The Loess Plateau (LP) is one of the most representative drylands ecosystems and eroded landscape in the world. Intensive agricultural practices had caused extremely severe soil erosion in the LP. Thus, several practical measurements on soil and water loss control have been implemented since the 1980s to give information on optimizing the land use pattern and configuration, including the building of terraces and sediment-trapping dams, banning grazing and afforestation in bareland. But soil erosion was still severe in cultivated slope cropland. Then, "Grain for Green" project (GGP), one of the most well-known revegetation programs, has been implemented in the LP to restore the fragile ecosystems by converting slope cropland (> 25°) and bareland into grassland, shrubland and woodland since 1999 and expanded to the whole plateau in 2000. In the process of comprehensive management of soil erosion, we should follow the concept of "soil is the foundation, water is the key, vegetation is the sign, industrial structure is the guarantee, soil and water conservation is the goal". Hence, for a better implementation of the GGP, we needs to fully understand its impact on the spatio-temporal distribution of ‘soil-water-vegetation’. Meanwhile, it is necessary to explore the balance between ecological restoration and agricultural production.
The ‘3S’ technology integrates satellite positioning, remote sensing technology, computer technology, and space technology to collect, manage, analyze, and express spatial data. It provides an opportunity to evaluate the improvement of GGP. In this paper, with the support of ‘3S’ technology, we used the sampling data, literature data, remote sensing, meteorological station data, FLUX data, statistical yearbook data, etc. Zhifanggou catchment, the typical small watershed of the LP, and Loess Plateau were selected as our study area. Then, we analyzed the dynamics of soil aggregate stability at two spatial scales, soil moisture, gross primary productivity and agricultural production at the Loess Plateau scale, respectivley. A spatial analysis of soil aggregate stability and erodibility (K factors) was performed to understand the formation processes of aggregates at catchment scale in detail. To understand the formation processes of aggregates by a spatial analysis, a prediction model combining soil properties with natural and human factors should be developed to improve the accuracy of the spatial interpolation of soil aggregate stability indices. Then, we analyzed the contribution of influence factors to soil aggregate stability indices. Spatio-temporal dynamics of soil moisture (SM) and their driving factors were explored by using remote sensing data and trend analysis. The remote sensing data was used to monitor the spatio-temporal dynamics of the GGP and to detect its turning points or break points. The statistical yearbook data were used to analyzed the spatio-temporal variation of agricultural activity. Our results provide theoretical basis for ecological environment construction and sustainable social and economic development. The main results are as follows:
(1) Spherical, Exponential, and Gaussian models were proved to be the best-fit models in describing the spatial variability of aggregate stability. The low nugget values and sill indicate small sampling errors or random variabilities and total variance. The mean weight-diameter (MWD, mm), water-stable aggregates greater than 0.25 mm (WSA>0.25, %) and K factors had a larger range of spatial autocorrelation in 0–10 cm layer than in 10–20 cm layer. The lowest range value was found for K factors at 10–20 cm layer, indicating a maximal heterogeneity and a lowest spatial dependence. Nugget/sill ratios C0/(C0+C) showed a very strong spatial dependence for MWD and WSA>0.25. They are mainly controled by the intrinsic factors. For the K factors, the human impact cannot be ignored, especially in the soil surface. Local Indicators of Spatial Association further proves that strong agricultural activities are closely related to low soil aggregate stability and high soil erodibility. Grazing can significantly reduce the aggregate stability. It is interesting to note that one high-high relationship in 0–10 cm soil layer and low-high relationship in 10–20 cm soil layer were located in shrubland with a one-year revegetation duration. It has a high-high relationship with those woodlands at 0–10 cm soil layer, indicating that the conversion of farmland to shrubland has a stronger positive effect on the surface aggregates than on the sub-surface aggregates after a short-term soil restoration. The landscape metrics can be used as an indicator of land use type and landscape structure. The prediction models combining soil properties with natural and human factors were developed for predicting the sapatial distribution of MWD、WSA>0.25 and K factors. The spatial variability of aggregate stability indices is synergistically affected by the soil, topography, vegetation, and human factors. The spatial variability and prediction modeling of aggregate stability indices are highly dependent on the quantification of land use type and landscape structure (the spatial structure of landscape elements and the connections between the different ecosystem types or landscape elements). It has received little attention in previous studies. The performance of the models was relatively lower when excluded all soil variables for MWD, WSA>0.25, and K factor, but still satisfactory, indicating that the prediction of the spatial distributions of aggregate stability indices with easily available auxiliary data is practicable and effective. The contribution of each influencing factor was further quantified and the direct and indirect contributions were distinguished. The results show that the soil organic carbon (SOC), elevation, slope, corpland percentage of landscape area, grassland percentage of landscape area, pH, oxalate-extractable iron, calcium carbonate, seasonal temperature difference, and topographic wetness index play a major role. In surface soil, there is a stronger direct effect of natural factors. The land use type and landscape structure indirectly contributes to the aggregate stability by directly affecting SOC, slope, etc. And going deeper, the direct and indirect effects of soil properties are both enhanced;
