Biomass-derived materials (BDM) have broad applications in water and agricultural systems. As an emerging tool, Machine learning (ML) has been applied to BDM systems to address material, process, and supply chain design challenges. This paper reviewed 53 papers published since 2008 to understand the capabilities, current limitations, and future potentials of ML in supporting sustainable development and applications of BDM. Previous ML applications were classified into three categories based on their objectives – material and process design, end-use performance prediction, and sustainability assessment. These ML applications focus on identifying critical factors for optimizing BDM systems, predicting material features and performances, reverse engineering, and addressing data challenges for sustainability assessments. BDM datasets show large variations, and ∼75% of them possess < 600 data points. Ensemble models and state-of-the-art neural networks (NNs) perform and generalize well on such datasets. Limitations for scaling up ML for BDM systems lie in the low interpretability of the ensemble and NN models and the lack of studies in sustainability assessment that consider geo-temporal dynamics. A workflow is recommended for future ML studies for BDM systems. More research is needed to explore ML applications for sustainable development, assessment, and optimization of BDM systems. 

Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review

Machine learning combined with multisource remote sensing data to assess soil moisture content (SMC) has attracted considerable attention in SMC studies, but the retrieval results still remain uncertain. The purpose of this study is to combine multiple single machine learning models with integrated learning algorithms and propose an SMC retrieval method based on multiple differentiated models under a stacking integrated learning architecture. First, 19 factors, including: radar backscattering coefficient, vegetation index, and drought index, that affect SMC were extracted from SENTINEL‐1, LANDSAT, and terrain factors. Those with the highest importance scores were selected as retrieval factors using the Boruta algorithm combined with four single machine learning methods—classified regression tree, random forest, gradient boosting decision tree (GBDT), and extreme random tree. In addition, the two stacking ensemble models using least absolute shrinkage and selection operator (LASSO) and the generalized boosted regression model (GBM) were tested and applied to build the most reliable and accurate estimation model. The results showed that radar backscattering coefficient, temperature, vegetation drought index, land surface temperature, enhanced vegetation index, and solar local incident angle were the most important environmental variables for soil moisture retrieval. A comparison of the four machine learning methods in April and August showed that the GBDT model revealed the highest SMC retrieval accuracy, with root mean square error values of 1.87% and 1.64%, respectively. The stacking models were more accurate than the optimal single machine learning model, especially when using GBM. The multifactor integrated model constructed using spectral indices, radar backscatter coefficients, and topographic data exhibited high accuracy in soil surface moisture retrieval in an arid zone, providing a reference for land desertification studies and ecological environment management in the study region.

Remote sensing‐based retrieval of soil moisture content using stacking ensemble learning models

Soil organic carbon (SOC) has attracted a lot of attention in the soil science community. Freely available remote sensing data combined with advanced digital soil mapping (DSM) techniques has led to a better understanding and management of SOC. This paper has considered the published literature with a focus on digital mapping of SOC using remote sensing data within 2010 to 2023 intervals. The objective was to consider all the important aspects of SOC prediction and mapping, including different land-use types, DSM algorithms, environmental variables, and remote sensing data sources. According to this review conducted on the 217 papers, cropland was the most popular type of land use. Regarding the DSM algorithms, random forest (RF) appeared in the largest number of studies. The terrain and spectral variables derived from the digital elevation model (DEM) and remote sensing images, were the highest demanding among all those used as input predictors. In addition, satellite platforms provided the largest portion of the remote sensing data used for the calibration of DSM models. This review provides quantitative insight into recent trends of SOC digital mapping using remote sensing technology while suggesting some directions for future development of the topic. 

Digital mapping of soil organic carbon using remote sensing data: A systematic review

Comparison of the uncertainty of soil organic carbon stocks in different land uses

An accurate and fine map of soil organic carbon (SOC) plays a vital role in understanding the global carbon cycle and achieving soil carbon sequestration potential. Although global and national maps of SOC are already available with various spatial resolutions, limited sample size, and relative coarse resolution hinder its accuracy and application in local regions. Here, we collected 13,424 soil samples and information of 45 environmental covariates from cropland in Jiangxi Province, Southern China during 2012 and 2013. Then, the optimal covariates were selected by a recursive feature elimination algorithm for mapping SOC. After that, we deployed the random forest (RF) to produce a fine map (30 m) of SOC in the cropland of Jiangxi Province and 100 times bootstrapping was performed to calculate the prediction uncertainty. Finally, we determined the impacts of various covariates on SOC variability using RF and partial least squares structural equation modeling. Our results showed that compared with the predictive model without soil management information, introducing soil management information improved the predictability of SOC with an increase in R2 by 7.35 % (0.73 vs 0.68) and decrease in RMSE by 7.03 % (2.91 vs 3.13 g kg−1). Our results well estimated the uncertainty of the predicted result with a PICP of 0.91 for a 90 % prediction interval. Soil properties and soil management activities make the largest contribution for modelling SOC. Specifically, the total nitrogen content, straw return amount, total potassium content, and multi-resolution valley bottom flatness were found as the most important factors for mapping SOC in the cropland of our study area. Overall, this study deepened our knowledge of the variation of SOC and also emphasizes that incorporating soil management information could help us to achieve more accurate predictions of SOC. 

