Drought, as an extreme climate event, affects the ecological environment for vegetation and agricultural production. Studies of the vegetative response to drought are paramount to providing scientific information for drought risk mitigation. In this paper, the spatial-temporal pattern of drought and the response lag of vegetation in Nebraska were analyzed from 2000 to 2015. Based on the long-term Daymet data set, the standard precipitation index (SPI) was computed to identify precipitation anomalies, and the Gaussian function was applied to obtain temperature anomalies. Vegetation anomaly was identified by dynamic time warping technique using a remote sensing Normalized Difference Vegetation Index (NDVI) time series. Finally, multilayer correlation analysis was applied to obtain the response lag of different vegetation types. The results show that Nebraska suffered severe drought events in 2002 and 2012. The response lag of vegetation to drought typically ranged from 30 to 45 days varying for different vegetation types and human activities (water use and management). Grasslands had the shortest response lag (~35 days), while forests had the longest lag period (~48 days). For specific crop types, the response lag of winter wheat varied among different regions of Nebraska (35–45 days), while soybeans, corn and alfalfa had similar response lag times of approximately 40 days.

Soil aggregate stability is an important aspect of soil function and health. Fertilization could potentially alter soil properties and thereby affect aggregate stability. To determine which fertilizer is useful for improving soil fertility and stabilizing soil aggregates and thereby reducing soil erodibility, we examined three types of fertilizer, and measured how soil organic carbon, carbohydrates, and related soil properties influenced aggregate stability in eroded Ultisols. Treatments included control (CK), mineral fertilizer nitrogen (N), phosphorus (P), potassium (K) (NPK), fertilizer NPK plus straw (NPKS), and farmyard manure (FYM). Aggregate stability was tested according to Le Bissonnais method, involving three disruptive tests: fast wetting (FW), slow wetting (SW), and mechanical breakdown (WS). Total organic carbon, particulate organic carbon, mineral-associated carbon, and cold-water-soluble carbohydrate, hot-water-soluble carbohydrate, and dilute acid hydrolysable carbohydrate were measured, as well as soil intrinsic properties (including pH, bulk density, iron and aluminum oxides). The 12-year fertilization had a larger effect on aggregate stability and related soil properties in a 0–15 cm soil layer, whereas no effect was evident at a soil depth of 15–40 cm. MWD (mean weight diameter) under the three tests decreased with increasing soil depth. Fertilization, especially farmyard manure evidently improved MWD_FW and MWD_WS at a depth of 0–15 cm. Slaking was the main mechanism of aggregate breakdown in Ultisols studied, followed by mechanical breakdown. Correlation analysis showed that MWD_FW and MWD_WS at a depth of 0–15 cm increased with the increase of particulate organic carbon, total organic carbon, hot-water-soluble carbohydrate and pH. Furthermore, their interaction with amorphous iron oxides enhanced aggregate stability against slaking or, with amorphous aluminum oxides, modified aggregate stability against mechanical breakdown. Consequently, particulate organic carbon was the dominant cementing agent for aggregation in Ultisols studied, and its combination with pH, amorphous aluminum oxides, amorphous iron oxides, and free aluminum oxides play a synergetic role in stabilizing soil aggregate. Accordingly, farmyard manure or fertilizer NPK plus straw improved soil fertility and the ability to resist slaking.

Daye is a city in China known for its rich mineral resources, with a history of metal mining and smelting that dates back more than 3000 years. To analyze the spatial distribution patterns, ecological risk, and sources of heavy metals (Cd, Co, Cr, Cu, Mn, Ni, Pb, and Zn) in soils, 213 topsoil samples were collected in the main urban area of Daye in September 2016. The mean concentrations of Cd, Cu, Pb, and Zn were higher than the corresponding background values, with the mean concentration of Cd being almost seven times its background value. Spatially, the high concentrations of Cd, Mn, Pb, and Zn were mainly concentrated in the southeastern part of the region due to nonferrous metal mining and smelting. However, the high concentrations of Co and Cu were concentrated in the central part of the study area, resulted from copper mining and smelting. The data of the geoaccumulation index showed that the contamination levels ranged from no pollution (Co, Cr, Mn, and Ni) to heavy contamination (Cd, Cu, and Pb). Ecological risk assessment showed that Cd posed a high, serious, and even severe ecological risk in 53.78% of the area of Daye. According to the results of the principal component analysis, mineral exploitation and smelting involving a variety of minerals (ES_M), mining exploitation, and smelting of copper ore (ES_C), and natural sources are the three main sources of heavy metals in these soils. Furthermore, the absolute principal component scores showed that 69.21% and 23.17% of the heavy metal concentrations were ascribed to ES_M and ES_C, respectively.

The photovoltaic (PV) industry boom has accelerated the need for accurately understanding the spatial distribution of PV energy systems. The synergy of remote sensing and artificial intelligence presents significant prospects for PV energy monitoring. Currently, numerous studies have focused on extracting rooftop PV systems from airborne or satellite imagery, but their small-scale and size-varying characteristics make the segmentation results suffer from PV internal incompleteness and small PV omission. To address these issues, this study proposed a size-aware deep learning network called Rooftop PV Segmenter (RPS) for segmenting small-scale rooftop PV systems from high-resolution imagery. In detail, the RPS network introduced a Semantic Refinement Module (SRM) to sense size variations of PV panels and reconstruct high-resolution deep semantic features. Moreover, a Feature Aggregation Module (FAM) enhanced the representation of robust features by continuously aggregating deeper features into shallower ones. In the output stage, a Deep Supervised Fusion Module (DSFM) was employed to constrain and fuse the outputs at different scales to achieve more refined segmentation. The proposed RPS network was tested and shown to outperform other models in producing segmentation results closer to the ground truth, with the F1 score and IoU reaching 0.9186 and 0.8495 on the publicly available California Distributed Solar PV Array Dataset (C-DSPV Dataset), and 0.9608 and 0.9246 on the self-annotated Heilbronn Rooftop PV System Dataset (H-RPVS Dataset). This study has provided an effective solution for obtaining a refined small-scale energy distribution database.