Against the background of coordinated development of the Beijing–Tianjin–Hebei region, it is of great significance to quantitatively reveal the contribution rate of the influencing factors of urban land for optimizing the layout of urban land across regions and innovating the inter-regional urban land supply linkage. However, the interaction effects and spatial effects decomposition have not been well investigated in the existing research studies on this topic. In this study, based on the cross-sectional data in 2015 and using the spatial lag model, spatial error model and spatial Durbin model, we analyzed the relationship between urban land and regional economic development at the county level in the Beijing–Tianjin–Hebei region. The results show that: (1) there are endogenous interaction effects of urban land, and the growth of urban land in a county will drive the corresponding growth of urban land in neighboring counties; (2) the local population, average wages, highway mileage density, and actual utilization of foreign capital have positive effects on the scale of urban land in local and neighboring counties; local GDP in the secondary/tertiary sector and the urbanization rate have positive effects on local urban land scale, but negative effects on the urban land scale of neighboring counties; (3) the contribution degree of the direct effect is ranked as follows: GDP in the secondary/tertiary sector > total population > urbanization rate. The order of factors with a significant spatial spillover effect on the scale of urban land in neighboring counties is as follows: average wages > total population > highway mileage density. The GDP in secondary/tertiary sector, population, and urbanization rate are the main influencing factors for the scale of urban land at the county level in the Beijing–Tianjin–Hebei region. It is an important finding that average wages are the most prominent among the spatial spillovers. We should attach importance to the spillover effect of geographic space and construct an urban spatial pattern coordinated with economic development.

Against the background of coordinated development of the Beijing–Tianjin–Hebei region (BTH), it is of great significance to quantitatively reveal spatiotemporal dynamics of urban expansion for optimizing the layout of urban land across regions. However, the urban expansion characteristics, types and trends, and spatial coevolution (including urban land, GDP, and population) have not been well investigated in the existing research studies. This study presents a new spatial measure that describes the difference of the main trend direction. In addition, we also introduce a new method to classify an urban expansion type based on other scholars. The results show the following: (1) The annual urban expansion area (UEA) in Beijing and Tianjin has been ahead of that in Hebei; the annual urban expansion rate (UER) gradually shifted from the highest in megacities to the highest in counties; the high–high clusters of the UEA presented an evolution from a “seesaw” pattern to a “dumbbell” pattern, while that of the UER moved first from Beijing to Tianjin and eventually to Hebei. (2) Double high speed for both UEA and UER was the main extension type; most cities presented a U-shaped trend. (3) Qinhuangdao has the largest difference between the main trend direction of spatial distribution of urban land, GDP and population; the spatial distribution of GDP is closer to that of urban land than population. (4) The area and proportion of land occupied by urban expansion varied greatly across districts/counties. BTH experienced dramatic urban expansion and has a profound impact on land use. These research results can provide a data basis and empirical reference for territorial spatial planning.

The use of remote sensing technology to monitor farmland is currently the mainstream method for crop research. However, in cloudy and misty regions, the use of optical remote sensing image is limited. Synthetic aperture radar (SAR) technology has many advantages, including high resolution, multi-mode, and multi-polarization. Moreover, it can penetrate clouds and mists, can be used for all-weather and all-time Earth observation, and is sensitive to the shape of ground objects. Therefore, it is widely used in agricultural monitoring. In this study, the polarization backscattering coefficient on time-series SAR images during the rice-growing period was analyzed. The rice identification results and accuracy of InSAR technology were compared with those of three schemes (single-time-phase SAR, multi-time-phase SAR, and combination of multi-time-phase SAR and InSAR). Results show that VV and VH polarization coherence coefficients can well distinguish artificial buildings. In particular, VV polarization coherence coefficients can well distinguish rice from water and vegetation in August and September, whereas VH polarization coherence coefficients can well distinguish rice from water and vegetation in August and October. The rice identification accuracy of single-time series Sentinel-1 SAR image (78%) is lower than that of multi-time series SAR image combined with InSAR technology (81%). In this study, Guanghan City, a cloudy region, was used as the study site, and a good verification result was obtained.

Quantitative evaluation of the coupling coordination degree (CCD) between regional haze the disaster risk index (HRI) and urbanization development index level (UDI) is of great significance for the realization of regional sustainable development goals. Given the lack of the combination of remote sensing and statistical data to evaluate the CCD between two systems, the Chinese mainland’s 31 provinces and autonomous regions were taken to evaluate their HRI and UDI by building index systems. Then, an entropy method and one improved coupling coordination model were used to calculate and analyze the spatiotemporal characteristics of CCD between HRI and UDI during 2000–2020. The results showed that: (1) From 2000 to 2020, the value of HRI in China showed a “W” type change trend with its value increased from 0.7041 in 2000 to 0.8859 in 2020, indicating that haze pollution level showed a fluctuating downward trend; (2) From 2000 to 2020, China’s UDI values showed a gradual upward trend with its value increased from 0.1647 in 2000 to 0.4640 in 2020, with an average annual growth rate of 8.63%; (3) From 2000 to 2020, CCD values between HRI and UDI showed a fluctuating upward trend with its value increased from 0.5374 in 2000 to 0.7781 in 2020, with an average annual growth rate of 2.13%; the overall level of China’s CCD had raised from low coordination to moderate coordination, and eastern coastal provinces had higher CCD values, while those of central and western provinces had lower CCD values; (4) HRI, UDI and CCD could be well fitted with the R² of 0.9869. Specifically, UDI had a higher contribution to improving the CCD than the HRI.

Timely and quantitatively evaluating regional eco-environmental quality (EEQ) is of great significance for realizing regional sustainable development goals. Especially for cloudy areas, it was a great challenge to construct a regional EEQ dataset with high quality and high resolution. However, existing studies failed to consider the influence of land surface and season elements in evaluating regional EEQ. Therefore, this study aimed to promote an accurate EEQ-evaluating framework for cloudy areas. Zhaotong city, a typical karst and cloudy region, was chosen as the study area. First, we integrated multi-source spatiotemporal datasets and constructed a novel eco-environmental comprehensive evaluation index (ECEI) to assess its EEQ from 2000 to 2020. Next, standard deviation ellipse (SDE) and trend analysis methods were applied to investigate regional EEQ’s change trends. Finally, ecological index (EI) values for different years were calculated to validate the effectivity of the ECEI. The main findings were as follows: (1) The EEQ of Zhaotong showed an upward-fluctuating trend (0.0058 a⁻¹), with average ECEI values of 0.729, 0.693, 0.722, 0.749, and 0.730. (2) The spatial distribution pattern of the EEQ showed high values in the north and low values in the south, with Zhaoyang district having the lowest ECEI value. (3) From 2000 to 2020, the standard deviation of the major axis of the ellipse moved northeast of Zhaotong city with θ of SDE changing from 57.06° to 62.90°, thus, indicating the improvement of northeastern regions’ EEQ. (4) The coefficients of the determinant (R²) between the EI and ECEI were 0.84, which was higher than that of EI–RSEI (R² = 0.56). This indicated that our promoted framework and the ECEI could acquire more accurate EEQ results and provide suggestions for relevant policymakers.