**5. Conclusions**

An approach for yield forecasting from plot level to large scale was developed by incorporating remote sensing LST of a measured biological indicator (DBA and FBA) into corresponding logistic models. The main conclusions are as follows:


**Supplementary Materials:** The following supporting information can be downloaded at: https://www. mdpi.com/article/10.3390/rs15041025/s1, Figure S1: Rules of decision tree classification for Landsat8 images based on the values of NDVI and LSWI in ROI in 2017 (No.268 (2017/9/25) and No.156 (2017/6/5)); Figure S2: Map of HI obtained by the kriging interpolation method in Changchun area; Figure S3: Regressions between the LST from MOD11A1 product and the observed Tc in field in 2017. (**a**) H4; (**b**) H5; Figure S4: Regressions between the *T*LST calculated by the remote sensing instantaneous LST values at 11:30 a.m. (interpolation results) and daily average values observed (*T*canopy) from the CTMS system in 2017. (**a**) H4; (**b**) H5; Table S1: Data list of remote sensing images in 2017; Table S2: Values of harvest index (HI) of maize collected or measured in Changchun and its surrounding areas; Equations (S1)–(S5): Supplementary calculation formula of Section 2.6.

**Author Contributions:** Conceptualization, J.C.; data curation, H.C.; formal analysis, J.C.; funding acquisition, B.Z.; investigation, H.C.; methodology, H.C.; project administration, Z.W.; resources, J.C.; supervision, D.X.; validation, H.C.; writing—original draft, H.C.; writing—review and editing, J.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Key Research Program (grant number NK2022180403), the Project of National Natural Science Foundation of China (grant numbers 52130906, 51979286), and the Institute-City Cooperation Program (grant number HBAT02242202010-CG).

**Data Availability Statement:** The data that support the findings of this work are available from the corresponding author upon reasonable request.

**Acknowledgments:** The authors would like to thank the anonymous reviewers for their long-term guidance and constructive comments. The authors are grateful to the Ministry of Natural Resources of China for providing the land-cover map in Changchun and the Statistic Bureau of Jilin Province for providing the statistical data of crop areas. The authors also acknowledge the United States Geological Survey (USGS) for offering the images of Landsat 8 and MOD11A1 products.

**Conflicts of Interest:** The authors declare no conflict of interest.
