As a basic agricultural parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on the vegetation growth, agricultural production, and healthy operation of regional ecosystems. The Aksu river basin is a typical semi-arid agricultural area which seasonally suffers from water shortage. Due to the lack of knowledge on soil moisture change, the water management and decision-making processes have been a difficult issue for local government. Therefore, soil moisture monitoring by remote sensing became a reasonable way to schedule crop irrigation and evaluate the irrigation efficiency. Compared to in situ measurements, the use of remote sensing for the monitoring of soil water content is convenient and can be repetitively applied over a large area. To verify the applicability of the typical drought index to the rapid acquisition of soil moisture in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. GF-1 WFV images, Landsat 8 OLI images, and the measured soil moisture data were used to determine the Perpendicular Drought Index (PDI), the Modified Perpendicular Drought Index (MPDI), and the Vegetation Adjusted Perpendicular Drought Index (VAPDI). First, the determination coefficients of the correlation analyses on the PDI, MPDI, VAPDI, and measured soil moisture in the 0–10, 10–20, and 20–30 cm depth layers based on the GF-1 WFV and Landsat 8 OLI images were good. Notably, in the 0–10 cm depth layers, the average determination coefficient was 0.68; all models met the accuracy requirements of soil moisture inversion. Both indicated that the drought indices based on the Near Infrared (NIR)-Red spectral space derived from the optical remote sensing images are more sensitive to soil moisture near the surface layer; however, the accuracy of retrieving the soil moisture in deep layers was slightly lower in the study area. Second, in areas of vegetation coverage, MPDI and VAPDI had a higher inversion accuracy than PDI. To a certain extent, they overcame the influence of mixed pixels on the soil moisture spectral information. VAPDI modified by Perpendicular Vegetation Index (PVI) was not susceptible to vegetation saturation and, thus, had a higher inversion accuracy, which makes it performs better than MPDI’s in vegetated areas. Third, the spatial heterogeneity of the soil moisture retrieved by the GF-1 WFV and Landsat 8 OLI image were similar. However, the GF-1 WFV images were more sensitive to changes in the soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for the dynamic monitoring of surface soil moisture, obtaining agricultural information and agricultural condition parameters in arid and semi-arid regions.

Water yield is of great importance to the balance between supply and demand of water resources. The provision of freshwater for Dali is estimated and mapped in 1988, 1995, 2000, 2005 and 2008, using the Integrated Valuation of Environmental Services and Tradeoffs (InVEST) modeling toolset. The stability of water yield’s spatial variation is analyzed by a sorting method. The factors are explored which lead to the change in the relative water yield capacity. The yields at five points in time are compared, and the result of which shows a sharp fluctuation. The water yield curve is of a similar waveform as precipitation. An obvious and relatively stable spatial variation appears for water yield. The highest water yield areas are mainly located in the area where the elevation is high and both the elevation and the slope changes are large, and the main land uses are Shrub Land and High Coverage Grassland. The lowest areas are mainly in the eastern part of Erhai or the surrounding area. Precipitation, construction land expansion and the implementation of policy on land use are the three main factors which contribute to the change of the relative water yield capacity during 1988–2008 in Dali. In the study area, the water yield appears highly sensitive to the change in precipitation. The elasticity coefficient is calculated to illustrate the sensitivity of the water yield to the precipitation. When the elasticity index is larger, the risk of natural disaster will be higher.

