The spatial distribution of soil organic carbon (SOC) serves as critical geographic information for assessing ecosystem services, climate change mitigation, and optimal agriculture management. Digital mapping of SOC is challenging due to the complex relationships between the soil and its environment. Except for the well-known terrain and climate environmental covariates, vegetation that interacts with soils influences SOC significantly over long periods. Although several remote-sensing-based vegetation indices have been widely adopted in digital soil mapping, variables indicating long term vegetation growth have been less used. Vegetation phenology, an indicator of vegetation growth characteristics, can be used as a potential time series environmental covariate for SOC prediction. A CNN-LSTM model was developed for SOC prediction with inputs of static and dynamic environmental variables in Xuancheng City, China. The spatially contextual features in static variables (e.g., topographic variables) were extracted by the convolutional neural network (CNN), while the temporal features in dynamic variables (e.g., vegetation phenology over a long period of time) were extracted by a long short-term memory (LSTM) network. The ten-year phenological variables derived from moderate-resolution imaging spectroradiometer (MODIS) observations were adopted as predictors with historical temporal changes in vegetation in addition to the commonly used static variables. The random forest (RF) model was used as a reference model for comparison. Our results indicate that adding phenological variables can produce a more accurate map, as tested by the five-fold cross-validation, and demonstrate that CNN-LSTM is a potentially effective model for predicting SOC at a regional spatial scale with long-term historical vegetation phenology information as an extra input. We highlight the great potential of hybrid deep learning models, which can simultaneously extract spatial and temporal features from different types of environmental variables, for future applications in digital soil mapping.

The coastline situation reflects socioeconomic development and ecological environment in coastal zones. Analyzing coastline changes clarifies the current coastline situation and provides a scientific basis for making environmental protection policies, especially for coastlines with significant human interference. As human activities become more intense, coastline types and their dynamic changes become more complicated, which needs more detailed identification of coastlines. High spatial resolution images can help provide detailed large spatial coverage at high resolution information on coastal zones. This study aims to map the position and status of the Yangtze River Delta (YRD) coastline using an NDWI threshold method based on 2 m Gaofen-1/Ziyuan-3 imagery and analyze coastline change and coastline type distribution characteristics. The results showed that natural and artificial coastlines in the YRD region accounted for 42.73% and 57.27% in 2013 and 41.56% and 58.44% in 2018, respectively. The coastline generally advanced towards the sea, causing a land area increase of 475.62 km2. The changes in the YRD coastline mainly resulted from a combination of large-scale artificial construction and natural factors such as silt deposition. This study provides a reference source for large spatial coverage at high resolution remote sensing coastline monitoring and a better understanding of land use in coastal zone.

The rice rhizosphere microbiota is crucial for crop yields and nutrient use efficiency. However, little is known about how co‐occurrence patterns, keystone taxa and functional gene assemblages relate to soil pH in the rice rhizosphere soils. Using shotgun metagenome analysis, the rice rhizosphere microbiome was investigated across 28 rice fields in east‐central China. At higher pH sites, the taxonomic co‐occurrence network of rhizosphere soils was more complex and compact, as defined by higher average degree, graph density and complexity. Network stability was greatest at medium pH (6.5 < pH < 7.5), followed by high pH (7.5 < pH). Keystone taxa were more abundant at higher pH and correlated significantly with key ecosystem functions. Overall functional genes involved in C, N, P and S cycling were at a higher relative abundance in higher pH rhizosphere soils, excepting C degradation genes (e.g. key genes involved in starch, cellulose, chitin and lignin degradation). Our results suggest that the rice rhizosphere soil microbial network is more complex and stable at higher pH, possibly indicating increased efficiency of nutrient cycling. These observations may indicate routes towards more efficient soil management and understanding of the potential effects of soil acidification on the rice rhizosphere system.

Cropland soil carbon not only serves food security but also contributes to the stability of the terrestrial ecosystem carbon pool due to the strong interconnection with atmospheric carbon dioxide. Therefore, the better monitoring of soil carbon in cropland is helpful for carbon sequestration and sustainable soil management. However, severe anthropogenic disturbance in cropland mainly in gentle terrain creates uncertainty in obtaining accurate soil information with limited sample data. Within the past twenty years, digital soil mapping has been recognized as a promising technology in mapping soil carbon. Herein, to advance existing knowledge and highlight new directions, the article reviews the research on mapping soil carbon in cropland from 2005 to 2021. There is a significant shift from linear statistical models to machine learning models because nonlinear models may be more efficient in explaining the complex soil-environment relationship. Climate covariates and parent material play an important role in soil carbon on the regional scale, while on a local scale, the variability of soil carbon often depends on topography, agricultural management, and soil properties. Recently, several kinds of agricultural covariates have been explored in mapping soil carbon based on survey or remote sensing technique, while, obtaining agricultural covariates with high resolution remains a challenge. Based on the review, we concluded several challenges in three categories: sampling, agricultural covariates, and representation of soil processes in models. We thus propose a conceptual framework with four future strategies: representative sampling strategies, establishing standardized monitoring and sharing system to acquire more efficient crop management information, exploring time-series sensing data, as well as integrating pedological knowledge into predictive models. It is intended that this review will support prospective researchers by providing knowledge clusters and gaps concerning the digital mapping of soil carbon in cropland.

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