{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T07:37:41Z","timestamp":1775029061712,"version":"3.50.1"},"reference-count":125,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T00:00:00Z","timestamp":1681257600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Water Research Commission of South Africa","award":["WRC2020\/2021-00490"],"award-info":[{"award-number":["WRC2020\/2021-00490"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A third of the land on the Earth is composed of grasslands, mainly used for forage. Much effort is being conducted to develop tools to estimate grassland productivity (GP) at different extents, concentrating on spatial and seasonal variability pertaining to climate change. GP is a reliable indicator of how well an ecosystem works because of its close connection to the ecological system equilibrium. The most commonly used proxies of GP in ecological studies are aboveground biomass (AGB), leaf area index (LAI), canopy storage capacity (CSC), and chlorophyll and nitrogen content. Grassland science gains much information from the capacity of remote sensing (RS) techniques to calculate GP proxies. An overview of the studies on RS-based GP prediction techniques and a discussion of current matters determining GP monitoring are critical for improving future GP prediction performance. A systematic review of articles published between 1970 and October 2021 (203 peer-reviewed articles from Web of Science, Scopus, and DirectScience databases) showed a trend in the choice of the sensors, and the approaches to use are largely dependent on the extent of monitoring and assessment. Notably, all the reviewed articles demonstrate the growing demand for high-resolution sensors, such as hyperspectral scanners and computationally efficient image-processing techniques for the high prediction accuracy of GP at various scales of application. Further research is required to attract the synthesis of optical and radar data, multi-sensor data, and the selection of appropriate techniques for GP prediction at different scales. Mastering and listing major uncertainties associated with different algorithms for the GP prediction and pledging to reduce these errors are critical.<\/jats:p>","DOI":"10.3390\/rs15082043","type":"journal-article","created":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T01:35:00Z","timestamp":1681349700000},"page":"2043","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Remote Sensing Grassland Productivity Attributes: A Systematic Review"],"prefix":"10.3390","volume":"15","author":[{"given":"Tsitsi","family":"Bangira","sequence":"first","affiliation":[{"name":"Centre for Transformative Agricultural and Food Systems, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal (UKZN), Scottsville, Pietermaritzburg 3209, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7358-8111","authenticated-orcid":false,"given":"Onisimo","family":"Mutanga","sequence":"additional","affiliation":[{"name":"Discipline of Geography and Environmental Science, School of Agricultural Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg 3209, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4589-7099","authenticated-orcid":false,"given":"Mbulisi","family":"Sibanda","sequence":"additional","affiliation":[{"name":"Department of Geography, Environmental Studies & Tourism, Faculty of Arts, University of the Western Cape, Bellville 7535, South Africa"}]},{"given":"Timothy","family":"Dube","sequence":"additional","affiliation":[{"name":"Institute of Water Studies, Department of Earth Sciences, University of the Western Cape, Private Bag X17, Bellville 7535, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9323-8127","authenticated-orcid":false,"given":"Tafadzwanashe","family":"Mabhaudhi","sequence":"additional","affiliation":[{"name":"Centre for Transformative Agricultural and Food Systems, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal (UKZN), Scottsville, Pietermaritzburg 3209, South Africa"},{"name":"International Water Management Institute (IWMI), Pretoria 0127, South Africa"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3070","DOI":"10.3390\/s150203070","article-title":"Some Insights on Grassland Health Assessment Based on Remote Sensing","volume":"15","author":"Xu","year":"2015","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.jnc.2012.02.001","article-title":"Assessment of grassland use intensity by remote sensing to support conservation schemes","volume":"20","author":"Franke","year":"2012","journal-title":"J. Nat. Conserv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1093\/jpe\/rtw005","article-title":"Satellite remote sensing of grasslands: From observation to management","volume":"9","author":"Ali","year":"2016","journal-title":"J. Plant. Ecol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e02582","DOI":"10.1002\/ecs2.2582","article-title":"Grasslands\u2014More important for ecosystem services than you might think","volume":"10","author":"Bengtsson","year":"2019","journal-title":"Ecosphere"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1111\/j.1469-8137.2004.01201.x","article-title":"Carbon sequestration in temperate grassland ecosystems and the