Estimating the Applicability of NDVI and SIF to Gross Primary Productivity and Grain-Yield Monitoring in China  
[PERSON]  
1 State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China; [EMAIL_ADDRESS] (Z.Z. ); [EMAIL_ADDRESS] (Y.W.) [PERSON]  
3 Yellow River Engineering Consulting Co. Ltd., Zhengzhou 450003, China; [EMAIL_ADDRESS]  
[PERSON]  
4 Center for Climate Physics, Institute for Basic Science, Busan 46241, Korea; [EMAIL_ADDRESS]  
[PERSON]  
1 State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China; [EMAIL_ADDRESS] (Z.Z. ); [EMAIL_ADDRESS] (Y.W.) [PERSON]  
5 School of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin 150006, China; [EMAIL_ADDRESS]  
[PERSON]  
1 State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China; [EMAIL_ADDRESS] (Z.Z. ); [EMAIL_ADDRESS] (Y.W.) Footnote 1: [[https://www.mdpi.com/journal/remotesensing](https://www.mdpi.com/journal/remotesensing)]([https://www.mdpi.com/journal/remotesensing](https://www.mdpi.com/journal/remotesensing))  
Footnote 2: [[https://www.mdpi.com/](https://www.mdpi.com/)]([https://www.mdpi.com/](https://www.mdpi.com/))  
Footnote 16: [[https://www.mdpi.com/journals/rem](https://www.mdpi.com/journals/rem)]([https://www.mdpi.com/journals/rem](https://www.mdpi.com/journals/rem))level of accuracy and reliability [17]. This method can even be used to evaluate the accuracies of other methods [18]. For example, [PERSON] et al. [18] provided sampling protocols to effectively improve the estimation accuracy of the forest leaf area index. However, spot sampling has some disadvantages; it is not a continuous vegetation monitor [17], it is unsuitable for monitoring vegetation over a large area [19], and it requires substantial resources (time and materials) [17]. With the development of remote-sensing technologies, vegetation changes can be effectively monitored by using equipment carried on various platforms, such as unmanned aerial vehicles, aircrafts, and satellites [15; 20; 21]. For example, [PERSON] et al. [15] used an unmanned aerial vehicle to monitor the vegetation dynamics in the foreland of the Urumqi Glacier, and [PERSON] et al. [21] used an unmanned aerial system to monitor corn and soybean crops on the Cornell Musgrave Research Farm in New York, USA. Different global-scale, satellite-derived datasets were used by [PERSON] et al. [13] to compare trends in vegetation seasonality. The rapid development of satellite technologies has resulted in the availability of time-series products with higher precision and longer temporal domains [14; 22; 23]. Many vegetation indicators have been used to describe vegetation characteristics, such as Normalized Difference Vegetation Index (NDVI) [24], Vegetation Condition Index [25], Vegetation Health Index [26], Enhanced Vegetation Index [27] and solar-induced chlorophyll fluorescence (SIF) [14]. Among the above vegetation indicators, NDVI and SIF have been widely used to describe vegetation dynamics in drought and crop-yield studies [4; 13; 14; 28; 29]. Regarding drought research, [PERSON] et al. [29] employed NDVI to reveal the effects of agricultural drought on forest, grassland, and cropland ecosystems over China, and [PERSON] et al. [30] used NDVI to obtain the response time and degree of vegetation to meteorological drought conditions in North China. [PERSON] et al. [31] applied SIF to analyze the sensitivity of vegetation in the Hulun Buir Grassland, China, and indicated that SIF was more sensitive than NDVI when monitoring the responses of vegetation to drought. [PERSON] et al.