{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:57:14Z","timestamp":1772823434693,"version":"3.50.1"},"reference-count":80,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,11]],"date-time":"2019-01-11T00:00:00Z","timestamp":1547164800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Estimating the net primary production (NPP) of vegetation is essential for eco-environment conservation and carbon cycle research. Remote sensing techniques, combined with algorithm models, have been proven to be promising methods for NPP estimation. High-precision and real-time NPP monitoring in heterogeneous areas requires high spatio-temporal resolution remote sensing data, which are not easy to acquire by single remote sensors, especially in cloudy weather. This study proposes to fuse images of different sensors to provide high spatio-temporal resolution data for NPP estimation in cloud-prone areas. Firstly, the time series Normalized Difference Vegetation Index (NDVI) with a spatial resolution of 30 m and a temporal resolution of 16 days, are obtained by the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Then, the time series NDVI data, combined with meteorological data are input into an improved Carnegie\u2013Ames\u2013Stanford Approach (CASA) model for NPP estimation. This method is validated by a case study of a heavily urbanized area, in the middle reaches of the Yangtze River in China. The results indicate that the NPP estimated by the fused NDVI data has more detailed spatial information than by using the MODIS data. The results show a strong correlation between the actual Landsat8 NDVI and the fused NDVI images, which means that the accuracy of synthetic NDVI images (a 16 day interval and a 30 m resolution) is reliable, and it can provide superior inputs for accurate estimations of a NPP time series. The correlation coefficient (R) and root mean square error between the NPP, based on the fused NDVI and the measured NPP, are 0.66 and 14.280 g C\/(m2\u00b7yr), respectively, indicating a good consistency. The small discrepancy is caused by the uncertainties of fused NDVI, measurement errors, conversion errors, and other factors in the CASA model. In this study, we achieved NPP with high spatial and temporal resolutions, which can provide higher accuracies of NPP data for analyzing the carbon cycling heavily urbanized areas, compared with similar studies using mono-temporal NPP data. The spatio-temporal fusion technique is an effective way of generating high spatio-temporal resolution images from different sensors, thereby providing enough data for NPP monitoring in urbanized areas.<\/jats:p>","DOI":"10.3390\/rs11020133","type":"journal-article","created":{"date-parts":[[2019,1,11]],"date-time":"2019-01-11T11:36:42Z","timestamp":1547206602000},"page":"133","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["RETRACTED: Estimation of Vegetation Productivity Using a Landsat 8 Time Series in a Heavily Urbanized Area, Central China"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3082-9410","authenticated-orcid":false,"given":"Meng","family":"Zhang","sequence":"first","affiliation":[{"name":"Research Center of Forest Remote Sensing and Information Engineering, Central South University of Forestry &amp; Technology, Changsha 410004, China"},{"name":"Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha 410004, China"},{"name":"Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China"}]},{"given":"Hui","family":"Lin","sequence":"additional","affiliation":[{"name":"Research Center of Forest Remote Sensing and Information Engineering, Central South University of Forestry &amp; Technology, Changsha 410004, China"},{"name":"Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha 410004, China"},{"name":"Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China"}]},{"name":"Sun","sequence":"additional","affiliation":[]},{"given":"Yaotong","family":"Cai","sequence":"additional","affiliation":[{"name":"Research Center of Forest Remote Sensing and Information Engineering, Central South University of Forestry &amp; Technology, Changsha 410004, China"},{"name":"Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha 410004, China"},{"name":"Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.rse.2006.02.017","article-title":"Evaluation of MODIS NPP and GPP products across multiple biomes","volume":"102","author":"Turner","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1486","DOI":"10.1016\/j.rse.2008.12.014","article-title":"Modeling and spatio-temporal analysis framework for monitoring environmental change using NPP as an ecosystem indicator","volume":"113","author":"Crabtree","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.isprsjprs.2013.03.005","article-title":"Estimating crop net primary production using national inventory data and MODIS-derived parameters","volume":"80","author":"Bandaru","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2018.04.051","article-title":"Megacity-scale analysis of urban vegetation temperatures","volume":"213","author":"Wetherley","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.rse.2005.12.001","article-title":"A temporal analysis of urban forest carbon storage using remote sensing","volume":"101","author":"Myeong","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2013.05.001","article-title":"Urban vegetation classification: Benefits of multitemporal RapidEye satellite data","volume":"136","author":"Tigges","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2016.12.014","article-title":"Estimating urban vegetation fraction across 25 cities in pan-Pacific using Landsat time series data","volume":"126","author":"Lu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","first-page":"211","article-title":"An efficient unsupervised index based approach for mapping urban vegetation from IKONOS imagery","volume":"50","author":"Anchang","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","first-page":"60","article-title":"Comparison of sampling strategies for object-based classification of urban vegetation from very high resolution satellite image","volume":"51","author":"Rougier","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_10","unstructured":"Group on Earth Observations (2013, April 23). Forest Carbon Tracking Portal. Available online: http:\/\/www.geo-fct.org\/."