influence of management, climate and elevated CO2","volume":"164","author":"Jones","year":"2004","journal-title":"New Phytol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1046\/j.1365-2486.1997.00055.x","article-title":"Potential for carbon sequestration in European soils: Preliminary estimates for five scenarios using results from long-term experiments","volume":"3","author":"Smith","year":"1997","journal-title":"Glob. Change Biol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1016\/j.scitotenv.2018.12.101","article-title":"Impact of grassland degradation on the distribution and bioavailability of soil silicon: Implications for the Si cycle in grasslands","volume":"657","author":"Yang","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1038\/s43017-021-00207-2","article-title":"Combatting global grassland degradation","volume":"2","author":"Bardgett","year":"2021","journal-title":"Nat. Rev. Earth Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Th\u00e9au, J., Lauzier-Hudon, \u00c9., Aub\u00e9, L., and Devillers, N. (2021). Estimation of forage biomass and vegetation cover in grasslands using UAV imagery. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0245784"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Soubry, I., Doan, T., Chu, T., and Guo, X. (2021). A Systematic Review on the Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Health Attributes, Indicators, and Measures. Remote Sens., 13.","DOI":"10.3390\/rs13163262"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1080\/01431160512331326738","article-title":"Estimating tropical pasture quality at canopy level using band depth analysis with continuum removal in the visible domain","volume":"26","author":"Mutanga","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, Y., Guerschman, J., Shendryk, Y., Henry, D., and Harrison, M.T. (2021). Estimating Pasture Biomass Using Sentinel-2 Imagery and Machine Learning. Remote Sens., 13.","DOI":"10.3390\/rs13040603"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1111\/j.1365-2486.2006.01224.x","article-title":"Reduction of ecosystem productivity and respiration during the European summer 2003 climate anomaly: A joint flux tower, remote sensing and modelling analysis","volume":"13","author":"Reichstein","year":"2007","journal-title":"Glob. Change Biol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.rama.2018.10.005","article-title":"Quantitative estimation of biomass of alpine grasslands using hyperspectral remote sensing","volume":"72","author":"Kong","year":"2019","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2861","DOI":"10.1080\/01431161.2019.1697004","article-title":"Estimating natural grassland biomass by vegetation indices using Sentinel 2 remote sensing data","volume":"41","author":"Kuplich","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","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_17","first-page":"159","article-title":"A radiative transfer model-based method for the estimation of grassland aboveground biomass","volume":"54","author":"Quan","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","first-page":"43","article-title":"Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data","volume":"43","author":"Ramoelo","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"43","DOI":"10.2989\/10220119.2015.1043647","article-title":"Aboveground biomass production of a semi-arid southern African savanna: Towards a new model","volume":"33","author":"Palmer","year":"2016","journal-title":"Afr. J. Range Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/s11707-019-0780-x","article-title":"Modeling grass yields in Qinghai Province, China, based on MODIS NDVI data-an empirical comparison","volume":"14","author":"Liu","year":"2020","journal-title":"Front. Earth Sci.-Prc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"107450","DOI":"10.1016\/j.ecolind.2021.107450","article-title":"A method to avoid spatial overfitting in estimation of grassland aboveground biomass on the Tibetan Plateau","volume":"125","author":"Yu","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4843","DOI":"10.1080\/10106049.2021.1899309","article-title":"Mapping grass aboveground biomass of grazing-lands using satellite remote sensing","volume":"37","author":"Zumo","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yu, R.Y., Yao, Y.J., Wang, Q., Wan, H.W., Xie, Z.J., Tang, W.J., Zhang, Z.P., Yang, J.M., Shang, K., and Guo, X.Z. (2021). Satellite-Derived Estimation of Grassland Aboveground Biomass in the Three-River Headwaters Region of China during 1982-2018. Remote Sens., 13.","DOI":"10.3390\/rs13152993"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1111\/aje.12861","article-title":"Remote sensing of aboveground grass biomass between protected and non-protected areas in savannah rangelands","volume":"59","author":"Dube","year":"2021","journal-title":"Afr. J. Ecol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.1111\/jbi.12381","article-title":"Distribution mapping of world grassland types","volume":"41","author":"Dixon","year":"2014","journal-title":"J. Biogeogr."