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The accurate assessment of the global gross primary productivity (GPP) of vegetation is the key to estimating the global carbon cycle. Temperature (Ts) and soil moisture (SM) are essential for vegetation growth. It is acknowledged that the global Ts has shown an increasing trend, yet SM has shown a decreasing trend. However, the importance of SM and Ts changes on the productivity of global ecosystems remains unclear, as SM and Ts are strongly coupled through soil‐atmosphere interactions. Using solar‐induced chlorophyll fluorescence (SIF) as a proxy for GPP and by decoupling SM and Ts changes, our investigation shows Ts plays a more important role in SIF in 60% of the vegetation areas. Overall, increased Ts promotes SIF by mitigating the resistance from SM’s reduction. However, the importance of SM and Ts varies, given different vegetation types. The results show that in the humid zone, the variation of Ts plays a more important role in SIF, but in the arid and semi‐arid zones, the variation of SM plays a more important role; in the semi‐humid zone, the disparity in the importance of SM and Ts is difficult to unravel. In addition, our results suggest that SIF is very sensitive to aridity gradients in arid and semi‐arid ecosystems. By decoupling the intertwined SM‐Ts impact on SIF, our study provides essential evidence that benefits future investigation on the factors the influence ecosystem productivity at regional or global scales.

This article aims to explore the applicability of SMMI (Soil moisture monitoring index), MSMMI (Modified soil moisture monitoring index), PDI (Perpendicular drought index), and MPDI (Modified perpendicular drought index) in estimating soil moisture (SM) in farmland. The random forest classifier (RFC) was used to obtain two-stage land cover types maps. The sensitivity of Sentinel-2 spectral bands to the measured SM at a depth of 0-5 cm was optimized by random forest regression (RFR). According to the sensitive bands, SMMI and PDI from different feature spaces were constructed to explore their feasibility for monitoring SM under different land cover types. Secondly, fractional vegetation cover (FVC) in the study area was estimated by nine kinds of FVC estimation models and compared with the measured FVC. The effects of different FVC methods on estimating SM by MSMMI and MPDI were evaluated. The results show that red edge and short-wave infrared (SWIR) bands of Sentinel-2 had irreplaceable effects on the land cover classification. In terms of monitoring SM in bare soil areas, the soil moisture indices with SWIR bands had high correlations with measured SM. For vegetation-covered areas, MSMMI from the FVCgr model (Dimidiate pixel model with red edge bands) and the SWIR1-SWIR2 feature space had the highest correlation with the measured 0-5 cm depth SM. Whether vegetation-covered areas or bare soil areas, the combination of red edge and SWIR bands can effectively improve the estimation accuracy of SM. MSMMI can be used as the best soil moisture monitoring index in the study area. Sentinel-2 images, with great potential, can effectively estimate SM at a depth of 0-5 cm in farmland with complex environments.

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