The boundary delineation of the urban-rural fringe (URF) is the basic work of fine planning and governance of cities, which plays a positive role in promoting the process of global sustainable development and urban and rural integration. In the past, the delineation of URF had shortcomings such as a single selected data source, difficulty in obtaining data, and low spatial and temporal resolution. This study combines Point of Interest (POI) and Nighttime Light (NTL) data, proposes a new spatial recognition method of URF according to the characteristics of urban and rural spatial structure, and conducts empirical analysis with Wuhan as the research object, combining the information entropy of land use structure, NDVI, and population density data to verify and compare the delineation results and field verification was conducted for typical areas. The results show that (1) the fusion of POI and NTL can maximize the use of the characteristics of the differences in facility types, light intensity, and resolution between POI and NTL, compared with the urban-rural fringe boundary identified by POI, NTL or population density data alone, and it is more accurate and time-sensitive; (2) NPP and POI (fusion data of Suomi NPP-VIIRS and POI) can quantitatively identify potential central area and multi-layer structure of the city. It fluctuates between 0.2 and 0.6 in the urban core area of Wuhan and between 0.1 and 0.3 in the new town clusters, while in the URF and rural areas drops sharply to below 0.1; (3) the urban-rural fringe area of Wuhan covers a total area of 1482.35 km², accounting for 17.30% of the total area of the city. Its land use types are mainly construction land, water area, and cultivated land, accounting for 40.75%, 30.03%, and 14.60% of the URF, respectively. Its NDVI and population density are at a medium level, with values of 1.630 and 2556.28 persons/km^2, respectively; (4) the double mutation law of NPP and POI in urban and rural space confirms that the URF exists objectively as a regional entity generated in the process of urban expansion, provides empirical support for the theory of urban and rural ternary structure, and has a positive reference value for the allocation of global infrastructure, industrial division, ecological function division, and other researches.

This paper aimed at using the niche-fitness analysis method to assess soil fertility of cultivated land in Jianghan Plain in China. By introducing the niche-fitness theory into comprehensive analysis of soil fertility, an evaluation method was developed using geographic information systems (GIS) and the soil basic niche-fitness model (SBNFM). To begin with, 10 indicators were selected and used to assess the spatial differences of soil fertility for cultivated land, and its weight was decided using analytic hierarchy process (AHP). Soil samples were collected from topsoil (0–25 cm) in 58 units and examined for pH, organic matter (OM), total nitrogen (TN), etc. After standardizing soil attribute data of each unit, the optimum niche of every indicator was obtained and the soil basic niche-fitness value of each plot was calculated by using SBNFM. Finally, the soil fertility of the cultivated land was divided into four grades based on the shortest distance cluster analysis method in this area. Results indicate that the soil fertility of the cultivated land in this area was on the medium and high side, the percentages of area in grades 1, 2, 3 and 4 accounted for 33.43%, 35.99%, 22.70% and 7.88%, respectively; thus, approximately 70% of the cultivated land was classified in the top two grades. The linear-correlated relationship between niche-fitness value and rice yield has shown that SBNFM is an important and effective tool to analyze soil fertility and evaluate the quality of cultivated land. The above results might be helpful and support fertilizer management or agricultural activity in the study area.

Green technological innovation is pivotal in advancing the ‘dual carbon’ target and promoting sustainable and low-carbon development. This research examines 17 prefecture-level cities in Hubei Province, employing the Super-SBM model for assessing emissions of carbon efficiency from 2010 to 2020. The kernel density estimation and the Dagum coefficient of Gini are also used to examine the spatio-temporal differentiation and the evolution of these efficiencies. A data panel regression model is utilized to evaluate how green technological innovation impacts carbon emission efficiency in Hubei Province. The research revealed that (1) Hubei Province’s carbon emission efficiency first fluctuated and then increased rapidly, and (2) the overall regional difference in carbon emission efficiency in Hubei Province shows a trend of first decreasing and then gradually increasing. The Wuhan metropolitan area and the Xiang-yang-Shiyan-Suizhou-Shennongjia urban area are quite different; the differentiation within the Yichang-Jingzhou-Jing-Enshi urban agglomeration shows a narrowing trend. (3) The innovation elements of green technology are positively correlated with the effectiveness of carbon emissions; the relationship between economic expansion and population density among the control variables also shows a positive correlation, while the industrial structure and government environmental regulations are negatively correlated. (4) In Hubei Province, there is a temporal lag between green technological innovation and its impact on carbon emission efficiency. Capital investment and technical achievement currently enhance carbon emission efficiency, while human capital positively affects carbon emission efficiency during a second lag period. This article proposes countermeasures and recommendations for R&D capital spending, innovative talent cultivation, and regional differentiation, providing specific references to advance the coordinated growth of the whole Hubei Province and green sustainable development.