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1002\/eap.1473","article-title":"Grassland management impacts on soil carbon stocks: A new synthesis","volume":"27","author":"Conant","year":"2017","journal-title":"Ecol. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"43","DOI":"10.2989\/10220119.2020.1716072","article-title":"Grass community responses to drought in an African savanna","volume":"37","author":"Staver","year":"2020","journal-title":"Afr. J. Range Sci."},{"key":"ref_28","unstructured":"Gough, D., Oliver, S., and Thomas, J. (2017). An Introduction to Systematic Reviews, Sage."},{"key":"ref_29","first-page":"348","article-title":"Statistical bibliography or bibliometrics","volume":"25","author":"Pritchard","year":"1969","journal-title":"J. Doc."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, H., Huang, M., Qing, X., Li, G., and Tian, C. (2017). Bibliometric analysis of global remote sensing research during 2010\u20132015. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6110332"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s11192-009-0146-3","article-title":"Software survey: VOSviewer, a computer program for bibliometric mapping","volume":"84","author":"Waltman","year":"2010","journal-title":"Scientometrics"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G., and Group, P. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med., 6.","DOI":"10.1371\/journal.pmed.1000097"},{"key":"ref_33","first-page":"317","article-title":"Spectral Mapping of Shortgrass Prairie Biomass","volume":"41","author":"Pearson","year":"1976","journal-title":"Photogramm. Eng. Rem. S"},{"key":"ref_34","first-page":"1157","article-title":"Shortgrass prairie spectral measurements","volume":"41","author":"Tucker","year":"1975","journal-title":"Photogramm. Eng. Rem. S"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1023\/B:GEJO.0000026688.74589.58","article-title":"Biomass estimation using Landsat-TM and-ETM+. Towards a regional model for Southern Africa?","volume":"59","author":"Samimi","year":"2004","journal-title":"GeoJournal"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3619","DOI":"10.1080\/01431160110114529","article-title":"Estimating leaf nitrogen concentration in ryegrass (Lolium spp.) pasture using the chlorophyll red-edge: Theoretical modelling and experimental observations","volume":"23","author":"Lamb","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1080\/01431169408954174","article-title":"Estimating Grassland Biomass and Leaf-Area Index Using Ground and Satellite Data","volume":"15","author":"Friedl","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1016\/S0273-1177(03)90545-X","article-title":"Comparative analysis of red-edge hyperspectral indices","volume":"32","author":"Gupta","year":"2003","journal-title":"Adv. Space Res."},{"key":"ref_39","first-page":"399","article-title":"High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm","volume":"18","author":"Mutanga","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_40","first-page":"393","article-title":"Quantifying aboveground biomass in African environments: A review of the trade-offs between sensor estimation accuracy and costs","volume":"57","author":"Dube","year":"2016","journal-title":"Trop. Ecol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"44","DOI":"10.5589\/m06-001","article-title":"A comparison between Terra MODIS and NOAA AVHRR NDVI satellite image composites for the monitoring of natural grassland conditions in Alberta, Canada","volume":"32","author":"Crump","year":"2006","journal-title":"Can. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2016.08.001","article-title":"Progress in the remote sensing of C3 and C4 grass species aboveground biomass over time and space","volume":"120","author":"Shoko","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Matongera, T.N., Mutanga, O., Sibanda, M., and Odindi, J. (2021). Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges. Remote Sens., 13.","DOI":"10.3390\/rs13112060"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5313","DOI":"10.1080\/01431160802036276","article-title":"MODIS-based remote sensing monitoring of grass production in China","volume":"29","author":"Xu","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhang, C., Zhang, Y., Wang, Z., Li, J., and Odeh, I. (2019). Monitoring Phenology in the Temperate Grasslands of China from 1982 to 2015 and Its Relation to Net Primary Productivity. Sustainability, 12.","DOI":"10.3390\/su12010012"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"257","DOI":"10.3390\/rs6010257","article-title":"Phenological Metrics Derived over the European Continent from NDVI3g Data and MODIS Time Series","volume":"6","author":"Atzberger","year":"2014","journal-title":"Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"107859","DOI":"10.1016\/j.agrformet.2019.107859","article-title":"Detecting intra- and inter-annual variability in gross primary productivity of a North American grassland using MODIS MAIAC data","volume":"281","author":"Wang","year":"2020","journal-title":"Agric. For. Meteorol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.scitotenv.2016.02.106","article-title":"Estimating net primary production of natural grassland and its spatio-temporal distribution in