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4125","DOI":"10.3390\/rs5084125","article-title":"Assessment Impacts of Weather and Land Use\/Land Cover (LULC) Change on Urban Vegetation Net Primary Productivity (NPP): A Case Study in Guangzhou, China","volume":"5","author":"Fu","year":"2013","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1038\/nature06444","article-title":"Net carbon dioxide losses of northern ecosystems in response to autumn warming","volume":"451","author":"Piao","year":"2008","journal-title":"Nature"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Guan, X., Shen, H., Gan, W., Yang, G., Wang, L., and Li, X. (2017). A 33-year NPP monitoring study in southwest China by the fusion of multi-source remote sensing and station data. Remote Sens., 9.","DOI":"10.3390\/rs9101082"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1007\/s10584-005-6339-8","article-title":"Variations in vegetation net primary production in the Qinghai-Xizang Plateau, China, from 1982 to 1999","volume":"74","author":"Piao","year":"2006","journal-title":"Clim. Chang."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, R., Zhou, Y., Luo, H., Wang, F., and Wang, S. (2017). Estimation and Analysis of Spatiotemporal Dynamics of the Net Primary Productivity Integrating Efficiency Model with Process Model in Karst Area. Remote Sens., 9.","DOI":"10.3390\/rs9050477"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/0034-4257(94)00061-Q","article-title":"Remote sensing of the land surface for studies of global change: Models\u2014algorithms\u2014experiments","volume":"51","author":"Sellers","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.rse.2004.07.016","article-title":"Quantifying forest aboveground carbon content using lidar remote sensing","volume":"93","author":"Patenaude","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1029\/93GB02725","article-title":"Terrestrial ecosystem production: A process model-based on global satellite and surface data","volume":"7","author":"Potter","year":"1993","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.rse.2004.12.011","article-title":"Improvements of the MODIS terrestrial gross and net primary production global data set","volume":"95","author":"Zhao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"815","DOI":"10.2307\/2845983","article-title":"Global primary production: A remote sensing approach","volume":"22","author":"Prince","year":"1995","journal-title":"J. Biogeogr."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"471","DOI":"10.3402\/tellusb.v47i4.16062","article-title":"Impact of drought stress and other factors on seasonal land biosphere CO2 exchange studied through an atmospheric tracer transport model","volume":"47","author":"Knorr","year":"1995","journal-title":"Tellus"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1175\/1520-0442(1996)009<0676:ARLSPF>2.0.CO;2","article-title":"A revised land surface parameterization (SiB2) for atmospheric GCMs. I. Model formulation","volume":"9","author":"Sellers","year":"1996","journal-title":"J. Clim."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1029\/96GB00349","article-title":"TURC: A diagnostic model of continental gross primary productivity and net primary productivity","volume":"10","author":"Ruimy","year":"1996","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/S0034-4257(97)00089-8","article-title":"A Process-Based Boreal Ecosystem Productivity Simulator Using Remote Sensing Inputs","volume":"62","author":"Liu","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1029\/93GB02042","article-title":"Observations and modeling of biomass and soil organic matter dynamics for the grassland biome worldwide","volume":"7","author":"Parton","year":"1993","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Rafique, R., Zhao, F., Rogier, D.J., Zeng, N., and Ghassem, A. (2016). Global and Regional Variability and Change in Terrestrial Ecosystems Net Primary Production and NDVI: A Model-Data Comparison. Remote Sens., 8.","DOI":"10.3390\/rs8030177"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1641\/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2","article-title":"Acontinuous satellite-derived measure of global terrestrial primary production","volume":"54","author":"Running","year":"2004","journal-title":"BioScience"},{"key":"ref_28","first-page":"336","article-title":"Modelling the current and future spatial distribution of NPP in a Mediterranean watershed","volume":"13","author":"Donmez","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_29","first-page":"217","article-title":"Remote sensing net primary productivity (NPP) estimation with the aid of GIS modelled shortwave radiation (SWR) in a Southern African Savanna","volume":"30","author":"Pachavo","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_30","first-page":"84","article-title":"Modeling net primary productivity of terrestrial ecosystems in the semi-arid climate of the Mongolian Plateau using LSWI-based CASA ecosystem model","volume":"46","author":"Bao","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.foreco.2012.03.022","article-title":"Reconciling satellite with ground data to estimate forest productivity at national scales","volume":"276","author":"Hasenauer","year":"2012","journal-title":"For. Ecol. Manag."