Vegetation canopies diminish raindrop energy; stems and litter disperse runoff, reducing runoff and erosion [1
]. Root systems improve the resistance of soil to runoff. As a result, re-vegetation is widely applied across the world as one of the most important erosion control measures [2
A widely-renowned ecological rehabilitation program, “Grain for Green Project” (GGP), was initiated by the Chinese government in 1998 to control soil erosion and flashfloods on hill slopes in order to reduce the sedimentation of river beds and to improve the sustainability of the eco-hydrological system on the Loess Plateau [4
]. The GGP has shown remarkable success in terms of ecological restoration and agricultural production, through encouraging local farmers to convert cropland on slopes to grassland and forest by providing subsidies in the form of food and money. It has now been operating for more than 10 years, largely improving the natural environment in parts of the Loess Plateau [5
]. The physical and chemical characteristics of the soil were improved, while vegetation communities were gradually stabilized [6
]. In particular, measures of returning croplands to forest adopted in the GGP have been demonstrated to be effective approaches to soil and water conservation in hilly loess regions [6
Although remarkable achievements on soil erosion control have been obtained by re-vegetation promoted by the GGP, there are still debates on whether re-vegetation on the Loess Plateau positively or negatively influences SMC conditions. Some authors found that plant canopy shadows reduced land surface temperature and soil evaporation and, thereafter, increased SMC [7
]. However, other authors suggested that vegetation cover had negative impacts on SMC. For example, artificial forest land and grassland increase precipitation interception and transpiration. This will increase total water consumption, leading to a negative soil storage balance and, eventually, an enhanced soil desiccation [7
]. The above contradictory findings demonstrate the requirement of additional knowledge on how exactly re-vegetation influences SMC, given that SMC crucially constrains the growth and distribution of vegetation and the erosion risk. Moreover, it should be noted that these findings were drawn mainly based on traditional experimental methods (e.g., on-site monitoring), which were suitable for the investigation of soil moisture change at small scales (e.g., patch, hillslope scales) rather than large scales (e.g., regional, global scales). However, given the great spatial variability of soil moisture [10
], it may be a mistake to apply the findings obtained at small scales elsewhere to eventually achieve conclusions on SMC change and its reactions to vegetation cover change over a large space such as the Loess Plateau. Thus, there was a need to explore the response of the SMC to re-vegetation promoted by the GGP across the whole of the Loess Plateau.
Remotely-sensed datasets are widely acknowledged as a useful product to detect vegetation cover, soil moisture, and their changes over large areas. There have been many algorithms developed for the estimation of vegetation cover change based on remote sensing data [11
]. Normalized difference vegetation index (NDVI), which reflects green vegetation photosynthetic activity [12
], has been adopted in numerous studies that require the spatial pattern of vegetation and plant phenology over large areas as a useful and reliable monitoring tool for vegetation health and dynamics. Microwave remote sensing technology provides an effective means of detecting soil moisture information in shallow soil layers, and has been widely applied across arid and semi-arid regions [10
]. Although there are some weaknesses with microwave sensors, such as the coarse temporal resolution and the requirement of complex calibration process and techniques, they offer two major advantages over other methods: (1) long wavelengths (i.e.
, low frequency) of microwave sensors penetrate deeper into the soil than the sensors using other bands such as visible light, near-infrared, infrared, etc.
, and are less likely to be affected by cloud cover or daily time-only acquisition conditions; (2) unlike high-frequency-band sensors, which are highly sensitive to vegetation coverage, microwave sensors are less sensitive to vegetation coverage and, therefore, better account for the SMC [14
]. Along with the evolvement of the microwave remote sensing technology, there have been a variety of methods developed for the interpretation and utilization of datasets produced by microwave sensors in the past several decades. In this study, the SWI, defined as the percentage of saturation soil moisture to describe the average profile SMC between 0 and 1 m deep, was employed to investigate the regional differences and changes in soil moisture. The SWI was produced with a semi-empirical model established by Wagner et al.
] based on surface soil moisture measurements retrieved from back-scatterometer coefficients of the Europe Remote Sensing Satellite (ERS-1/2) and Meteorological Operational Satellite Program (MetOp).
This paper aims to understand the impact of large-scale re-vegetation during the GGP on soil moisture condition based on NDVI and SWI datasets. We firstly examined the spatial pattern of SMC and corresponding vegetation coverage and precipitation over two chosen periods since the beginning of GGP (i.e., 1998–2000 and 2008–2010), and then investigated the relationship between changes in soil moisture, vegetation coverage and precipitation.
Soil moisture is a part of hydrological cycle, which is a complex system. There are, therefore, many factors driving changes in soil moisture, such as temperature, precipitation, vegetation, topography, soil texture, etc. In this study, we only explored the relationship among soil moisture, vegetation coverage, and precipitation. Further study is still desirable to examine the effects of other factors on soil moisture.