(2) At the Loess Plateau scale, the difference in the human activities intensity leads to different influence factors affecting the soil aggregate stability before and after the GGP. Before the GGP, it is mainly controlled by soil texture, climate factors, SOC, and topographical factors. The effect of soil texture after GGP is more stronger than that before GGP. The effect of land use type and landscape structure changes from insignificant to strong. It proves that human disturbance has a significant effect on the soil aggregate stability. The enhanced imapct of land use types and landscape structure after the GGP further verifies this result. The change of the slope impact sign proves that converting the sloping farmland to forest, shrubland and grassland is beneficial to the improvement of soil structure. In addition, the performance of the linear regression model show that the spatial prediction of aggregate stability at LP scale is difficult, and more detailed planning is needed. This paper is a preliminary exploration of the scale-related research and provides a basis for detailed research in the future.
(3) An integrated method based on a variety of satellite data products had proposed to provide a systematic and quantitative assessment of the revegetation drivers in spatio-temporal dynamics of SM. And this method can be applied to other places without a SM monitoring network. This study confirms the availability of GLEAM SM in evaluating the spatio-temporal variations of SM in the LP. At the 34-year time scale, revegetation plays a dominant role in SM dynamics in vegetated areas, and turns the wet region (MAP > 450 mm) to be dry and dry region to be wet, which is attributable to the differences in vegetation structure, density, growth age, and species.The significantly positive effect of precipitation on SM is only found in bareland and sparsely vegetated area. Evapotranspiration has an important effect on SM in bareland, sparsely vegetated area or densely vegetated area. At the spatial scale, the driving effect of vegetation cover on SM dynamics is relatively weak due to the more significant role of evapotranspiration and precipitation. Evapotranspiration plays a dominant role in SM dynamic in revegetated woodland, especially in the early stage of GGP (From 2000 to 2010), while precipitation and vegetation cover have much greater contributions to SM than evapotranspiration in revegetated grassland. Therefore, our findings highlight the importance of spatial analysis to investigate the interactions between SM and vegetation activity and alert the excessive reliance on afforestation. Our study suggests that vegetation should not be further expanded in semi-humid areas, but should be further restored in arid and semi-arid areas with sparse or excessively sparse vegetation cover.
(4) The applicability of the GLASS GPP dataset in the LP region was first verified by using FLUX monitoring data. Then, trend analysis was used to detect the overall trend of GPP over the past 1982–2015. The results of piecewise function analysis show that the change rate and trend of all pixels are significantly different, and different at different stages, mainly showing a rapid increase first, then slowly increase (turning points), or first increase and then decrease (break points). The average turning points is in 2005 and the average break point is in 2003. The main pixel inflection points/breakpoints are concentrated in 2011–2015. The results emphasize that the methods of GGP need to be different in different geographical locations, and the intensity should be adapted to local conditions. Otherwise, the goal of ecological restoration will not be achieved, but will cause irreversible negative ecological effects. And the vegetation restoration of the main pixel has reached their threshold.
(5) After GGP, the grain yield of the LP has not decreased because of the reduction of planting area. A large-scale county area has shown an increasing trend. One of the reasons is the increase in fertilizer application. From the trend analysis, we knows that the yield of some counties fluctuates greatly. In this case, the maintenance of the GGP is unfavorable. If the farmers' livelihood is relatively simple, more land will be farmed to increase crop yield. To address this, consideration should be given to broaden farmers' livelihoods and reduce their dependence on farming.
Although the GGP on the LP has reduced the cropland area, it has not caused the decrease in crop yield. Given the impact of GGP on ‘soil-water-vegetation’, our paper emphasizes that the GGP implementation needs to be adapted to local conditions. Our results are of great significance for revealing the law of soil erosion, carrying out soil and water conservation work more effectively, reducing sediment, providing successful experiences and suggestions for the development of later management work, and theoretically supporting the ecological and economic sustainable development.