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3878","DOI":"10.3390\/rs70403878","article-title":"Comparing MODIS Net Primary Production Estimates with Terrestrial National Forest Inventory Data in Austria","volume":"7","author":"Neumann","year":"2015","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"810","DOI":"10.3390\/rs5020810","article-title":"Trends and variability of AHVRR-derived NPP in India","volume":"5","author":"Bala","year":"2013","journal-title":"Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3803","DOI":"10.3390\/rs5083803","article-title":"Disentangling the relationships between net primary production and precipitation in southern africa savannas using satellite observations from 1982 to 2010","volume":"5","author":"Zhu","year":"2013","journal-title":"Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6043","DOI":"10.3390\/rs5116043","article-title":"Recent changes in terrestrial gross primary productivity in Asia from 1982 to 2011","volume":"5","author":"Ichii","year":"2013","journal-title":"Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.rse.2003.11.008","article-title":"Satellite-based modeling of gross primary production in an evergreen needle leaf forest","volume":"89","author":"Xiao","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.3390\/rs70201798","article-title":"Comparison of Spatiotemporal Fusion Models: A Review","volume":"7","author":"Chen","year":"2015","journal-title":"Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4411","DOI":"10.1080\/01431161.2015.1083633","article-title":"A generalization of spatial and temporal fusion methods for remotely sensed surface parameters","volume":"36","author":"Zhang","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TGRS.2006.872081","article-title":"On the blending of the Landsat and MODIS surface reflectance predicting daily Landsat surface reflectance","volume":"44","author":"Gao","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.1016\/j.rse.2009.05.011","article-title":"Generation of dense time series synthetic Landsat data through dada blending with MODIS using a spatial and temporal adaptive reflectance fusion model","volume":"113","author":"Hiker","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.1016\/j.rse.2010.05.032","article-title":"An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions","volume":"114","author":"Zhu","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.rse.2014.09.013","article-title":"Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature","volume":"156","author":"Wu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2018.02.009","article-title":"A robust adaptive spatial and temporal image fusion model for complex land surface changes","volume":"208","author":"Zhao","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"063507","DOI":"10.1117\/1.JRS.6.063507","article-title":"Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model","volume":"6","author":"Wu","year":"2012","journal-title":"J. Appl. Remote. Sens."},{"key":"ref_46","first-page":"65","article-title":"Spatio-temporal fusion for daily Sentinel-2 images","volume":"204","author":"Wang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.rse.2012.08.003","article-title":"Mapping spatial and temporal patterns of Mediterranean wildfires from MODIS","volume":"126","author":"Levin","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Liao, C., Wang, J., Pritchard, I., Liu, J., and Shang, J. (2017). A Spatio-Temporal Data Fusion Model for Generating NDVI Time Series in Heterogeneous Regions. Remote Sens., 9.","DOI":"10.3390\/rs9111125"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Mansaray, L., Huang, W., Zhang, D., Huang, J., and Li, J. (2017). Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets. Remote Sens., 9.","DOI":"10.3390\/rs9030257"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Chen, Z., Wang, L., Wu, W., Jiang, Z., and Li, H. (2016). Monitoring Plastic-Mulched Farmland by Landsat-8 OLI Imagery Using Spectral and Textural Features. Remote Sens., 8.","DOI":"10.3390\/rs8040353"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Karakizi, C., Karantzalos, K., Vakalopoulou, M., and Antoniou, G. (2018). Detailed land cover mapping from multitemporal landsat-8 data of different cloud cover. Remote Sens., 8.","DOI":"10.3390\/rs10081214"},{"key":"ref_52","first-page":"35","article-title":"Spatial and temporal variability of the net primary production (NPP) and its relationship with climate factors in subtropical mountainous and hilly regions of China: A case study in Hunan province","volume":"71","author":"Chen","year":"2016","journal-title":"Acta Geogr. Sin."},{"key":"ref_53","first-page":"1","article-title":"Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images","volume":"46","author":"Zhou","year":"2016","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1080\/01431161.2014.995278","article-title":"Towards the development of a regional version of MOD17 for the determination of gross and net primary productivity of oil palm trees","volume":"36","author":"Cracknell","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"e01561","DOI":"10.1002\/ecs2.1561","article-title":"Drought resistance across California ecosystems: Evaluating changes in carbon dynamics using satellite imagery","volume":"7","author":"Malone","year":"2016","journal-title":"Ecosphere"},{"key":"ref_56","unstructured":"Running, S., Mu, Q., and Zhao, M. (2015). MOD17A3H MODIS\/Terra Net Primary Production Yearly L4 Global 500 m SIN Grid V006 Data Set."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1139\/x83-056","article-title":"The ratio of total to merchantable forest biomass and its application to the global carbon budget","volume":"13","author":"Johnson","year":"1983","journal-title":"Can. J. For. Res."