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The growing population and northward shifts in the center of grain production collectively contribute to the arising farmland use intensity of the farming–pastoral ecotone of Northern China (FPENC). Consequently, it poses a great threat to the vulnerable ecosystem of FPENC. Thus, farmland use intensity monitoring is a top priority to practice sustainable farming. In this study, we establish an indicator system designed to evaluate farmland use intensity in Ulanqab, located in the central part of FPENC. This system includes three single-year indicators (the degree of coupling between effective rainfall and crop water requirement (Dcrr), irrigation intensity (Iri) and crop duration (Cd)) and two multi-year indicators (the frequency of adopting the green-depressing cropping system (Gf) and rotation frequency (Rf)). We mapped five farmland use intensity indicators in Ulanqab from 2010 to 2019 using satellite imagery and other ancillary data. Then, the farmland use patterns were recognized by applying the self-organizing map algorithm. Our results suggest that the mapping results of crop types, center pivot irrigation (CPI), and irrigated areas are reasonably accurate. Iri, Cd, and Rf experienced an increase of 31 m3/hm2, 1 day, and 0.06 in Ulanqab from 2010 to 2019, respectively, while Dcrr and Gf witnessed a decrease of 0.002 and 0.004, respectively. That is, farmers are progressively inclined to higher farmland use intensity. Moreover, spatial heterogeneity analysis shows that Northern Ulanqab owned higher Dcrr, Iri, Cd, and Rf, and lower Gf than the southern part. We conclude the paper by discussing the implications of the results for areas with different farmland use intensity patterns. [ABSTRACT FROM AUTHOR]

The North China agro–pastoral zone is a large, ecologically fragile zone in the arid and semi-arid regions. Quantitative remote sensing inversion of soil organic carbon (SOC) in this region can facilitate understanding of the current status of degraded land restoration and provide data support for carbon cycling research in the region. Deep learning (DNN) for SOC inversion has been W.a hot topic over the past decade, but there have been few studies at the regional scale in the arid and semi-arid zones. In this study, a DNN model with five hidden layers and five skip connections was established using 644 spatially distributed SOC samples and Landsat 8 OLI imagery. The model was compared with the random forest algorithm in terms of generalization ability. The main conclusions were as follows: 1. The DNN algorithm can establish a high-precision SOC inversion model (R2 = 0.52, RMSE = 0.7), with 90% of errors concentrated in the range of −2.5 to 2.5 kg·C/m2; 2. the Boruta variable-screening algorithm can effectively improve the model accuracy of the random forest algorithm, but due to the DNN's better ability to mine hidden information in the data, the improvement effect on the DNN model accuracy is limited; 3. the SOC samples in arid and semi-arid areas are highly positively skewed, with a significant impact on the modeling accuracy of DNN, and conversion is required to obtain a model with better generalization ability; and 4. in arid and semi-arid regions, SOC has a weak correlation with vegetation indices but a stronger correlation with temperature, elevation, and aridity. This study established a reliable deep learning model for SOC density in a large arid and semi-arid region, providing a reference and framework for the establishment of SOC inversion models in other regions. [ABSTRACT FROM AUTHOR]

Understanding the impact of climate change on terrestrial wet and dry changes and the relationship between the two is of great significance to the sustainable development of terrestrial ecosystems. The farming-pastoral ecotone of northern China (FPENC) is an area that is sensitive to climate change, suffering from perennial drought and a clear aridification trend. Unlike previous single-factor, single-timescale studies, we identified aridification in the region based on a dataset established by remote sensing and ground-based monitoring stations from a combination of two perspectives: climate and soil. The results show that, in terms of climate, the period from 2000 to 2019 was the driest in the region during the last 120 years , and the summer drought was the most severe and shifted from a summer to spring drought; in terms of soil, the soil aridification trend in the region was severe, with 16.1% of the areas becoming significantly drier (P < 0.1) among the years and 41.6% in spring, respectively. Similar to climate change, soils exhibited recessive aridification due to the counterbalancing effect of the dry and wet seasons within the year. Then the coupling relationship between climate change and soil aridification was established in time and space. Moreover, the spatiotemporal response patterns of both were obtained. The results showed that the frequency of soil drought under meteorological drought conditions showed an increasing trend and that the sensitivity of soil drought occurrence increased. Among them, the effect of precipitation on relative soil moisture (RSM) was immediate, and the effect of prolonged warming on RSM is greater. The area of soil aridification that was caused by climatic aridification in spring accounted for 13.7% of the entire area. The regional aridification research mode proposed in this paper can provide ideas for subsequent studies. [Display omitted] • A comprehensive judgment mode of the regional aridification combining climate elements, soil moisture was established. • Over the last 20 years, the central part of the FPENC showed overall warming and wetting of the climate. • Over the last 20 years, the trend of the soil aridification in the central part of the FPENC remained severe. • The counterbalancing effect of the dry and wet seasons leads to recessive aridification. • The sensitivity of the occurrences of soil drought to the climate drought anomalies continuously became stronger. [ABSTRACT FROM AUTHOR]