###### Abstract
Among the methods of ground-based observation of GPP, the inventory method estimates changes in carbon stocks in terrestrial ecosystems based on comparisons of inventory data from different periods. The lack of long-term continuous inventory data and the uncertainty of the conversion process from sample points to regional scales have led to a large bias in the results at the regional scales [16]. The eddy covariance (EC) technique is considered to be the most stable and accurate method for estimating GPP at the ecosystem scale [17]. It is based on the principle of micrometeorology and directly measures the net CO2 exchange between land ecosystems and the atmosphere over a fixed coverage area. Nevertheless, the number of EC stations is sparse and unevenly distributed, making it challenging to study aspects of GPP at the regional scale [18]. The remote sensing technique is more effective than the ground-based observation method. Remote sensing techniques are well suited to monitor changes in ecosystem GPP at broad spatial scales compared to ground-based observation methods. Over the past 40 years, a series of time-continuous and spatially consistent GPP products have been generated based on satellite remote sensing, providing directly observed data for ecosystem research and management [19]. Thus, in this paper, Moderate-resolution Imaging Spectroradiometer (MODIS) GPP products were used to analyze the study area. Vegetation indices (VIs) based on remotely sensed reflectance, which have been widely used to monitor changes in vegetation growth, can help us to better understand the changes in GPP in areas during periods of drought [20]. Previous studies have shown that the normalized difference vegetation index (NDVI) can indicate the greenness of vegetation and can be used to monitor vegetation growth [21]. Two recently developed indices, near-infrared reflectance of vegetation (NIRV) and kernel NDVI (kNDVI), both of which solve the background contamination problem well, are stronger than NDVI in linking with GPP [22; 23]. NIRV only needs to input two parameters when estimating GPP, which drastically reduces the complexity of GPP estimation. It has been shown that NIRV can replace SIF to study the photosynthesis of vegetation [24]. The coupling between the components of canopy structure that influence NIR reflectance and stress-constrained canopy photosynthetic capacity remains strong at drought stress events, making NIRV and GPP maintain a strong coupling during drought events [24]. kNDVI computes all higher-order relationships for NDVI and resolves the nonlinear relationship with GPP. kNDVI is more highly correlated with GPP and less problematic in terms of noise and instability than products such as NDVI and NIRV [23]. Solar-induced chlorophyll fluorescence (SIF) is a promising index for satellite monitoring of vegetative photosynthesis. It is fundamentally different from VIs. SIF is a product of vegetative photosynthesis, which can reflect the intensity of vegetative photosynthesis and has a very close physiological and metabolic connection with GPP [25; 26; 27; 28; 29; 30]. It is well known that an arid environment will weaken the photosynthesis ability and metabolic function of plants, which in turn will weaken the signal release of SIF. VIs are only sensitive to changes in the canopy structure and chlorophyll concentration vegetation and are not directly related to the photosynthesis of plants. Hence, when drought occurs, the greenness of vegetation does not decrease immediately, resulting in a certain lag effect of vegetation indexes on drought [31; 32; 33]. Compared with VIs, SIF has a more sensitive response to drought and is more suitable for monitoring changes in GPP during drought. China has one of the widest distribution of ecologically fragile areas in the world, accounting for 22% of the national territory, with diverse types of fragility and severe vulnerability [34]. The Loess Plateau in China has experienced severe vegetation loss, soil erosion, and land degradation [35].