To improve land use efficiency, urban renewal must also consider urban microclimates and heat islands. Existing research has depended on manual interpretation of high-resolution optical satellite imagery to resolve land surface temperature (LST) changes caused by urban renewal; however, the acquired ground time series data tend to be uneven and unique to specific frameworks. The objective of this study was to establish a more general framework to study LST changes caused by urban renewal using multi-source remote sensing data. Specifically, urban renewal areas during 2007–2017 were obtained by integrating Landsat and yearly Phased Array type L-band Synthetic Aperture Radar (PALSAR) images, and LST was retrieved from Landsat thermal infrared data using the generalized single-channel algorithm. Our results showed that urban renewal land (URL) area accounted for 1.88% of urban land area. Relative LST between URL and general urban land (GUL) of Liwan, Yuexiu, Haizhu, and Tianhe districts dropped by 0.88, 0.42, 0.43, and 0.10 K, respectively, whereas those of Baiyun, Huangpu, Panyu, and Luogang districts presented opposite characteristics, with a rise in the LST of 0.98, 1.03, 1.63, and 2.11 K, respectively. These results are attributable to population density, building density, and landscape pattern changes during the urban renewal process.

The quantity and spatial pattern of farmland has changed in China, which has led to a major change in the production potential under the influence of the national project of ecological environmental protection and rapid economic growth during 1990–2010. In this study, the production potential in China was calculated based on meteorological, terrain elevation, soil and land-use data from 1990, 2000 and 2010 using the Global Agro-ecological Zones model. Then, changes in the production potential in response to farmland changes from 1990 to 2010 were subsequently analyzed. The main conclusions were the following. First, the total production potential was 1.055 billion tons in China in 2010. Moreover, the average production potential was 7614 kg/ha and showed tremendous heterogeneity in spatial pattern. Total production in eastern China was high, whereas that in northwestern China was low. The regions with high per unit production potential were mainly distributed over southern China and the middle and lower reaches of the Yangtze River. Second, the obvious spatiotemporal heterogeneity in farmland changes from 1990 to 2010 had a significant influence on the production potential in China. The total production potential decreased in southern China and increased in northern China. Furthermore, the center of growth of the production potential moved gradually from northeastern China to northwestern China. The net decrease in the production potential was 2.97 million tons, which occupied 0.29% of the national total actual production in 2010. Third, obvious differences in the production potential in response to farmland changes from 1990 to 2000 and from 2000 to 2010 were detected. The net increase in the production potential during the first decade was 10.11 million tons and mainly distributed in the Northeast China Plain and the arid and semi-arid regions of northern China. The net decrease in the production potential during the next decade was 13.08 million tons and primarily distributed in the middle and lower reaches of the Yangtze River region and the Huang-Huai-Hai Plain. In general, the reason for the increase in the production potential during the past two decades might be due to the reclamation of grasslands, woodlands and unused land, and the reason for the decrease in the production potential might be urbanization that occupied the farmland and Green for Grain Project, which returned farmland to forests and grasslands.

Soil nutrients play a vital role in plant growth and thus the rapid acquisition of soil nutrient content is of great significance for agricultural sustainable development. Hyperspectral remote-sensing techniques allow for the quick monitoring of soil nutrients. However, at present, obtaining accurate estimates proves to be difficult due to the weak spectral features of soil nutrients and the low accuracy of soil nutrient estimation models. This study proposed a new method to improve soil nutrient estimation. Firstly, for obtaining characteristic variables, we employed partial least squares regression (PLSR) fit degree to select an optimal screening algorithm from three algorithms (Pearson correlation coefficient, PCC; least absolute shrinkage and selection operator, LASSO; and gradient boosting decision tree, GBDT). Secondly, linear (multi-linear regression, MLR; ridge regression, RR) and nonlinear (support vector machine, SVM; and back propagation neural network with genetic algorithm optimization, GABP) algorithms with 10-fold cross-validation were implemented to determine the most accurate model for estimating soil total nitrogen (TN), total phosphorus (TP), and total potassium (TK) contents. Finally, the new method was used to map the soil TK content at a regional scale using the soil component spectral variables retrieved by the fully constrained least squares (FCLS) method based on an image from the HuanJing-1A Hyperspectral Imager (HJ-1A HSI) of the Conghua District of Guangzhou, China. The results identified the GBDT-GABP was observed as the most accurate estimation method of soil TN ( of 0.69, the root mean square error of cross-validation (RMSECV) of 0.35 g kg⁻¹ and ratio of performance to interquartile range (RPIQ) of 2.03) and TP ( of 0.73, RMSECV of 0.30 g kg⁻¹ and RPIQ = 2.10), and the LASSO-GABP proved to be optimal for soil TK estimations ( of 0.82, RMSECV of 3.39 g kg⁻¹ and RPIQ = 3.57). Additionally, the highly accurate LASSO-GABP-estimated soil TK (R² = 0.79) reveals the feasibility of the LASSO-GABP method to retrieve soil TK content at the regional scale.

Soil nutrients are essential factors that reflect farmland quality. Nitrogen, phosphorus, and potassium are essential elements for plants, while silicon is considered a “quasi-essential” element. This study investigated the spatial distribution of plant nutrients in soil in a hilly region of the Pearl River Delta in China. A total of 201 soil samples were collected from farmland topsoil (0–20 cm) for the analysis of total nitrogen (TN), available phosphorus (AP), available potassium (AK), and available silicon (ASi). The coefficients of variation ranged from 47.88% to 76.91%. The NSRs of TN, AP, AK, and ASi were 0.15, 0. 07, 0.12, and 0.13, respectively. The NSRs varied from 0.02 to 0.20. All variables exhibited weak spatial dependence (R² < 0.5), except for TN (R² = 0.701). After comparing the prediction accuracy of the different methods, we used the inverse distance weighting method to analyze the spatial distribution of plant nutrients in soil. The uniform spatial distribution of AK, TN overall showed a trend of increasing from northeast to southwest, and the overall spatial distribution of AP and ASi showed that the northeast was higher than the southwest. This study provides support for the delimitation of basic farmland protection areas, the formulation of land use spatial planning, and the formulation of accurate farmland protection policies.