{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T16:51:50Z","timestamp":1772297510045,"version":"3.50.1"},"reference-count":75,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,2,25]],"date-time":"2020-02-25T00:00:00Z","timestamp":1582588800000},"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":["41621061"],"award-info":[{"award-number":["41621061"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41977405"],"award-info":[{"award-number":["41977405"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31561143003"],"award-info":[{"award-number":["31561143003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Wheat is a leading cereal grain throughout the world. Timely and reliable wheat yield prediction at a large scale is essential for the agricultural supply chain and global food security, especially in China as an important wheat producing and consuming country. The conventional approach using either climate or satellite data or both to build empirical and crop models has prevailed for decades. However, to what extent climate and satellite data can improve yield prediction is still unknown. In addition, socio-economic (SC) factors may also improve crop yield prediction, but their contributions need in-depth investigation, especially in regions with good irrigation conditions, sufficient fertilization, and pesticide application. Here, we performed the first attempt to predict wheat yield across China from 2001 to 2015 at the county-level by integrating multi-source data, including monthly climate data, satellite data (i.e., Vegetation indices (VIs)), and SC factors. The results show that incorporating all the datasets by using three machine learning methods (Ridge Regression (RR), Random Forest (RF), and Light Gradient Boosting (LightGBM)) can achieve the best performance in yield prediction (R2: 0.68~0.75), with the most individual contributions from climate (~0.53), followed by VIs (~0.45), and SC factors (~0.30). In addition, the combinations of VIs and climate data can capture inter-annual yield variability more effectively than other combinations (e.g., combinations of climate and SC, and combinations of VIs and SC), while combining SC with climate data can better capture spatial yield variability than others. Climate data can provide extra and unique information across the entire growing season, while the peak stage of VIs (Mar.~Apr.) do so. Furthermore, incorporating spatial information and soil proprieties into the benchmark models can improve wheat yield prediction by 0.06 and 0.12, respectively. The optimal wheat prediction can be achieved with approximately a two-month leading time before maturity. Our study develops timely and robust methods for winter wheat yield prediction at a large scale in China, which can be applied to other crops and regions.<\/jats:p>","DOI":"10.3390\/rs12050750","type":"journal-article","created":{"date-parts":[[2020,2,26]],"date-time":"2020-02-26T04:18:29Z","timestamp":1582690709000},"page":"750","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":117,"title":["Identifying the Contributions of Multi-Source Data for Winter Wheat Yield Prediction in China"],"prefix":"10.3390","volume":"12","author":[{"given":"Juan","family":"Cao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology\/MEM&amp;MoE Key Laboratory of Environmental Change and Natural Hazards, Faculty of Geographical Science, Beijing Normal University; Beijing 100875, China"}]},{"given":"Zhao","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology\/MEM&amp;MoE Key Laboratory of Environmental Change and Natural Hazards, Faculty of Geographical Science, Beijing Normal University; Beijing 100875, China"}]},{"given":"Fulu","family":"Tao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Natural Resources Institute Finland (Luke), FI-00790 Helsinki, Finland"}]},{"given":"Liangliang","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology\/MEM&amp;MoE Key Laboratory of Environmental Change and Natural Hazards, Faculty of Geographical Science, Beijing Normal University; Beijing 100875, China"}]},{"given":"Yuchuan","family":"Luo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology\/MEM&amp;MoE Key Laboratory of Environmental Change and Natural Hazards, Faculty of Geographical Science, Beijing Normal University; Beijing 100875, China"}]},{"given":"Jichong","family":"Han","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology\/MEM&amp;MoE Key Laboratory of Environmental Change and Natural Hazards, Faculty of Geographical Science, Beijing Normal University; Beijing 100875, China"}]},{"given":"Ziyue","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology\/MEM&amp;MoE Key Laboratory of Environmental Change and Natural Hazards, Faculty of Geographical Science, Beijing Normal University; Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.agrformet.2019.03.010","article-title":"Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches","volume":"274","author":"Cai","year":"2019","journal-title":"Agric. Meteorol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1495","DOI":"10.1080\/01431169308953983","article-title":"NDVI\u2014Crop monitoring and early yield assessment of Burkina Faso","volume":"14","author":"Groten","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"He, Z., Xia, X., and Zhang, Y. (2010). Breeding noodle wheat in China. Asian Noodles: Science, Technology, and Processing, Wiley.","DOI":"10.1002\/9780470634370.ch1"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.scitotenv.2019.06.367","article-title":"The influence of