China","volume":"553","author":"Zhang","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"106114","DOI":"10.1016\/j.ecolind.2020.106114","article-title":"Using the random forest model and validated MODIS with the field spectrometer measurement promote the accuracy of estimating aboveground biomass and coverage of alpine grasslands on the Qinghai-Tibetan Plateau","volume":"112","author":"Gao","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_50","first-page":"35","article-title":"MODIS EVI-based net primary production in the Sahel 2000\u20132014","volume":"65","author":"Tagesson","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"7796","DOI":"10.1080\/01431161.2013.823000","article-title":"Using MODIS time series data to estimate aboveground biomass and its spatio-temporal variation in Inner Mongolia\u2019s grassland between 2001 and 2011","volume":"34","author":"Gao","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.rse.2013.07.020","article-title":"Monitoring and modeling spatial and temporal patterns of grassland dynamics using time-series MODIS NDVI with climate and stocking data","volume":"138","author":"Li","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"El Hajj, M., Baghdadi, N., Bazzi, H., and Zribi, M. (2019). Penetration analysis of SAR signals in the C and L bands for wheat, maize, and grasslands. Remote Sens., 11.","DOI":"10.3390\/rs11010031"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4089","DOI":"10.1080\/01431160110115924","article-title":"SAR-based estimation of areal aboveground biomass (AAB) of herbaceous vegetation in the semi-arid zone: A modification of the water-cloud model","volume":"23","author":"Svoray","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"525","DOI":"10.5589\/m03-069","article-title":"The application of C-band polarimetric SAR for agriculture: A review","volume":"30","author":"McNairn","year":"2004","journal-title":"Can. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3611","DOI":"10.3390\/rs5073611","article-title":"Pasture Monitoring Using SAR with COSMO-SkyMed, ENVISAT ASAR, and ALOS PALSAR in Otway, Australia","volume":"5","author":"Wang","year":"2013","journal-title":"Remote Sens."},{"key":"ref_57","first-page":"102306","article-title":"The potential of sentinel-1 InSAR coherence for grasslands monitoring in Eastern Cape, South Africa","volume":"98","author":"Dubovyk","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_58","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_59","doi-asserted-by":"crossref","first-page":"10002","DOI":"10.3390\/rs61010002","article-title":"Irrigated grassland monitoring using a time series of TerraSAR-X and COSMO-skyMed X-Band SAR Data","volume":"6","author":"Hajj","year":"2014","journal-title":"Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/S0034-4257(01)00343-1","article-title":"Season-long daily measurements of multifrequency (Ka, Ku, X, C, and L) and full-polarization backscatter signatures over paddy rice field and their relationship with biological variables","volume":"81","author":"Inoue","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_61","first-page":"1","article-title":"Estimating the Leaf Area Index, height and biomass of maize using HJ-1 and RADARSAT-2","volume":"24","author":"Gao","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.rse.2014.05.018","article-title":"Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches","volume":"152","author":"Barrett","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Pairman, D., McNeill, S., Belliss, S., Dalley, D., and Dynes, R. (2008, January 7\u201311). Pasture Monitoring from Polarimetric TerraSAR-X Data. Proceedings of the IGARSS 2008\u20142008 IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA.","DOI":"10.1109\/IGARSS.2008.4779475"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Buckley, J.R., and Smith, A.M. (2010, January 25\u201330). Monitoring grasslands with radarsat 2 quad-pol imagery. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5652367"},{"key":"ref_65","first-page":"3225","article-title":"Application of Repeat-Pass TerraSAR-X Staring Spotlight Interferometric Coherence to Monitor Pasture Biophysical Parameters: Limitations and Sensitivity Analysis","volume":"10","author":"Ali","year":"2017","journal-title":"IEEE J.-Stars."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1080\/22797254.2021.1901063","article-title":"Biomass retrieval based on genetic algorithm feature selection and support vector regression in Alpine grassland using ground-based hyperspectral and Sentinel-1 SAR data","volume":"54","author":"Chiarito","year":"2021","journal-title":"Eur. J. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Zhang, X., Bao, Y., Wang, D., Xin, X., Ding, L., Xu, D., Hou, L., and Shen, J. (2021). Using UAV LiDAR to Extract Vegetation Parameters of Inner Mongolian Grassland. Remote Sens., 13.","DOI":"10.3390\/rs13040656"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"111432","DOI":"10.1016\/j.rse.2019.111432","article-title":"Lidar provides novel insights into the effect of pixel size and grazing intensity on measures of spatial heterogeneity in a native bunchgrass ecosystem","volume":"235","author":"Jansen","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/0034-4257(93)90040-5","article-title":"Evaluating landsat thematic mapper derived vegetation indices for estimating aboveground biomass on semiarid rangelands","volume":"45","author":"Anderson","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.gloenvcha.2006.02.002","article-title":"NDVI-based increase in growth of temperate grasslands and its responses to climate changes in China","volume":"16","author":"Piao","year":"2006","journal-title":"Glob. Env. Chang."