# Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method  
[PERSON]  
1 Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China; [EMAIL_ADDRESS] (C.H. );  
[EMAIL_ADDRESS] (X.C. ); [EMAIL_ADDRESS] (O.C. ); [EMAIL_ADDRESS] (X.W. );  
1  
[PERSON]  
1 Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China; [EMAIL_ADDRESS] (C.H. );  
[EMAIL_ADDRESS] (X.C. ); [EMAIL_ADDRESS] (O.C. ); [EMAIL_ADDRESS] (X.W. );  
1  
[PERSON]  
1 Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China; [EMAIL_ADDRESS] (C.H. );  
[EMAIL_ADDRESS] (X.C. ); [EMAIL_ADDRESS] (O.C. ); [EMAIL_ADDRESS] (X.W. );  
1  
[PERSON]  
1 Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China; [EMAIL_ADDRESS] (C.H. );  
[EMAIL_ADDRESS] (X.C. ); [EMAIL_ADDRESS] (O.C. ); [EMAIL_ADDRESS] (X.W. );  
1  
[PERSON]  
1 Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China; [EMAIL_ADDRESS] (C.H. );  
[EMAIL_ADDRESS] (X.C. ); [EMAIL_ADDRESS] (O.C. ); [EMAIL_ADDRESS] (X.W. );  
1  
Footnote 1: email: {j.s.j.} Footnote 2: email: {j.s.j.} Footnote 3: email: {j.s.j.} Footnote 4: email: {j.s.j.} Footnote 5: email: {j.s.j.} Footnote 6: email: {j.s.j.} Footnote 7: email: {j.s.j.} In light of this, timely and accurate crop output forecasts on a local to regional scale are becoming more and more crucial [6]. Traditional crop yield information statistics mostly employ techniques like field observation and sampling surveys [7]. Although the statistical results are quite reliable and these approaches are less susceptible to weather change, survey coverage is limited. Additionally, it is difficult to update the data on a timely basis, which makes it difficult to fulfill the task of the dynamic estimation of large-area agricultural yields [8]. They also require a lot of people and material resource support. With the development of satellite remote sensing technologies over the past few decades, agricultural yield estimation has become more accurate, efficient, and affordable [9; 10]. The growth of crops can be continuously monitored, and satellite remote sensing technologies can continuously provide information on the vegetation's surface in regard to both time and space [11; 12]. The normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) are biological metrics that are closely connected to crop yield and can be obtained by using remote sensing image analysis [13]. For estimating crop yields using satellite remote sensing technologies, four widely used methods are: crop growth models, statistical models, machine learning algorithms, and light use efficiency models (LUE) [12; 14]. The statistical model uses historical yield data and several variables [15] to construct a regression relationship between them, such as the vegetation index (VI) and land surface temperature (LST). It has a high degree of estimation accuracy for crop yields in the research area, but it must be re-calibrated when used in other locations and is unsuitable for large-area simulations [16; 17]. In recent years, machine learning algorithms have become more popular, particularly deep neural networks, to forecast crop yields. Studies have shown that these algorithms can predict county-level yields up to four months in advance of harvest [18; 19; 20]. However, it needs a lot of historical data to be trained, and the outcomes of the training on a particular period and region cannot be extended to other times and regions [21; 22].