A high-resolution map of soil organic carbon in cropland of Southern China

Soil organic C (SOC) plays an important role in soil quality. Thus, it is of great significance to know the spatial distribution characteristics of SOC. Environmental variables, such as natural predictors, remote sensing (RS) variables, and digital soil mapping approaches have been widely used in SOC prediction. However, it is still challenging to determine which methods and variables are effective to map SOC in farmland. In this study, we compare the performance of three machine learning models, including random forest, Cubist, and the support vector machine model for the prediction of SOC contents using RS variables, topographic variables, climate variables, soil property variables, and spatial position variables as environmental covariates. The recursive feature elimination (RFE) technique was used to select optimal variables. Of the 130 sampling sites of the cultivated layer that were collected in Shanggao county, 70% of the data were used for training and 30% for validation. Prediction accuracy and uncertainty was quantified using the bootstrap approach with 100 iterations. The performance of the model was verified by the mean absolute error, RMSE, R2, and Lin's concordance correlation coefficient (CCC); the SD was used as a measure of prediction uncertainty. Results showed that the Cubist model combined with the RFE method (Cubist_RFE) had a mean R2, RMSE, and SD of .84, 2.39 g kg–1, and 0.73 g kg–1, respectively, which showed the highest prediction accuracy and the lowest uncertainty. The soil property variables (soil alkaline hydrolysis N and pH) and RS variables (soil adjusted vegetation index, enhanced vegetation index, and band7 of Sentinel-2) were the controlling factors of SOC prediction. Thus, the results indicate the potential of the Cubist model in SOC prediction, and the combination of the optical and radar RS data is a viable way for predicting SOC. 

Mapping of soil organic carbon using machine learning models: Combination of optical and radar remote sensing data

The disordered mining of ion-type rare soil has caused significant ecological and environmental problems. Arbuscular mycorrhizal fungi (AMF) can promote soil quality, providing new ideas about the ecological restoration of degraded soil. In this study, the degraded ion-type rare earth tailing soils in Xinfeng County, southern Jiangxi Province, was used as a research object. The soil was subjected to different restoration methods (biochar + fly ash + surface vegetation planting) for 2 yr. A total of six treatments were set up in this experiment, and the phospholipid fatty acid method was used to determine the microbial community structure and biomass of the rhizosphere soil of navel oranges [Citrus sinensis (L.) Osbeck] treated with different amounts of biochar (0, 1, and 5%) plus surface mulching (Trifolium repens L.) or soil without plants. Additionally, a pot experiment was conducted to study the effects of single and double inoculation of AMF (Septoglomus viscosum and Claroideoglomus claroideum) on plant growth and soil improvement under various P levels (0 and 50 mg kg–1) in ion-type rare soil. The results showed that the microbial biomass (fungi, Gram-positive bacteria, Gram-negative bacteria, actinomycetes, AMF, and protozoa) when T. repens was present was noticeably higher than that when T. repens was not present. In particular, the biomass of the AMF under the T. repens treatment with 5% biochar (1.28 nmol g–1) was 34.38% higher than that under the 5% biochar treatment without plants (p < .05). Furthermore, the pot experiments showed that AMF inoculation significantly increased the plant biomass, single AMF inoculation significantly increased soil P in the absence of basal P, and double inoculation significantly increased the glomalin (a component of soil organic matter) content. Our findings indicated that the soil microbial biomass was greatly affected by planting T. repens and adding biological modifiers. These composite remediation methods could provide an increasing number of C sources for the soil microbial community and could effectively improve soil quality and plant growth. 

Effects of arbuscular mycorrhizal fungi on improvement of degraded landscape soil in an ionized rare earth mining area, subtropical China

The risks associated with estrogens in detectable concentrations in the aquatic environment underscore the need to best manage the release of estrogenic compounds. Modeling work can effectively help to understand the variation of estrogens in the natural water bodies at low costs. This study developed a modeling framework based on the Hydrological Simulation Program – FORTRAN (HSPF) code to simulate the fate and transport of three dominant estrogens, estrone (E1), 17β-estradiol (E2β), and 17α -estradiol (E2α) in surface water by integrating their excretion, transport, interconversion, and attenuation processes. This work focused on estrogens from sewage systems, failing septic systems, grazing animals, and manure land application. The developed modeling framework was applied to the Redwood River Watershed in Minnesota to model the in-stream estradiol equivalents (EEQs). The modeled EEQs were comparable to the values measured at three sampling sites along the Redwood River in 2007. The modeling results indicate that wastewater treatment plant discharges elevate estrogen levels on dry days, and surface runoff caused by storms or snow melting after the manure land application and/or cattle grazing can drastically spike EEQs levels, suggesting the application of buffer stripes and manure storage/composting for estrogen control. The case study shows that the modeling framework can be used to characterize the temporal and spatial variation of estrogens in streams, evaluate their risk to aquatic animals, and determine the best management practices for estrogen control. 

Developing a quantitative framework to track the fate and transport of estrogens on a watershed scale

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Mapping of soil organic carbon using machine learning models: Combination of optical and radar remote sensing data

Effects of arbuscular mycorrhizal fungi on improvement of degraded landscape soil in an ionized rare earth mining area, subtropical China

Developing a quantitative framework to track the fate and transport of estrogens on a watershed scale