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.ecolind.2012.05.024","article-title":"Mapping grassland productivity with 250-m eMODIS NDVI and SSURGO database over the Greater Platte River Basin, USA","volume":"24","author":"Gu","year":"2013","journal-title":"Ecol. Indic."},{"key":"ref_72","first-page":"344","article-title":"Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3","volume":"23","author":"Clevers","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"3999","DOI":"10.1080\/01431160310001654923","article-title":"Narrow band vegetation indices overcome the saturation problem in biomass estimation","volume":"25","author":"Mutanga","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Nestola, E., Calfapietra, C., Emmerton, C., Wong, C., Thayer, D., and Gamon, J. (2016). Monitoring Grassland Seasonal Carbon Dynamics, by Integrating MODIS NDVI, Proximal Optical Sampling, and Eddy Covariance Measurements. Remote Sens., 8.","DOI":"10.3390\/rs8030260"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1111\/j.1744-7909.2007.00401.x","article-title":"Comparison of Vegetation Indices and Red-edge Parameters for Estimating Grassland Cover from Canopy Reflectance Data","volume":"49","author":"Liu","year":"2007","journal-title":"J. Integr. Plant. Biol."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"3835","DOI":"10.1080\/01431161.2018.1553319","article-title":"Modelling aboveground biomass based on vegetation indexes: A modified approach for biomass estimation in semi-arid grasslands","volume":"40","author":"Wang","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"2178","DOI":"10.1080\/01431161.2011.607195","article-title":"New spectral vegetation indices based on the near-infrared shoulder wavelengths for remote detection of grassland phytomass","volume":"33","author":"Vescovo","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"e05272","DOI":"10.1016\/j.heliyon.2020.e05272","article-title":"Prediction of grass biomass from satellite imagery in Somali regional state, eastern Ethiopia","volume":"6","author":"Meshesha","year":"2020","journal-title":"Heliyon"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.jaridenv.2008.09.027","article-title":"Aboveground biomass in Tibetan grasslands","volume":"73","author":"Yang","year":"2009","journal-title":"J. Arid. Environ."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"107227","DOI":"10.1016\/j.ecolind.2020.107227","article-title":"A novel UAV-based approach for biomass prediction and grassland structure assessment in coastal meadows","volume":"122","author":"Bergamo","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"667905","DOI":"10.1117\/12.734933","article-title":"2-band enhanced vegetation index without a blue band and its application to AVHRR data","volume":"Volume 6679","author":"Jiang","year":"2007","journal-title":"Proceedings of the Remote Sensing and Modeling of Ecosystems for Sustainability IV"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"043520","DOI":"10.1117\/1.3400635","article-title":"Spectral compatibility of vegetation indices across sensors: Band decomposition analysis with Hyperion data","volume":"4","author":"Kim","year":"2010","journal-title":"J. Appl. Remote Sens."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Jarchow, C.J., Didan, K., Barreto-Mu\u00f1oz, A., Nagler, P.L., and Glenn, E.P. (2018). Application and comparison of the MODIS-derived enhanced vegetation index to VIIRS, landsat 5 TM and landsat 8 OLI platforms: A case study in the arid colorado river delta. Mexico. Sensors, 18.","DOI":"10.3390\/s18051546"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"9007","DOI":"10.1080\/01431161.2010.532172","article-title":"Hyperspectral remote sensing for estimating aboveground biomass and for exploring species richness patterns of grassland habitats","volume":"32","author":"Psomas","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_85","first-page":"196","article-title":"Estimation of grassland biomass and nitrogen using MERIS data","volume":"19","author":"Ullah","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.3390\/rs6021496","article-title":"Remote Sensing-Based Biomass Estimation and Its Spatio-Temporal Variations in Temperate Grassland, Northern China","volume":"6","author":"Jin","year":"2014","journal-title":"Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/j.rama.2020.06.006","article-title":"Estimating Aboveground Net Primary Production (ANPP) Using Landsat 8-Based Indices: A Case Study From Hir-Neur Rangelands, Iran","volume":"73","author":"Ghorbani","year":"2020","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Pang, H., Zhang, A., Kang, X., He, N., and