},{"key":"ref_58","first-page":"300","article-title":"Estimating net primary productivity of terrestrial vegetation based on remote sensing: A case study in Inner Mongolia, China","volume":"9","author":"Zhu","year":"2005","journal-title":"J. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.cageo.2004.05.006","article-title":"A program for analyzingtime-series of satellite sensor data","volume":"30","author":"Eklundh","year":"2004","journal-title":"Comput. Geosci."},{"key":"ref_60","first-page":"193","article-title":"A natural vegetation NPP model","volume":"19","author":"Zhou","year":"1995","journal-title":"Acta Phytoecol. Sin."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Running, S.W., Thornton, P.E., Nemani, R., and Glassy, J.M. (2000). Global Terrestrial Gross and Net Primary Productivity from the Earth Observing System. Methods in Ecosystem Science, Springer.","DOI":"10.1007\/978-1-4612-1224-9_4"},{"key":"ref_62","unstructured":"Zhu, W. (2005). Estimation of Net Primary Productivity of Chinese Terrestrial Vegetation Based on Remote Sensing and Its Relationship with Global Climate Change, Beijing Normal University."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/s11434-006-0457-1","article-title":"Simulation of maximum light use efficiency for some typical vegetation types in China","volume":"51","author":"Zhu","year":"2006","journal-title":"Chin. Sci. Bull."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.agrformet.2012.12.003","article-title":"Assessing the differences in net primary productivity between pre- and post-urban land development in China","volume":"171\u2013172","author":"Pei","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1046\/j.1365-2486.2002.00503.x","article-title":"Satellite estimates of productivity and light use efficiency in United States agriculture, 1982-98","volume":"8","author":"Lobell","year":"2002","journal-title":"Glob. Chang. Biol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"3237","DOI":"10.1080\/01431161.2014.903351","article-title":"Blending MODIS and Landsat images for urban flood mapping","volume":"35","author":"Zhang","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Zhang, B., Zhang, L., Xie, D., Yin, X., Liu, C., and Liu, G. (2016). Application of Synthetic NDVI Time Series Blended from Landsat and MODIS Data for Grassland Biomass Estimation. Remote Sens., 8.","DOI":"10.3390\/rs8010010"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.3390\/rs5041704","article-title":"Using low resolution satellite imagery for yield prediction and yield anomaly detection","volume":"5","author":"Rembold","year":"2013","journal-title":"Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Xie, D., Gao, F., Sun, L., and Anderson, M. (2018). Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs. Remote Sens., 10.","DOI":"10.3390\/rs10071142"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"731","DOI":"10.5589\/m04-040","article-title":"Spatial distribution of net primary productivity and evapotranspiration in Changbaishan Natural Reserve, China, using Landsat ETM+ data","volume":"30","author":"Sun","year":"2004","journal-title":"Can. J. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1016\/j.jenvman.2006.08.017","article-title":"Measurements and simulation of forest leaf area index and net primary productivity in Northern China","volume":"85","author":"Wang","year":"2007","journal-title":"J. Environ. Manag."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"4879","DOI":"10.5194\/bg-10-4879-2013","article-title":"Effects of vegetation heterogeneity and surface topography on spatial scaling of net primary productivity","volume":"10","author":"Chen","year":"2013","journal-title":"Biogeosciences"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Singha, M., Wu, B., and Zhang, M. (2016). An Object-Based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India. Remote Sens., 8.","DOI":"10.3390\/rs8060479"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Liu, M., Liu, X., Wu, L., Zou, X., Jiang, T., and Zhao, B. (2018). A Modified Spatiotemporal Fusion Algorithm Using Phenological Information for Predicting Reflectance of Paddy Rice in Southern China. Remote Sens., 10.","DOI":"10.3390\/rs10050772"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.rse.2018.04.016","article-title":"Addressing spatio-temporal resolution constraints in Landsat and MODIS-based image mapping of large-scale floodplain inundation dynamics","volume":"211","author":"Heimhuber","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.rse.2016.01.028","article-title":"The benefit of synthetically generated RapidEye and Landsat 8 data fusion time series for riparian forest disturbance monitoring","volume":"177","author":"Kleinschmit","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/0034-4257(94)00066-V","article-title":"Global net primary production: Combining ecology and remote sensing","volume":"51","author":"Field","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.rse.2005.02.013","article-title":"The relative importance of light-use efficiency modifications from environmental conditions and cultivation for estimation of large-scale net primary productivity","volume":"96","author":"Bradford","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.rse.2017.12.025","article-title":"Detecting Himalayan glacial lake outburst floods from Landsat time series","volume":"207","author":"Veh","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.rse.2016.02.010","article-title":"Remotely sensed assessment of urbanization effects on vegetation phenology in China\u2019s 32 major cities","volume":"176","author":"Zhou","year":"2016","journal-title":"Remote Sens. 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