{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:44:10Z","timestamp":1760168650176,"version":"build-2065373602"},"reference-count":77,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T00:00:00Z","timestamp":1631059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFD0300101"],"award-info":[{"award-number":["2016YFD0300101"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41901342","31571565","31671585"],"award-info":[{"award-number":["41901342","31571565","31671585"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017055","name":"National Natural Science Foundation of China-Shandong Joint Fund","doi-asserted-by":"publisher","award":["ZR2017ZB0422"],"award-info":[{"award-number":["ZR2017ZB0422"]}],"id":[{"id":"10.13039\/100017055","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Estimating yield potential (Yp) and quantifying the contribution of suboptimum field managements to the yield gap (Yg) of crops are important for improving crop yield effectively. However, achieving this goal on a regional scale remains difficult because of challenges in collecting field management information. In this study, we retrieved crop management information (i.e., emerging stage information and a surrogate of sowing date (SDT)) from a remote sensing (RS) vegetation index time series. Then, we developed a new approach to quantify maize Yp, total Yg, and the suboptimum SDT-induced Yg (Yg0) using a process-based RS-driven crop yield model for maize (PRYM\u2013Maize), which was developed in our previous study. PRYM\u2013Maize and the newly developed method were used over the North China Plain (NCP) to estimate Ya, Yp, Yg, and Yg0 of summer maize. Results showed that PRYM\u2013Maize outputs reasonable estimates for maize yield over the NCP, with correlations and root mean standard deviation of 0.49 \u00b1 0.24 and 0.88 \u00b1 0.14 t hm\u22122, respectively, for modeled annual maize yields versus the reference value for each year over the period 2010 to 2015 on a city level. Yp estimated using our new method can reasonably capture the spatial variations in site-level estimates from crop growth models in previous literature. The mean annual regional Yp of 2010\u20132015 was estimated to be 11.99 t hm\u22122, and a Yg value of 5.4 t hm\u22122 was found between Yp and Ya on a regional scale. An estimated 29\u201342% of regional Yg in each year (2010\u20132015) was induced by suboptimum SDT. Results also show that not all Yg0 was persistent over time. Future studies using high spatial-resolution RS images to disaggregate Yg0 into persistent and non-persistent components on a small scale are required to increase maize yield over the NCP.<\/jats:p>","DOI":"10.3390\/rs13183582","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T21:28:45Z","timestamp":1631136525000},"page":"3582","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Remote Sensing-Based Quantification of the Summer Maize Yield Gap Induced by Suboptimum Sowing Dates over North China Plain"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9047-4247","authenticated-orcid":false,"given":"Sha","family":"Zhang","sequence":"first","affiliation":[{"name":"Research Center for Remote Sensing Information and Digital Earth, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3477-7884","authenticated-orcid":false,"given":"Yun","family":"Bai","sequence":"additional","affiliation":[{"name":"Research Center for Remote Sensing Information and Digital Earth, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2894-9627","authenticated-orcid":false,"given":"Jiahua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Research Center for Remote Sensing Information and Digital Earth, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.fcr.2012.09.009","article-title":"Yield gap analysis with local to global relevance\u2014A review","volume":"143","author":"Cassman","year":"2013","journal-title":"Field Crop Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.fcr.2015.10.020","article-title":"Contribution of persistent factors to yield gaps in high-yield irrigated maize","volume":"186","author":"Farmaha","year":"2016","journal-title":"Field Crop Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.eja.2018.04.012","article-title":"Environmental and management variables explain soybean yield gap variability in Central Argentina","volume":"99","author":"Cipriotti","year":"2018","journal-title":"Eur. J. Agron."