Dong, G. (2020). Estimation of the Grassland Aboveground Biomass of the Inner Mongolia Plateau Using the Simulated Spectra of Sentinel-2 Images. Remote Sens., 12.","DOI":"10.3390\/rs12244155"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.5194\/bg-14-1333-2017","article-title":"Modelling spatial and temporal dynamics of gross primary production in the Sahel from earth-observation-based photosynthetic capacity and quantum efficiency","volume":"14","author":"Tagesson","year":"2017","journal-title":"Biogeosciences"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Lin, S., Li, J., Liu, Q., Li, L., Zhao, J., and Yu, W. (2019). Evaluating the Effectiveness of Using Vegetation Indices Based on Red-Edge Reflectance from Sentinel-2 to Estimate Gross Primary Productivity. Remote Sens., 11.","DOI":"10.3390\/rs11111303"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/01431161.2016.1259678","article-title":"Testing the capabilities of the new WorldView-3 space-borne sensor\u2019s red-edge spectral band in discriminating and mapping complex grassland management treatments","volume":"38","author":"Sibanda","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Imran, H.A., Gianelle, D., Rocchini, D., Dalponte, M., Mart\u00edn, M.P., Sakowska, K., Wohlfahrt, G., and Vescovo, L. (2020). VIS-NIR, Red-Edge and NIR-Shoulder Based Normalized Vegetation Indices Response to Co-Varying Leaf and Canopy Structural Traits in Heterogeneous Grasslands. Remote Sens., 12.","DOI":"10.3390\/rs12142254"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1080\/07038992.1996.10855178","article-title":"Evaluation of vegetation indices and a modified simple ratio for boreal applications","volume":"22","author":"Chen","year":"1996","journal-title":"Can. J. Remote Sens."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1111\/aje.12622","article-title":"Remotely sensed C3 and C4 grass species aboveground biomass variability in response to seasonal climate and topography","volume":"57","author":"Shoko","year":"2019","journal-title":"Afr. J. Ecol."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1016\/j.isprsjprs.2008.01.001","article-title":"LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements","volume":"63","author":"Darvishzadeh","year":"2008","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"036015","DOI":"10.1117\/1.JRS.10.036015","article-title":"Comparison of partial least squares and support vector regressions for predicting leaf area index on a tropical grassland using hyperspectral data","volume":"10","author":"Kiala","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"e2265","DOI":"10.1002\/eco.2265","article-title":"Aboveground biomass production and dominant species type determined canopy storage capacity of abandoned grassland communities on semiarid Loess Plateau","volume":"14","author":"Xiong","year":"2021","journal-title":"Ecohydrology"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1080\/01431161.2015.1131867","article-title":"Chlorophyll content estimation in arid grasslands from Landsat-8 OLI data","volume":"37","author":"Yin","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/S1872-2032(07)60012-2","article-title":"Remote sensing monitoring upon the grass production in China","volume":"27","author":"Xu","year":"2007","journal-title":"Acta Ecol. Sin."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"5359","DOI":"10.1080\/01431160410001719849","article-title":"Remote sensing capabilities to estimate pasture production in France","volume":"25","author":"Bella","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"2592","DOI":"10.1016\/j.rse.2007.12.003","article-title":"Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland","volume":"112","author":"Darvishzadeh","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1080\/01431160701268947","article-title":"Estimating structural and biochemical parameters for grassland from spectroradiometer data by radiative transfer modelling (PROSPECT+SAIL)","volume":"29","author":"Vohland","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1080\/15481603.2018.1492213","article-title":"Estimating LAI and mapping canopy storage capacity for hydrological applications in wattle infested ecosystems using Sentinel-2 MSI derived red edge bands","volume":"56","author":"Sibanda","year":"2019","journal-title":"GIScience Remote Sens."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"695","DOI":"10.5589\/m12-056","article-title":"Quantifying biomass production on rangeland in southern Alberta using SPOT imagery","volume":"38","author":"Grant","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1023\/A:1026308928874","article-title":"Satellite estimate of grass biomass in a mountainous range in central Italy","volume":"59","author":"Schino","year":"2003","journal-title":"Agrofor. Syst."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1080\/15481603.2016.1221576","article-title":"Comparing the spectral settings of the new generation broad and narrow band sensors in estimating biomass of native grasses grown under different management practices","volume":"53","author":"Sibanda","year":"2016","journal-title":"GIScience Remote Sens."