72577"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2018.2885506","article-title":"Achieving Super-Resolution Remote Sensing Images via the Wavelet Transform Combined With the Recursive Res-Net","volume":"57","author":"Ma","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","first-page":"14","article-title":"A POCS Super-Resolution Image Reconstruction based on the Projection Residue","volume":"8349","author":"Luo","year":"2011","journal-title":"Proc. SPIE Int. Soc. Opt. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1016\/j.jvcir.2006.02.002","article-title":"A POCS-based constrained total least squares algorithm for image restoration","volume":"17","author":"Gan","year":"2006","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_18","first-page":"99","article-title":"Fusion of TerraSAR-x and Landsat ETM+ data for protected area mapping in Uganda","volume":"38","author":"Otukei","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.ecolind.2016.09.029","article-title":"Comparison of object-based and pixel-based Random Forest algorithm for wetland vegetation mapping using high spatial resolution GF-1 and SAR data","volume":"73","author":"Fu","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_20","unstructured":"Rouse, J.W., Hass, R.H., Schell, J.A., and Deering, D.W. (1973, January 10\u201314). Monitoring vegetation systems in the great plains with ERTS. Proceedings of the Third Earth Resources Technology Satellite-1 Symposium, Washington, DC, USA."},{"key":"ref_21","first-page":"1541","article-title":"Distinguishing vegetation from soil background information","volume":"43","author":"Richardson","year":"1977","journal-title":"Eng. Remote Sens."},{"key":"ref_22","unstructured":"Pearson, R.L., and Miller, L.D. (1972, January 2\u20136). Remote Mapping of Standing Crop Biomass for Estimation of the Productivity of the Short-Grass Prairie. Proceedings of the 8th International Symposium on Remote Sensing of Environment, Ann Arbor, MI, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/36.134076","article-title":"Atmospherically resistant vegetation index (ARVI) for EOS-MODIS","volume":"30","author":"Kaufman","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1109\/TGRS.1995.8746027","article-title":"A feedback based modification of the NDVI to minimize canopy background and atmospheric noise","volume":"33","author":"Liu","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/S0034-4257(02)00010-X","article-title":"Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages","volume":"81","author":"Sims","year":"2002","journal-title":"Remote Sens Environ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, D., Wan, B., Qiu, P., Su, Y., and Wu, X. (2018). Evaluating the Performance of Sentinel-2, Landsat 8 and Pl\u00e9iades-1 in Mapping Mangrove Extent and Species. Remote Sens., 10.","DOI":"10.3390\/rs10091468"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1016\/j.rse.2018.11.031","article-title":"dPEN: Deep Progressively Expanded Network for mapping heterogeneous agricultural landscape using WorldView-3 satellite imagery","volume":"221","author":"Sidike","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_29","unstructured":"Marshall, V., Lewis, M., and Ostendorf, B. (September, January 25). Do additional bands (Coastal, NIR-2, Red-Edge and Yellow) in worldview-2 multispectral imagery improve discrimination of an invasive Tussock, Buffel Grass (Cenchrus Ciliaris)?. Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, Melbourne, Australia."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3331","DOI":"10.1080\/01431160310001654365","article-title":"Estimating winter wheat plant water content using red edge parameters","volume":"25","author":"Liu","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.rse.2018.12.032","article-title":"Assessment of red-edge vegetation indices for crop leaf area index estimation","volume":"222","author":"Dong","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.isprsjprs.2017.10.011","article-title":"Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine","volume":"134","author":"Maimaitijiang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","first-page":"318","article-title":"Performance of vegetation indices from Landsat time series in deforestation monitoring","volume":"52","author":"Schultz","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_34","first-page":"70","article-title":"Comparison of different reflectance indices for vegetation analysis using Landsat-TM data","volume":"12","author":"Kumar","year":"2018","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.rse.2012.01.003","article-title":"Image texture as a remotely sensed measure of vegetation structure","volume":"121","author":"Wood","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_37","first-page":"65","article-title":"Mapping forest aboveground biomass in the reforested Buffelsdraai landfill site using texture combinations computed from SPOT-6 pan-sharpened imagery","volume":"74","author":"Hlatshwayo","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1109\/LGRS.2010.2055830","article-title":"Rice crop monitoring in South China with RADARSAT-2 quad-polarization SAR data","volume":"8","author":"Fan","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2019.03.016","article-title":"Estimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B, multispectral Sentinel-2A, and DEM imagery","volume":"151","author":"Liu","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","first-page":"126","article-title":"High resolution multisensor fusion of SAR, optical and LiDAR data based on crisp vs. fuzzy and feature vs. decision ensemble systems","volume":"52","author":"Bigdeli","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_41","first-page":"118","article-title":"Estimating above ground biomass as an indicator of carbon storage in vegetated wetlands of the grassland biome of South Africa","volume":"78","author":"Naidoo","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.isprsjprs.2019.06.007","article-title":"Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images","volume":"154","author":"Wang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_43","first-page":"776","article-title":"A review on biomass estimation methods using synthetic aperture radar data","volume":"1","author":"GhasemiMahmod","year":"2011","journal-title":"Int. J. Ofgeomatics Geosci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.rse.2017.12.011","article-title":"Quantifying the relative contributions of vegetation and soil moisture conditions to polarimetric C-Band SAR response in a temperate peatland","volume":"206","author":"Millard","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"104190","DOI":"10.1016\/j.catena.2019.104190","article-title":"Predicting particle-size distribution using thermal infrared spectroscopy from reclaimed mine land in the semi-arid grassland of North China","volume":"183","author":"Bao","year":"2019","journal-title":"CATENA"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chang, J., and Shoshany, M. (2016, January 10\u201315). Mediterranean shrublands biomass estimation using Sentinel-1 and Sentinel-2. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7730380"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"647","DOI":"10.3390\/rs8080647","article-title":"Mapping Above-Ground Biomass by Integrating Optical and SAR Imagery: A Case Study of Xixi National Wetland Park, China","volume":"8","author":"Chudong","year":"2016","journal-title":"Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"063588","DOI":"10.1117\/1.JRS.6.063588","article-title":"Aboveground biomass estimation of tropical forest from Envisat advanced synthetic aperture radar data using modeling approach","volume":"6","author":"Kumar","year":"2012","journal-title":"J. Appl. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.1007\/s13762-015-0750-0","article-title":"A review of radar remote sensing for biomass estimation","volume":"12","author":"Sinha","year":"2015","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Vafaei, S., Soosani, J., Adeli, K., Fadaei, H., Naghavi, H., Pham, T., and Tien Bui, D. (2018). Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran). Remote Sens., 10.","DOI":"10.3390\/rs10020172"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1109\/36.295053","article-title":"Mapping biomass of a northern forest using multifrequency SAR data","volume":"32","author":"Ranson","year":"1994","journal-title":"Geosci. Remote Sens. IEEE Trans."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/23\/2855\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:39:04Z","timestamp":1760189944000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/23\/2855"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,1]]},"references-count":51,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["rs11232855"],"URL":"https:\/\/doi.org\/10.3390\/rs11232855","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,1]]}}}