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.agsy.2006.02.010","article-title":"Yield uncertainty at the field scale evaluated with multi-year satellite data","volume":"92","author":"Lobell","year":"2007","journal-title":"Agric. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.agsy.2016.03.011","article-title":"Exploring genotype, management, and environmental variables influencing grain yield of late-sown maize in central Argentina","volume":"146","author":"Gambin","year":"2016","journal-title":"Agric. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1016\/j.scitotenv.2015.08.145","article-title":"Maize yield gaps caused by non-controllable, agronomic, and socioeconomic factors in a changing climate of Northeast China","volume":"541","author":"Liu","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.scitotenv.2017.10.284","article-title":"Reduced irrigation increases the water use efficiency and productivity of winter wheat-summer maize rotation on the North China Plain","volume":"618","author":"Wang","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cui, X., and Xie, W. (2021). Adapting Agriculture to Climate Change through Growing Season Adjustments: Evidence from Corn in China. Am. J. Agric. Econ., 1\u201324.","DOI":"10.1111\/ajae.12227"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.eja.2018.12.008","article-title":"Soybean-maize succession in Brazil: Impacts of sowing dates on climate variability, yields and economic profitability","volume":"103","author":"Sentelhas","year":"2019","journal-title":"Eur. J. Agron."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.eja.2013.05.005","article-title":"Contribution of cultivar, fertilizer and weather to yield variation of winter wheat over three decades: A case study in the North China Plain","volume":"50","author":"Zhang","year":"2013","journal-title":"Eur. J. Agron."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.agrformet.2014.05.004","article-title":"Declining yield potential and shrinking yield gaps of maize in the North China Plain","volume":"195\u2013196","author":"Wang","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.eja.2015.07.001","article-title":"Quantifying the impact of irrigation on groundwater reserve and crop production\u2014A case study in the North China Plain","volume":"70","author":"Sun","year":"2015","journal-title":"Eur. J. Agron."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"125988","DOI":"10.1016\/j.eja.2019.125988","article-title":"Yield gap analysis simulated for sugar beet-growing areas in water-limited environments","volume":"113","author":"Deihimfard","year":"2020","journal-title":"Eur. J. Agron."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1146\/annurev.environ.041008.093740","article-title":"Crop Yield Gaps: Their Importance, Magnitudes, and Causes","volume":"34","author":"Lobell","year":"2009","journal-title":"Annu. Rev. Environ. Resour."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/S1161-0301(02)00108-9","article-title":"An overview of APSIM, a model designed for farming systems simulation","volume":"18","author":"Keating","year":"2003","journal-title":"Eur. J. Agron."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/bs.agron.2015.11.004","article-title":"A Comprehensive Review of the CERES-Wheat, -Maize and -Rice Models\u2019 Performances","volume":"136","author":"Basso","year":"2016","journal-title":"Adv. Agron."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"766","DOI":"10.2134\/agronj2010.0423","article-title":"CSM-IXIM: A New Maize Simulation Model for DSSAT Version 4.5","volume":"103","author":"Lizaso","year":"2011","journal-title":"Agron. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.agwat.2015.07.001","article-title":"Simulation of crop and water productivity for rice (Oryza sativa L.) using APSIM under diverse agro-climatic conditions and water management techniques in Sri Lanka","volume":"160","author":"Amarasingha","year":"2015","journal-title":"Agric. Water Manag."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kwesiga, J., Grotel\u00fcschen, K., Senthilkumar, K., Neuhoff, D., D\u00f6ring, T.F., and Becker, M. (2020). Rice Yield Gaps in Smallholder Systems of the Kilombero Floodplain in Tanzania. Agronomy, 10.","DOI":"10.3390\/agronomy10081135"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.fcr.2015.04.013","article-title":"Decomposing maize yield gaps differentiates entry points for intensification in the rainfed mid-hills of Nepal","volume":"179","author":"Devkota","year":"2015","journal-title":"Field Crop. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.eja.2018.12.003","article-title":"Downscaling rice yield simulation at sub-field scale using remotely sensed LAI data","volume":"103","author":"Gilardelli","year":"2019","journal-title":"Eur. J. Agron."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"6335","DOI":"10.1080\/01431161.2010.508800","article-title":"Yield estimation of winter wheat in the North China Plain using the remote-sensing\u2013photosynthesis\u2013yield estimation for crops (RS\u2013P\u2013YEC) model","volume":"32","author":"Wang","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.agrformet.2018.03.014","article-title":"BESS-Rice: A remote sensing derived and biophysical process-based rice productivity simulation model","volume":"256\u2013257","author":"Huang","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/10106049.2016.1222633","article-title":"A Comparative Analysis of the NDVIg and NDVI3g in Monitoring Vegetation Phenology Changes in the Northern Hemisphere","volume":"33","author":"Chang","year":"2016","journal-title":"Geocart. Internat."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.agsy.2012.09.003","article-title":"Satellite detection of earlier wheat sowing in India and implications for yield trends","volume":"115","author":"Lobell","year":"2013","journal-title":"Agric. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ji, Z., Pan, Y., Zhu, X., Wang, J., and Li, Q. (2021). Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index. Sensors, 21.","DOI":"10.3390\/s21041406"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.fcr.2015.07.004","article-title":"Using satellite remote sensing to understand maize yield gaps in the North China Plain","volume":"183","author":"Zhao","year":"2015","journal-title":"Field Crop Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/S0168-1923(02)00138-7","article-title":"Soil, climate, and management impacts on regional wheat productivity in Mexico from remote sensing","volume":"114","author":"Lobell","year":"2002","journal-title":"Agric. For. Meteorol."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1007\/s42106-020-00095-4","article-title":"Yield Gap Analysis Using Remote Sensing and Modelling Approaches: Wheat in the Northwest of Iran","volume":"14","author":"Dehkordi","year":"2020","journal-title":"Int. J. Plant Prod."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"112174","DOI":"10.1016\/j.rse.2020.112174","article-title":"A million kernels of truth: Insights into scalable satellite maize yield mapping and yield gap analysis from an extensive ground dataset in the US Corn Belt","volume":"253","author":"Deines","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"He, L., and Mostovoy, G. (2019). Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model over the Southern US. Remote Sens., 11.","DOI":"10.3390\/rs11172000"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/S2095-3119(20)63293-2","article-title":"Developing a process\u2013based and remote sensing driven crop yield model for maize (PRYM\u2013Maize) and its validation over the Northeast China Plain","volume":"20","author":"Zhang","year":"2020","journal-title":"J. Integr. Agric."},{"key":"ref_36","unstructured":"Liu, Z. (2013). The Yield Gaps and Constraint Factors of Spring Maize in Northeast China, China Agricultural University."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2034","DOI":"10.1080\/01431161.2018.1492181","article-title":"Identifying crop planting areas using Fourier-transformed feature of time series MODIS leaf area index and sparse-representation-based classification in the North China Plain","volume":"40","author":"Xun","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.rse.2018.06.005","article-title":"A remote sensing-based two-leaf canopy conductance model: Global optimization and applications in modeling gross primary productivity and evapotranspiration of crops","volume":"215","author":"Bai","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1759","DOI":"10.1016\/j.enconman.2003.09.019","article-title":"Validation of five global radiation models with measured daily data in China","volume":"45","author":"Chen","year":"2004","journal-title":"Energy Convers. Manag."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1002\/2016MS000702","article-title":"Using precipitation, vertical root distribution and satellite-retrieved vegetation information to parameterize water stress in a Penman-Monteith approach to evapotranspiration modelling under Mediterranean climate","volume":"9","author":"Bai","year":"2017","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_41","unstructured":"Supit, I., Hooijer, A.A., and Van Diepen, C.A. (1994). System Description of the WOFOST 6.0 Crop Simulation Model Implemente in CGMS., Joint Research Centre, European Commission. Volume 1: Theory and Algorithms."