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"1","DOI":"10.17159\/sajs.2017\/20160277","article-title":"Potential of interval partial least square regression in estimating leaf area index","volume":"113","author":"Kiala","year":"2017","journal-title":"S. Afr. J. Sci."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Li, C., Zhou, L., and Xu, W. (2021). Estimating Aboveground Biomass Using Sentinel-2 MSI Data and Ensemble Algorithms for Grassland in the Shengjin Lake Wetland, China. Remote Sens., 13.","DOI":"10.3390\/rs13081595"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"6163","DOI":"10.3390\/rs6076163","article-title":"Combined Use of Multi-Temporal Optical and Radar Satellite Images for Grassland Monitoring","volume":"6","author":"Dusseux","year":"2014","journal-title":"Remote Sens."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"107215","DOI":"10.1016\/j.ecolind.2020.107215","article-title":"Remote sensing inversion of grassland aboveground biomass based on high accuracy surface modeling","volume":"121","author":"Zhou","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"1810","DOI":"10.1016\/j.ecolmodel.2009.04.025","article-title":"A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia, China","volume":"220","author":"Xie","year":"2009","journal-title":"Ecol. Model."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.rse.2018.09.028","article-title":"Application of Sentinel-2A data for pasture biomass monitoring using a physically based radiative transfer model","volume":"218","author":"Punalekar","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.isprsjprs.2015.05.005","article-title":"Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties\u2014A review","volume":"108","author":"Verrelst","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/S2095-3119(15)61303-X","article-title":"Estimating grassland LAI using the Random Forests approach and Landsat imagery in the meadow steppe of Hulunber, China","volume":"16","author":"Li","year":"2017","journal-title":"J. Integr. Agric."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Dube, T., Pandit, S., Shoko, C., Ramoelo, A., Mazvimavi, D., and Dalu, T. (2019). Numerical Assessments of Leaf Area Index in Tropical Savanna Rangelands, South Africa Using Landsat 8 OLI Derived Metrics and In-Situ Measurements. Remote Sens., 11.","DOI":"10.3390\/rs11070829"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2015.10.005","article-title":"Examining the potential of Sentinel-2 MSI spectral resolution in quantifying above ground biomass across different fertilizer treatments","volume":"110","author":"Sibanda","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"16398","DOI":"10.3390\/rs71215841","article-title":"Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data","volume":"7","author":"Ali","year":"2015","journal-title":"Remote Sens."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Zhang, X., Chen, X., Tian, M., Fan, Y., Ma, J., and Xing, D. (2020). An evaluation model for aboveground biomass based on hyperspectral data from field and TM8 in Khorchin grassland, China. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0223934"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2015.12.024","article-title":"Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity","volume":"185","author":"Roy","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Sibanda, M., Mutanga, O., Rouget, M., and Kumar, L. (2017). Estimating biomass of native grass grown under complex management treatments using worldview-3 spectral derivatives. Remote Sens., 9.","DOI":"10.3390\/rs9010055"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"247","DOI":"10.2989\/10220119.2019.1697754","article-title":"Quantifying grass productivity using remotely sensed data: An assessment of grassland restoration benefits","volume":"37","author":"Vundla","year":"2020","journal-title":"Afr. J. Range Sci."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Berger, K., Atzberger, C., Danner, M., D\u2019Urso, G., Mauser, W., Vuolo, F., and Hank, T. (2018). Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study. Remote Sens., 10.","DOI":"10.3390\/rs10010085"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.isprsjprs.2017.02.010","article-title":"Estimating and mapping chlorophyll content for a heterogeneous grassland: Comparing prediction power of a suite of vegetation indices across scales between years","volume":"126","author":"Tong","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"9","DOI":"10.2111\/REM-D-13-00059.1","article-title":"A Comparison of Satellite-Derived Vegetation Indices for Approximating Gross Primary Productivity of Grasslands","volume":"67","author":"Zhou","year":"2014","journal-title":"Rangel. Ecol. Manag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/2043\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:14:51Z","timestamp":1760123691000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/2043"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,12]]},"references-count":125,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["rs15082043"],"URL":"https:\/\/doi.org\/10.3390\/rs15082043","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,12]]}}}