Mercury is one of the five most toxic heavy metals to the human body. In order to select a high-precision method for predicting the mercury content in soil using hyperspectral techniques, 75 soil samples were collected in Guangdong Province to obtain the soil mercury content by chemical analysis and hyperspectral data based on an indoor hyperspectral experiment. A multiple linear regression (MLR), a back-propagation neural network (BPNN), and a genetic algorithm optimization of the BPNN (GA-BPNN) were used to establish a relationship between the hyperspectral data and the soil mercury content and to predict the soil mercury content. In addition, the feasibility and modeling effects of the three modeling methods were compared and discussed. The results show that the GA-BPNN provided the best soil mercury prediction model. The modeling $R^2$ is 0.842, the root mean square error (RMSE) is 0.052, and the mean absolute error (MAE) is 0.037; the testing $R^2$ is 0.923, the RMSE is 0.042, and the MAE is 0.033. Thus, the GA-BPNN method is the optimum method to predict soil mercury content and the results provide a scientific basis and technical support for the hyperspectral inversion of the soil mercury content.

The rapid and accurate acquisition of rice cultivation information is very important for the management and assessment of rice agriculture and for research on food security, the use of agricultural water resources, and greenhouse gas emissions. Rice mapping methods based on phenology have been widely used but further studies are needed to clearly quantify the rice characteristics during the growth cycle. This paper selected the area where rice agriculture has undergone tremendous changes as the observation object. The rice areas were mapped in three time periods during the period from 1993 to 2016 by combining the characteristics of the harvested areas, flooded areas, and the time interval when harvesting and flooding occurred. An error matrix was used to determine the mapping accuracy. After exclusion of clouds and cloud shadows, the overall accuracy of the paddy fields was higher than 90% (90.5% and 93.5% in period 1 and period 3, respectively). Mixed pixels, image quality, and image acquisition time are important factors affecting the accuracy of rice mapping. The rapid economic development led to an adjustment of people’s diets and presumably this is the main reason why rice cultivation is no longer the main agricultural production activity in the study area.

Arable land is a crucial natural resource for human survival and development, which supports food production, ecological services, and material–energy cycling. It is not only an important production resource for agriculture but also a key guarantee for ensuring food security and sustainable agricultural development. Understanding the current utilization of arable land, exploring the spatial–temporal evolution characteristics, and analyzing the driving mechanisms behind its pattern changes are essential for the rational allocation and sustainable utilization of arable land resources. This study focuses on the utilization of arable land in Guangzhou from 2005 to 2018, employing methods such as statistical analysis and spatial econometrics to provide an in-depth analysis of the spatial–temporal distribution characteristics and driving mechanisms of arable land changes. The results show that from 2005 to 2018, the issue of the conversion of arable land to non-agricultural uses was quite severe in Guangzhou, with the primary form being the conversion of arable land into urban residential construction land. Kernel density analysis revealed that non-agriculturization in Guangzhou exhibited spatial clustering, mainly concentrated in areas with lower elevation. Using standard deviation ellipses and centroid migration analysis, it was found that the center of gravity of non-agriculturization in Guangzhou was generally distributed in a southwest–northeast direction, with a more distinct dispersion compared to the northwest–southeast direction. From 2005 to 2010, the rapid increase in the non-agriculturization rate of arable land in Guangzhou was mainly driven by population density and per capita income, both having a positive impact. From 2010 to 2015, the main driving factor shifted to regional GDP. From 2015 to 2018, regional GDP and the value of the tertiary industry became the main driving factors, but unlike the impact of GDP, the tertiary industry exerted a negative influence on non-agriculturization.

To increase the spatial resolution of Soil Moisture Active Passive (SMAP), this study modifies the downscaling factor model based on the Temperature Vegetation Drought Index (TVDI) using data from the Project for On-Board Autonomy (PROBA-V). In the modified model, TVDI parameters were derived from the temperature-vegetation space and the Enhanced Vegetation Index (EVI). This study was conducted in the north China region using SMAP, PROBA-V, and Moderate Resolution Imaging Spectroradiometer satellite images. The 9-km spatial resolution SMAP data was downscaled to 0.3-km spatial resolution soil moisture using a modified downscaling method. Downscaling accuracies from the original and modified downscaling factor models were compared based on field observations. The results show that both methods generated similar spatial distributions in which soil moisture estimates increased as vegetation coverage increased from built-up areas to forest. However, based on the root mean square error between observations and estimations, the modified model demonstrated an increased estimation accuracy of 4.2% for soil moisture compared to the original method. This study also implies that downscaled soil moisture shows promise as a data source for subsequent watershed scale studies.