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.agrformet.2017.11.012","article-title":"Improving maize growth processes in the community land model: Implementation and evaluation","volume":"250\u2013251","author":"Peng","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Shawon, A.R., Ko, J., Ha, B., Jeong, S., Kim, D.K., and Kim, H.-Y. (2020). Assessment of a Proximal Sensing-integrated Crop Model for Simulation of Soybean Growth and Yield. Remote Sens., 12.","DOI":"10.3390\/rs12030410"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.5194\/gmd-8-1139-2015","article-title":"JULES-crop: A parametrisation of crops in the Joint UK Land Environment Simulator","volume":"8","author":"Osborne","year":"2015","journal-title":"Geosci. Model Dev."},{"key":"ref_45","unstructured":"Wang, Y. (2008). Study on Population Quality and Individual Physiology Function of Super High-yielding Maize (Zea mays L.), Shandong Agricultural University."},{"key":"ref_46","first-page":"57","article-title":"Study on Effects of Plant Densities on the Yield and the Related Characters of Maize Hybrids","volume":"15","author":"Yang","year":"2006","journal-title":"Acta Agric. Boreali-Occident. Sin."},{"key":"ref_47","unstructured":"Jing, L. (2011). Study on Population Quality Indices for High or Super High-Yield of Maize, Yangzhou University."},{"key":"ref_48","first-page":"130","article-title":"Effects of Nitrogen Application on Photosynthetic Characteristics, Yield and Nitrogen Use Efficiency in Drip Irrigation of Super High-yield Spring Maize","volume":"24","author":"Chu","year":"2016","journal-title":"J. Maize Sci."},{"key":"ref_49","unstructured":"Huang, Z. (2007). Studies on Photosynthetic and Nutrient Physiological Characteristics of Super-High Yield Summer Maize Hybrids, Shandong Agricultural University."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.fcr.2017.08.011","article-title":"Canopy characteristics of high-yield maize with yield potential of 22.5 Mg ha\u22121","volume":"213","author":"Liu","year":"2017","journal-title":"Field Crop Res."},{"key":"ref_51","unstructured":"Wang, J. (2009). Characteristics on Canopy Vertical Structures and Agronomic Regulation of Super-High Yield of Spring Maize, Inner Mongolia Agricultural University."},{"key":"ref_52","first-page":"1737","article-title":"Effects of planting density on the grain yield and source-sink characteristics of summer maize","volume":"21","author":"Liu","year":"2010","journal-title":"Chin. J. Appl. Ecol."},{"key":"ref_53","unstructured":"Hu, W. (2012). A Study on Characteristics of Radiation and Photosynthesis in Canopy of Super High-Yield Summer Maize, Henan Agricultural University."},{"key":"ref_54","unstructured":"Cao, Y. (2008). Study on the Activity of Photosynthesis Enzymes and Protective Enzymes in Super High Yield Corns and Common Corns, Jilin Agricultural University."},{"key":"ref_55","first-page":"79","article-title":"Physiological Characters of the Summer Maize Population with High Yield in the North Areas of the Yellow River, Huai and Hai Rivers Plain","volume":"20","author":"Jin","year":"2012","journal-title":"J. Maize Sci."},{"key":"ref_56","unstructured":"Wang, Z. (2009). Structural and Functional Properties of Canopy and Root of Super High Yield Spring Maize & Agronomic Water Saving Compensatory Mechanism, Inner Mongolia Agricultural University."},{"key":"ref_57","first-page":"75","article-title":"Study on Growth of Super-high-yield Summer Maize in the Ecological Area of the Yellow River, Huai and Hai Rivers","volume":"19","author":"Chang","year":"2011","journal-title":"J. Maize Sci."},{"key":"ref_58","first-page":"129","article-title":"Yield and Canopy Structure of Maize under the Condition of High Yield Cultivation","volume":"24","author":"Yang","year":"2016","journal-title":"J. Maize Sci."},{"key":"ref_59","unstructured":"Bao, Y. (2006). Study on Canopy Structure and Photosynthesis Character of Super-High-Yield Maize, Jilin Agricultural University."},{"key":"ref_60","first-page":"130","article-title":"Study on Cultivated Technology for Super High Yield of Summer Maize in Huanghuaihai Region","volume":"25","author":"Zhang","year":"2009","journal-title":"Chin. Agric. Sci. Bull."