# The Observed Impacts of Wind Farms on Local Vegetation Growth in Northern China
## 4 Discussion
The main reason for this trend may be that the WFs can reduce soil moisture through increasing LST in the study area (Figure 7). In this research, the study area is located in the semi-arid area of Northern China where soil moisture has a decisive effect on vegetation growth [16]. An increase in the LST reduces soil moisture and enhances water stress on vegetation growth. As a result, growth conditions for vegetation growth are suppressed, leading to a lower vegetation index. Furthermore, we also observed decreasing vegetation productivity in the WFs, which may be caused by inhibited photosynthesis through increasing water stress and enhanced autotrophic respiration through increasing nighttime temperatures. Wind speed is the main factor determining energy conversion in wind turbines. Wind turbines have a cut-in and a cut-out wind speed, which are approximately 3-4 m/s and 20 m/s, respectively [10]. The wind speed in the study area is generally 4-8 m/s, meaning that the wind turbines can operate most of the time (Figure 8). Wind direction in the study area can affect the effects of WFs on LST, and thereby affect vegetation growth and productivity. Generally, the winds blow mainly from the southeast and northwest at night and from the southwest during the daytime (Figure 8). The positive LST anomaly  
Figure 6: Summer MODIS LST anomaly differences (°C) between post- and pre-turbine-construction years (2012–2014 minus 2003–2005 averages) at (**a**) \(\sim\)13:30 LT daytime and (**b**) \(\sim\)01:30 LT nighttime. Plus symbols represent pixels with one or more wind turbines. Figure 7: Interannual variations of area mean soil moisture difference between WFPs and NNWFPs (WFPs minus NNWFPs) in the summer for the period 2003–2014. differences all occur to the northwest and southeast of the WFs at night and to the northeast in the daytime (Figure 6). This result indicates that a downwind warming effect is occurring in the study area. To assess the downwind effect on vegetation growth, the SCI values of 5%, 10%, and 15% pixels of the smallest vegetation index anomaly differences located in UWFPs, DWFPs, and NNWFPs are compared with the corresponding SCI thresholds (5.3%, 6.3%, and 14.9%). The higher the SCI value of the DWFPs, the stronger the downwind effect. The SCI of the DWFPs ranges from 6.1% to 10.8% and 14 of the 15 are higher than the corresponding SCI thresholds (6.3%), indicating the existence of a downwind effect on vegetation growth (Table 1). In contrast, only 6 of the 15 SCI values for NNWFPs and none of the SCI values for UWFPs are higher than the corresponding SCI thresholds, indicating that the smallest vegetation index anomaly differences are unlikely to be found in the NNWFPs and UWFPs. The main source of uncertainty in this paper comes from the errors in the MODIS vegetation index and productivity products [20; 21; 24]. Although the MODIS products represent the highest-quality retrieval data from clear-sky conditions, it still contains errors and noise due to clouds, aerosols, and the imperfection of the retrieval algorithms. In addition, there may be discrepancies in determining the positions of the wind turbines. The matching of various kinds of data of different resolutions may also produce errors in the results. However, the spatial and temporal averaging used in this paper can effectively reduce errors and uncertainties. The remaining residuals may not produce a spatial coupling between the layout of WFs and the vegetation index anomaly differences, especially when many different kinds of vegetation indexes are used.