{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T03:42:10Z","timestamp":1764906130209,"version":"build-2065373602"},"reference-count":80,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T00:00:00Z","timestamp":1673308800000},"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":["2022YFD2001101","2021YFB3901303","42171325","41771468","41801393","2020N5002"],"award-info":[{"award-number":["2022YFD2001101","2021YFB3901303","42171325","41771468","41801393","2020N5002"]}],"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":["2022YFD2001101","2021YFB3901303","42171325","41771468","41801393","2020N5002"],"award-info":[{"award-number":["2022YFD2001101","2021YFB3901303","42171325","41771468","41801393","2020N5002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science Bureau of Fujian Province","award":["2022YFD2001101","2021YFB3901303","42171325","41771468","41801393","2020N5002"],"award-info":[{"award-number":["2022YFD2001101","2021YFB3901303","42171325","41771468","41801393","2020N5002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Maize yield in China accounts for more than one-fourth of the global maize yield, but it is challenged by frequent extreme weather and increasing food demand. Accurate and timely estimation of maize yield is of great significance to crop management and food security. Commonly applied vegetation indexes (VIs) are mainly used in crop yield estimation as they can reflect the greenness of vegetation. However, the environmental pressures of crop growth and development are difficult to monitor and evaluate. Indexes for water content, pigment content, nutrient elements and biomass have been developed to indirectly explain the influencing factors of yield, with extant studies mainly assessing VIs, climate and water content factors. Only a few studies have attempted to systematically evaluate the sensitivity of these indexes. The sensitivity of the spectral indexes, combined indexes and climate factors and the effect of temporal aggregation data need to be evaluated. Thus, this study proposes a novel yield evaluation method for integrating multiple spectral indexes and temporal aggregation data. In particular, spectral indexes were calculated by integrating publicly available data (remote sensing images and climate data) from the Google Earth Engine platform, and county-level maize yields in China from 2015 to 2019 were estimated using a random forest model. Results showed that the normalized moisture difference index (NMDI) is the index most sensitive to yield estimation. Furthermore, the potential of adopting the combined indexes, especially NMDI_NDNI, was verified. Compared with the whole-growth period data and the eight-day time series, the vegetative growth period and the reproductive growth period data were more sensitive to yield estimation. The maize yield in China can be estimated by integrating multiple spectral indexes into the indexes for the vegetative and reproductive growth periods. The obtained R2 of maize yield estimation reached 0.8. This study can provide feature knowledge and references for index assessments for yield estimation research.<\/jats:p>","DOI":"10.3390\/rs15020414","type":"journal-article","created":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T03:02:06Z","timestamp":1673319726000},"page":"414","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["National Scale Maize Yield Estimation by Integrating Multiple Spectral Indexes and Temporal Aggregation"],"prefix":"10.3390","volume":"15","author":[{"given":"Yuhua","family":"He","sequence":"first","affiliation":[{"name":"Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, School of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China"}]},{"given":"Bingwen","family":"Qiu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, School of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China"}]},{"given":"Feifei","family":"Cheng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, School of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China"}]},{"given":"Chongcheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, School of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China"}]},{"given":"Yu","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, School of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China"}]},{"given":"Dongshui","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China"}]},{"given":"Li","family":"Lin","sequence":"additional","affiliation":[{"name":"Fujian Jingwei Digital Technology Corporation, Fuzhou 350001, China"}]},{"given":"Aizhen","family":"Xu","sequence":"additional","affiliation":[{"name":"Fujian Jingwei Digital Technology Corporation, Fuzhou 350001, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1126\/science.1185383","article-title":"Food Security The Challenge of Feeding 9 Billion People","volume":"327","author":"Godfra","year":"2010","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"86885","DOI":"10.1109\/ACCESS.2020.2992480","article-title":"Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications","volume":"8","author":"Elavarasan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_3","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":"Kersebaum","year":"2013","journal-title":"Field Crops Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"108666","DOI":"10.1016\/j.agrformet.2021.108666","article-title":"Integrating satellite-derived climatic and vegetation indices to predict smallholder maize yield using deep learning","volume":"311","author":"Zhang","year":"2021","journal-title":"Agric. 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