4.1. Increased NDVI and Decreased SWI
It was found that soil moisture decreased and the vegetation increased for about 57.65% of the Loess Plateau. There was 24.85% of the area with decreased precipitation, and 32.80% of the area with increased precipitation. As a result, SWI change ratio was found to be −5.86% and 3.43% for 24.85% and 32.80% of the Loess Plateau, respectively (Table 4
For the 24.85% of the Loess Plateau, the climate became drier from 1998–2000 to 2008–2010. Additionally, fast-growing vegetation consumed large amounts of soil moisture. Soil moisture is not timely supplied by precipitation, this will inevitably enhance the shortage of soil moisture, and sometimes resulted in a dry soil layer up to a depth of 2 m or more [36
]. For the 32.80% of the Loess Plateau, precipitation increased slightly from 1998–2000 to 2008–2010. However, artificial afforestation, which has been demonstrated to consume soil moisture quickly and the amount of soil moisture consumed could be higher than local precipitation [8
]. This may be the major reason for the decrease of soil moisture in the area under increased precipitation. It is, therefore, implied that vegetation may contribute largely to the decrease of soil moisture on the Loess Plateau. This finding is supported by other studies in arid and semi-arid areas. For example, on the Loess Plateau, Wang (2010) [8
] found that increased vegetation cover should decrease surface soil moisture when the precipitation did not change substantially.
If areas covered by the GGP expand, there will be more vegetation planted in the future. The demand of soil moisture will thus be increased, leading to a more serious soil moisture deficit, which possibly constraints the growth and distribution of vegetation and, thus, further re-vegetation in the region. In some cases this has resulted in mortality of the vegetation (e.g., [37
]), and in other cases while trees survive, their growth is stunted so that some patches of 30 year old plantation trees are only about 20% of their normal height-colloquially referred to as “little old man trees” [38
]. It is, therefore, suggested that soil moisture awareness should be kept in mind when carrying out re-vegetation in China’s arid and semi-arid regions.
4.2. Increased NDVI and SWI
NDVI and SWI were found to increase at the same time across 23.34% of the Loess Plateau, which was restricted in the north of the loess plateau. On this area, although the annual precipitation is less than 200 mm, it increased from 1998–2000 to 2008–2010 in the majority of the area. Additionally, the flow of the Yellow River is often used for irrigation of the farmland in the area, recharging the soil moisture to a certain degree. Furthermore, soil moisture increased because the implementation of land reclamation by human activities at the coal mining regions on the Loess Plateau [40
]. The above three factors offset the adverse impact of vegetation coverage increase on soil moisture and eventually lead to an improved soil water condition.
4.3. Decreased NDVI and SWI
There is about 14.99% of the Loess Plateau with decreased NDVI and SWI. It had a mean annual precipitation of 240 mm during the study periods with precipitation on 71.5% of the area occurring from July to September. The majority of the area was subject to a decreased precipitation from 1998–2000 to 2008–2010. Natural vegetation is sparse and low, vegetation growth is difficult in this dry and vulnerable environment [41
]. The area experiences severe drought events frequently, which introduces many problems for local agricultural production. It is, therefore, inferred that the decrease of soil moisture is mainly driven by the drier climate given the low vegetation coverage is unlikely to change the local soil water condition significantly.
4.4. Decreased NDVI and Increased SWI
It was also found that the soil moisture increased and the vegetation decreased across about 4.02% of the Loess Plateau. The majority of the area experienced a wetter climate in 2008–2010 compared to 1998–2000. Therefore, the increased SWI may be due to more precipitation and less water consumption, resulting from increased precipitation and decreased vegetation coverage.
In this study, the spatiotemporal pattern of SWI, NDVI, precipitation, and their interactions were investigated for the Chinese Loess Plateau between 1998–2000 and 2008–2010 based on the two remote sensing datasets and a measurement dataset. The results showed that average annual NDVI of the Loess Plateau significantly increased across 80.99% of the area from 1998–2000 to 2008–2010, indicating that vegetation restoration was remarkable after the implementation of the GGP. Precipitation conditions did not change significantly (the difference is 13.10 mm). However, SWI, which was retrieved from ECV SM dataset, showed that soil moisture decreased for 72.64% of the Loess Plateau, demonstrating that soil experienced a tendency of drought in the first ten year of the 21st Century. Our result demonstrated there was about 57.65% of the area where the vegetation coverage increased and the surface soil moisture decreased, of which near a half experienced increasing precipitation. There was about 4.02% of the area which experienced increased soil moisture and decreased vegetation coverage. These findings suggested that vegetation coverage was important for and negatively related to soil moisture variation on the Loess Plateau. There was about 23.34% of area with the vegetation coverage and surface soil moisture increasing. In this area, human activities, such as land reclamation, largely affected the soil moisture. Therefore, soil water condition was improved during the study period on these areas. The rest 14.99% showed a decreased trend in the vegetation coverage and soil moisture, which was mainly distributed in the western portion of the Loess Plateau where the mean annual precipitation reduced between 1998–2000 and 2008–2010. Here the drought, which caused vegetation degradation, may be the major driver to encourage soil moisture deficit. The results suggested that excessive reliance on afforestation was risky for the improvement of vegetation coverage in arid and semi-arid regions and we, thus, need to focus on the soil moisture conditions during large scale re-vegetation.