excess precipitation on winter wheat under climate change in China from 1961 to 2017","volume":"690","author":"Song","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.scitotenv.2019.06.262","article-title":"Impact-oriented water footprint assessment of wheat production in China","volume":"689","author":"Zhai","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1016\/j.scitotenv.2018.06.009","article-title":"Short-term biochar manipulation of microbial nitrogen transformation in wheat rhizosphere of a metal contaminated Inceptisol from North China plain","volume":"640","author":"Zhou","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.agrformet.2015.02.001","article-title":"Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model","volume":"204","author":"Huang","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.eja.2018.09.006","article-title":"Improving regional winter wheat yield estimation through assimilation of phenology and leaf area index from remote sensing data","volume":"101","author":"Chen","year":"2018","journal-title":"Eur. J. Agron."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1038\/nclimate1832","article-title":"The critical role of extreme heat for maize production in the United States","volume":"3","author":"Lobell","year":"2013","journal-title":"Nat. Clim. Chang."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1038\/nature16467","article-title":"Influence of extreme weather disasters on global crop production","volume":"529","author":"Lesk","year":"2016","journal-title":"Nature"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/0308-521X(92)90022-G","article-title":"Yield forecasting","volume":"40","author":"Horie","year":"1992","journal-title":"Agric. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"621","DOI":"10.3389\/fpls.2019.00621","article-title":"Crop Yield Prediction Using Deep Neural Networks","volume":"10","author":"Khaki","year":"2019","journal-title":"Front. Plant Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2464","DOI":"10.1111\/gcb.13530","article-title":"Hot spots of wheat yield decline with rising temperatures","volume":"23","author":"Asseng","year":"2017","journal-title":"Glob. Chang. Biol."},{"key":"ref_14","first-page":"026002","article-title":"Space-based vegetation health for wheat yield modeling and prediction in Australia","volume":"12","author":"Kogan","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.compag.2015.11.018","article-title":"Wheat yield prediction using machine learning and advanced sensing techniques","volume":"121","author":"Pantazi","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","first-page":"143","article-title":"Climate Change Impact on Wheat Production in the Southern Great Plains of the US Using Downscaled Climate Data","volume":"8","author":"Dhakal","year":"2018","journal-title":"Atmos. Clim. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.fcr.2017.02.012","article-title":"Spatio-temporal patterns of winter wheat yield potential and yield gap during the past three decades in North China","volume":"206","author":"Chen","year":"2017","journal-title":"Field Crop. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.rse.2015.04.021","article-title":"A scalable satellite-based crop yield mapper","volume":"164","author":"Lobell","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.catena.2018.12.013","article-title":"Susceptibility assessment of landslides triggered by earthquakes in the Western Sichuan Plateau","volume":"175","author":"Cao","year":"2019","journal-title":"Catena"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.irbm.2013.01.010","article-title":"TeleOphta: Machine learning and image processing methods for teleophthalmology","volume":"34","author":"Cazuguel","year":"2013","journal-title":"Irbm"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rosten, E., and Drummond, T. (2006, January 7\u201313). Machine learning for high-speed corner detection. Proceedings of the European Conference on Computer Vision, Graz, Austria.","DOI":"10.1007\/11744023_34"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1016\/j.eswa.2013.08.015","article-title":"Syntactic n-grams as machine learning features for natural language processing","volume":"41","author":"Sidorov","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1016\/j.scitotenv.2019.02.093","article-title":"Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gully erosion","volume":"664","author":"Garosi","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1016\/j.scitotenv.2018.12.217","article-title":"Assessment of urban flood susceptibility using semi-supervised machine learning model","volume":"659","author":"Zhao","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.rse.2018.02.045","article-title":"A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach","volume":"210","author":"Cai","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","article-title":"Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data","volume":"14","author":"Kussul","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.rama.2018.01.001","article-title":"Nondestructive estimation of standing crop and fuel moisture content in tallgrass prairie","volume":"71","author":"Sharma","year":"2018","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.agrformet.2015.11.003","article-title":"Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods","volume":"218","author":"Johnson","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.agsy.2004.07.009","article-title":"Artificial neural networks for corn and soybean yield prediction","volume":"85","author":"Kaul","year":"2005","journal-title":"Agric. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Everingham, Y., Sexton, J., Skocaj, D., and Inman-Bamber, G. (2016). Accurate prediction of sugarcane yield using a random forest algorithm. Agron. Sustain. Dev., 36.","DOI":"10.1007\/s13593-016-0364-z"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1111\/gcb.13136","article-title":"Improving the monitoring of crop productivity using spaceborne solar-induced fluorescence","volume":"22","author":"Guan","year":"2016","journal-title":"Glob. Chang. Biol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.rse.2013.10.027","article-title":"An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States","volume":"141","author":"Johnson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1471","DOI":"10.1016\/j.ecolmodel.2011.02.007","article-title":"Application of a Random Forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy","volume":"222","author":"Vincenzi","year":"2011","journal-title":"Ecol. Model."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.rse.2017.06.043","article-title":"The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields","volume":"199","author":"Guan","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_35","first-page":"158","article-title":"Early prediction of winter wheat yield with long time series meteorological data and random forest method","volume":"35","author":"Liu","year":"2019","journal-title":"Trans. CSAE"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Newlands, N.K., Zamar, D.S., Kouadio, L.A., Zhang, Y., Chipanshi, A., Potgieter, A., Toure, S., and Hill, H.S.J. (2014). An integrated, probabilistic model for improved seasonal forecasting of agricultural crop yield under environmental uncertainty. Front. Environ. Sci., 2.","DOI":"10.3389\/fenvs.2014.00017"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Patrignani, A., Lollato, R.P., Ochsner, T.E., Godsey, C.B., and Edwards, J.T. (2014). Yield Gap and Production Gap of Rainfed Winter Wheat in the Southern Great Plains. Agron. J., 106.","DOI":"10.2134\/agronj14.0011"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Vereecken, H., Weiherm\u00fcller, L., Jonard, F., and Montzka, C. (2012). Characterization of Crop Canopies and Water Stress Related Phenomena using Microwave Remote Sensing Methods: A Review. Vadose Zone J., 11.","DOI":"10.2136\/vzj2011.0138ra"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1016\/j.rse.2010.01.010","article-title":"A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data","volume":"114","author":"Vermote","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2189","DOI":"10.1073\/pnas.1616919114","article-title":"Satellite-based assessment of yield variation and its determinants in smallholder African systems","volume":"114","author":"Burke","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/0034-4257(92)90102-P","article-title":"Canopy reflectance, photosynthesis, and transpiration. III. A reanalysis using improved leaf models and a new canopy integration scheme","volume":"42","author":"Sellers","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Gitelson, A.A., Vi\u00f1a, A., Arkebauer, T.J., Rundquist, D.C., Keydan, G., and Leavitt, B. (2003). Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophys. Res. Lett., 30.","DOI":"10.1029\/2002GL016450"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Jain, M., Srivastava, A., Balwinder, S., Joon, R., McDonald, A., Royal, K., Lisaius, M., and Lobell, D. (2016). Mapping Smallholder Wheat Yields and Sowing Dates Using Micro-Satellite Data. Remote Sens., 8.","DOI":"10.3390\/rs8100860"},{"key":"ref_46","first-page":"73","article-title":"Relationship between MODIS-NDVI data and wheat yield: A case study in Northern Buenos Aires province, Argentina","volume":"2","author":"Lopresti","year":"2015","journal-title":"Inf. Process. Agric."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1038\/ncomms2296","article-title":"Recent patterns of crop yield growth and stagnation","volume":"3","author":"Ray","year":"2012","journal-title":"Nat. Commun."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s00271-007-0069-9","article-title":"Modeling the role of irrigation in winter wheat yield, crop water productivity, and production in China","volume":"26","author":"Liu","year":"2007","journal-title":"Irrig. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1038\/nature11420","article-title":"Closing yield gaps through nutrient and water management","volume":"490","author":"Mueller","year":"2012","journal-title":"Nature"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1057\/ces.2001.8","article-title":"Crop Yield Convergence: How Russia\u2019s Yield Performance Has Compared to Global Yield Leaders","volume":"43","author":"Trueblood","year":"2001","journal-title":"Comp. Econ. Stud."},{"key":"ref_51","first-page":"87","article-title":"Impacts of drought intensity and drought duration on winter wheat yield in five provinces of North China plain","volume":"074","author":"Yu","year":"2019","journal-title":"Acta Geogr. Sin."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1111\/gcb.12428","article-title":"Climatic and technological ceilings for Chinese rice stagnation based on yield gaps and yield trend pattern analysis","volume":"20","author":"Zhang","year":"2014","journal-title":"Glob. Chang. Biol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"233","DOI":"10.3354\/cr01131","article-title":"Response of crop yields to climate trends since 1980 in China","volume":"54","author":"Tao","year":"2012","journal-title":"Clim. Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s11442-014-1082-6","article-title":"Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s","volume":"24","author":"Liu","year":"2014","journal-title":"J. Geogr. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/j.rse.2005.08.012","article-title":"Spatial and temporal patterns of China\u2019s cropland during 1990\u20132000: An analysis based on Landsat TM data","volume":"98","author":"Liu","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"4155","DOI":"10.1080\/014311602320567955","article-title":"Improving an operational wheat yield model using phenological phase-based Normalized Difference Vegetation Index","volume":"23","author":"Boken","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"170191","DOI":"10.1038\/sdata.2017.191","article-title":"TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958\u20132015","volume":"5","author":"Abatzoglou","year":"2018","journal-title":"Sci. Data"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.geoderma.2011.01.013","article-title":"A soil particle-size distribution dataset for regional land and climate modelling in China","volume":"171","author":"Shangguan","year":"2012","journal-title":"Geoderma"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/00401706.1970.10488634","article-title":"Ridge Regression: Biased Estimation for Nonorthogonal Problems","volume":"12","author":"Hoerl","year":"1970","journal-title":"Technometrics"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2109","DOI":"10.3390\/rs70202109","article-title":"Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum aestivum L.) Grown under Three Water Regimes","volume":"7","author":"Hernandez","year":"2015","journal-title":"Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1198\/jasa.2004.s339","article-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","volume":"99","author":"Ruppert","year":"2004","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1007\/s10346-015-0614-1","article-title":"Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia","volume":"13","author":"Youssef","year":"2015","journal-title":"Landslides"},{"key":"ref_64","unstructured":"Sun, X., Liu, M., and Sima, Z. (2018). A novel cryptocurrency price trend forecasting model based on LightGBM. Financ. Res. Lett."},{"key":"ref_65","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017, January 4). Lightgbm: A highly efficient gradient boosting decision tree. Proceedings of the Thirty-First Conference on Neural Information Processing System, Long Beach, CA, USA."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1016\/j.energy.2018.07.019","article-title":"Parallel and reliable probabilistic load forecasting via quantile regression forest and quantile determination","volume":"160","author":"Zhang","year":"2018","journal-title":"Energy"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1007\/s10584-014-1136-x","article-title":"Temperature variations and rice yields in China: Historical contributions and future trends","volume":"124","author":"Wang","year":"2014","journal-title":"Clim. Chang."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"S117","DOI":"10.2134\/agronj2006.0370c","article-title":"Application of spectral remote sensing for agronomic decisions","volume":"100","author":"Hatfield","year":"2008","journal-title":"Agron. J."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s10658-011-9878-z","article-title":"Recent advances in sensing plant diseases for precision crop protection","volume":"133","author":"Mahlein","year":"2012","journal-title":"Eur. J. Plant Pathol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.fcr.2012.08.008","article-title":"The use of satellite data for crop yield gap analysis","volume":"143","author":"Lobell","year":"2013","journal-title":"Field Crop. Res."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"3023","DOI":"10.1080\/01431160110104692","article-title":"Large area operational wheat yield model development and validation based on spectral and meteorological data","volume":"23","author":"Manjunath","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.eja.2014.05.008","article-title":"Global warming over 1960\u20132009 did increase heat stress and reduce cold stress in the major rice-planting areas across China","volume":"59","author":"Zhang","year":"2014","journal-title":"Eur. J. Agron."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"You, J., Li, X., Low, M., Lobell, D., and Ermon, S. (2017, January 4). Deep gaussian process for crop yield prediction based on remote sensing data. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11172"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1016\/j.rse.2007.07.019","article-title":"Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains","volume":"112","author":"Wardlow","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"E1327","DOI":"10.1073\/pnas.1320008111","article-title":"Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence","volume":"111","author":"Guanter","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/5\/750\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:01:36Z","timestamp":1760173296000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/5\/750"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,25]]},"references-count":75,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["rs12050750"],"URL":"https:\/\/doi.org\/10.3390\/rs12050750","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,25]]}}}