},{"key":"ref_61","first-page":"4367","article-title":"Study on Canopy Structure and Physiological Characteristics of Super-High Yield Spring Maize","volume":"44","author":"Zhang","year":"2011","journal-title":"Sci. Agric. Sin."},{"key":"ref_62","first-page":"158","article-title":"Canopy Characteris tics of Super-high Yielding Maize Under Different Nitrogen Application","volume":"16","author":"Ma","year":"2008","journal-title":"J. Maize Sci."},{"key":"ref_63","first-page":"174","article-title":"Effects of Different Plant Densities on the Photosynthetic-Physiological Characters and Yield Traits in Spring Maize Grown on Super-High Yielding Paddy Field","volume":"26","author":"Li","year":"2011","journal-title":"Acta Agric. Boreali-Sin."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.3724\/SP.J.1006.2010.01226","article-title":"Effects of Planting Density and Row Spacing on Canopy Apparent Photosynthesis of High-Yield Summer Corn","volume":"36","author":"Yang","year":"2010","journal-title":"Acta Agron. Sin."},{"key":"ref_65","unstructured":"Wu, Z. (2002). Creation High-Yield Maize Canopy Structure and Micro-Environmental Factors, Jilin Agricultural University."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.fcr.2012.11.018","article-title":"Estimating crop yield potential at regional to national scales","volume":"143","author":"Wart","year":"2013","journal-title":"Field Crop Res."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.fcr.2012.09.023","article-title":"Understanding production potentials and yield gaps in intensive maize production in China","volume":"143","author":"Meng","year":"2013","journal-title":"Field Crop Res."},{"key":"ref_68","unstructured":"Li, K. (2014). Yield Gap Analysis Focused on Winter Wheat and Summer Maize Rotation in the North China Plain, China Agricultural University."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.agrformet.2017.08.001","article-title":"Improving the accuracy of satellite-based high-resolution yield estimation: A test of multiple scalable approaches","volume":"247","author":"Jin","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.isprsjprs.2017.03.002","article-title":"Mapping plastic greenhouse with medium spatial resolution satellite data: Development of a new spectral index","volume":"128","author":"Yang","year":"2017","journal-title":"Int. J. Photogramm. Remote Sens."},{"key":"ref_71","unstructured":"Li, J., Luo, J., Ming, D., and Shen, Z. (2005, January 29). A new method for merging IKONOS panchromatic and multispectral image data. Proceedings of the International Geoscience and Remote Sensing Symposium, Seoul, Korea."},{"key":"ref_72","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 data blending with MODIS using a spatial and temporal adaptive reflectance fusion model","volume":"113","author":"Hilker","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"012002","DOI":"10.1088\/1742-6596\/772\/1\/012002","article-title":"A novel algorithm for bad pixel detection and correction to improve quality and stability of geometric measurements","volume":"772","author":"Celestre","year":"2016","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1016\/j.agrformet.2010.07.008","article-title":"On the use of statistical models to predict crop yield responses to climate change","volume":"150","author":"Lobell","year":"2010","journal-title":"Agric. For. Meteorol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1038\/nclimate2153","article-title":"A meta-analysis of crop yield under climate change and adaptation","volume":"4","author":"Challinor","year":"2014","journal-title":"Nat. Clim. Chang."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"3048","DOI":"10.1016\/j.scitotenv.2018.09.369","article-title":"A random forest model to predict heatstroke occurrence for heatwave in China","volume":"650","author":"Wang","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"5407","DOI":"10.1002\/joc.6527","article-title":"Intensification of historical drought over China based on a multi-model drought index","volume":"40","author":"Han","year":"2020","journal-title":"Int. J. Climatol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3582\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:59:09Z","timestamp":1760165949000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3582"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,8]]},"references-count":77,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13183582"],"URL":"https:\/\/doi.org\/10.3390\/rs13183582","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,9,8]]}}}