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Arid zone grassland is a crucial component of terrestrial ecosystems and plays a significant role in ecosystem protection and soil erosion prevention. However, accurately mapping grassland spatial information in arid zones presents a great challenge. The accuracy of remote sensing grassland mapping in arid zones is affected by spectral variability caused by the highly diverse landscapes. In this study, we explored the potential of a rectangular tile classification model, constructed using the random forest algorithm and integrated images from Sentinel-1A (synthetic aperture radar imagery) and Sentinel-2 (optical imagery), to enhance the accuracy of grassland mapping in the semiarid to arid regions of Ordos, China. Monthly Sentinel-1A median value images were synthesised, and four MODIS vegetation index mean value curves (NDVI, MSAVI, NDWI and NDBI) were used to determine the optimal synthesis time window for Sentinel-2 images. Seven experimental groups, including 14 experimental schemes based on the rectangular tile classification model and the traditional global classification model, were designed. By applying the rectangular tile classification model and Sentinel-integrated images, we successfully identified and extracted grasslands. The results showed the integration of vegetation index features and texture features improved the accuracy of grassland mapping. The overall accuracy of the Sentinel-integrated images from EXP7-2 was 88.23%, which was higher than the accuracy of the single sensor Sentinel-1A (53.52%) in EXP2-2 and Sentinel-2 (86.53%) in EXP5-2. In all seven experimental groups, the rectangular tile classification model was found to improve overall accuracy (OA) by 1.20% to 13.99% compared to the traditional global classification model. This paper presents novel perspectives and guidance for improving the accuracy of remote sensing mapping for land cover classification in arid zones with highly diverse landscapes. The study presents a flexible and scalable model within the Google Earth Engine framework, which can be readily customized and implemented in various geographical locations and time periods. [ABSTRACT FROM AUTHOR]

In the dryland area of the Loess Plateau in northwest China, long-term excessive fertilization has led to soil compaction and nutrient loss, which in turn limits crop yield and soil productivity. To address this issue, we conducted experiments using environmentally friendly organic fertilizer and bacterium fertilizer. Our goal was to investigate the effects of additional organic and bacterium fertilizer inputs on soil water migration, crop root architecture, and yield formation. We implemented six different fertilizer strategies, namely: Nm (mulching, N 30 kg/ha), NPK1m (mulching, N 60 kg/ha; P 30 kg/ha; K 30 kg/ha), NPK2m (mulching, N 90 kg/ha; P 45 kg/ha; K 30 kg/ha), NPKOm (mulching, N 90 kg/ha; P 45 kg/ha; K 30 kg/ha; organic fertilizer 2 t/ha), NPKBm (mulching, N 60 kg/ha; P 30 kg/ha; K 30 kg/ha; bacterium fertilizer 10 kg/ha), and N (N 30 kg/ha; no mulching). The results revealed that the addition of bacterium fertilizer (NPKBm) had a positive impact on soybean root system development. Compared with the other treatments, it significantly increased the total root length, total root surface area, and total root length density by 25.96% ~ 94.89%, -19.63% ~ 36.28%, and 9.36% ~ 28.84%, respectively. Furthermore, NPKBm enhanced soil water consumption. In 2018, water storage during the flowering and podding periods decreased by 12.63% and 19.65%, respectively, while water consumption increased by 0.97% compared to Nm. In 2019, the flowering and harvest periods decreased by 23.49% and 11.51%, respectively, while water consumption increased by 0.65%. Ultimately, NPKBm achieved high grain yield and significantly increased water use efficiency (WUE), surpassing other treatments by 76.79% ~ 78.97% and 71.22% ~ 73.76%, respectively. Subsequently, NPK1m also exhibited significant increases in yield and WUE, with improvements of 35.58% ~ 39.27% and 35.26% ~ 38.16%, respectively. The use of bacterium fertilizer has a profound impact on soybean root architecture, leading to stable and sustainable grain yield production. [ABSTRACT FROM AUTHOR]

China's Loess Plateau regions have experienced severe soil erosion for many decades due to fragmented landscapes, steep slopes, and concentrated rainfall storm-events. Restoring sub-optimal or marginal farming fields, mostly on steep, hilly terrain, to plantation forests has been a long-standing strategic policy in China aimed at rehabilitating degraded environments and reducing soil and water erosion. While there are numerous studies that have focused on the effects of forests at controlling soil erosion at relatively short time scales, few have addressed longer-term effects of plantation forests on reducing runoff and the mechanisms that inhibit erosion. Chinese pine (Pinus tabulaeformis) has been widely planted in abandoned or reclaimed lands that were formerly farmed in Northwest China; however, there is limited knowledge about the effectiveness of the tree species at reducing soil and water erosion. In this study, we examined reduction rates of runoff and erosion by Chinese pine plantation in comparison with agricultural land as a control (i.e., wheat, a dominant agricultural commodity in the region), based on long-term monitoring of modified standard erosion plots with slopes of 10°, 15°, and 20°. Results showed that as the slope of the land increased, rates of erosion increased for both plantation and agricultural land use. However, the runoff and soil erosion rates under Chinese pine plantation forest were about 11% and 60% lower, respectively, than those under agricultural land use of the same slope. Scaling with the slope, the highest reduced runoff and erosion rates by Chinese pine forest were 17% and 72%, respectively, on 20° slope. Also, it was found that runoff rates from the forested land were positively related to erosive rainfall (i.e., rainfall when runoff generated), and varied with forest canopy coverage. The rates of runoff and erosion can be well model led with multiple regression models. Taken together, this study provides insight into the importance and potential of Chinese pine plantations in the conservation of soil and water in China's Loess Plateau. [ABSTRACT FROM AUTHOR]

Human demand for food has been increasing as population grows around the world. Meanwhile, global temperature has been rising with the increase of greenhouse gas (GHG) emissions. Although soil mulching (SM) is an effective method to increase crop yield because it could conserve soil moisture and temperature, it is also an important factor affecting GHG productions and emissions. At present, research results in terms of the impact of SM on agricultural GHG emissions are still inconsistent. Therefore, a meta-analysis was used to quantitatively analyze the impact of SM on crop yield and GHG emissions in China. Overall, SM significantly enhanced not only crop yield, but also GHG emissions. Compared with no soil mulching (NSM), SM improved crop yield by 21.84%, while increased global warming potential (GWP) by 11.38%. To minimize the negative impact of SM on GHG, for maize and wheat in arid, semi-arid and semi-humid zones, it is recommended to use flat full mulching with grave or straw plus drip irrigation under neutral or weakly alkaline soil with bulk density <1.3g cm-3. For rice in humid regions, it is advisable to apply SM to minimize GHG emissions by significantly decreasing CH4 emissions. [ABSTRACT FROM AUTHOR]