It is important to improve the accuracy of models estimating aboveground biomass (AGB) in large areas with complex geography and high forest heterogeneity. In this study, k-nearest neighbors (k-NN), gradient boosting machine (GBM), random forest (RF), quantile random forest (QRF), regularized random forest (RRF), and Bayesian regularization neural network (BRNN) machine learning algorithms were constructed to estimate the AGB of four forest types based on environmental factors and the variables selected by the Boruta algorithm in Yunnan Province and using integrated Landsat 8 OLI and Sentinel 2A images. The results showed that (1) DEM was the most important variable for estimating the AGB of coniferous forests, evergreen broadleaved forests, deciduous broadleaved forests, and mixed forests; while the vegetation index was the most important variable for estimating deciduous broadleaved forests, the climatic factors had a higher variable importance for estimating coniferous and mixed forests, and texture features and vegetation index had a higher variable importance for estimating evergreen broadleaved forests. (2) In terms of specific model performance for the four forest types, RRF was the best model both in estimating the AGB of coniferous forests and mixed forests; the R² and RMSE for coniferous forests were 0.63 and 43.23 Mg ha⁻¹, respectively, and the R² and RMSE for mixed forests were 0.56 and 47.79 Mg ha⁻¹, respectively. BRNN performed the best in estimating the AGB of evergreen broadleaved forests; the R² was 0.53 and the RMSE was 68.16 Mg ha⁻¹. QRF was the best in estimating the AGB of deciduous broadleaved forests, with R² of 0.43 and RMSE of 45.09 Mg ha⁻¹. (3) RRF was the best model for the four forest types according to the mean values, with R² and RMSE of 0.503 and 52.335 Mg ha⁻¹, respectively. In conclusion, different variables and suitable models should be considered when estimating the AGB of different forest types. This study could provide a reference for the estimation of forest AGB based on remote sensing in complex terrain areas with a high degree of forest heterogeneity.

The demand for accurate estimation of aboveground biomass (AGB) at high spatial resolution is increasing in grassland-related research and management, especially for those regions with complex topography and fragmented landscapes, where grass and shrub are interspersed. In this study, based on 519 field AGB observations, integrating Synthetic Aperture Radar (SAR; Sentinel-1) and high-resolution (Sentinel-2) remote sensing images, environmental and topographical data, we estimated the AGB of mountain grassland in Southwest China (Yunnan Province and Guizhou Province) by using remote sensing algorithms ranging from traditional regression to cutting edge machine learning (ML) and deep learning (DL) models. Four models (i.e., multiple stepwise regression (MSR), random forest (RF), support vector machine (SVM) and convolutional neural network (CNN)) were developed and compared for AGB simulation purposes. The results indicated that the RF model performed the best among the four models (testing dataset: decision co-efficient (R²) was 0.80 for shrubland and 0.75 for grassland, respectively). Among all input variables in the RF model, the vegetation indices played the most important role in grassland AGB estimation, with 6 vegetation indices (EVI, EVI2, NDVI, NIRv, MSR and DVI) in the top 10 of input variables. For shrubland, however, topographical factors (elevation, 12.7% IncMSE (increase in mean squared error)) and SAR data (VH band, 11.3% IncMSE) were the variables which contributed the most in the AGB estimation model. By comparing the input variables to the RF model, we found that integrating SAR data has the potential to improve grassland AGB estimation, especially for shrubland (26.7% improvement in the estimation of shrubland AGB). Regional grassland AGB estimation showed a lower mean AGB in Yunnan Province (443.6 g/m²) than that in Guizhou Province (687.6 g/m²) in 2021. Moreover, the correlation between five consecutive years (2018–2022) of AGB data and climatic factors calculated by partial correlation analysis showed that regional AGB was positively related with mean annual precipitation in more than 70% of the grassland and 60% of the shrubland area, respectively. Also, we found a positive relationship with mean annual temperature in 62.8% of the grassland and 55.6% of the shrubland area, respectively. This study demonstrated that integrating SAR into grassland AGB estimation led to a remote sensing estimation model that greatly improved the accuracy of modeled mountain grassland AGB in southwest China, where the grassland consists of a complex mix of grass and shrubs.

Soil environmental quality related to the residents’ life, health, and safety, has been the hotspot issues in science of ecological environment protection. Evaluating the distribution characteristics, ecological risk, and source of heavy metals in farmland is important for protecting soil resources. The agricultural area of Lianhua town, Gongcheng County, Guilin is a typical karst landform. In response to the problem of heavy metal pollution and complex sources in the soil of this area, the characteristics and sources of heavy metal pollution in the soil profiles from farmland, abandoned land, and forest were studied using the single-factor index method, the geoaccumulation index (Igeo) principal component analysis (PCA) and positive matrix factorization (PMF) model. The results showed that: (1) that the contents of cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn) in the soil profile of the study area were higher than that of the soil elements background values in Guangxi. The total and available forms contents of all heavy metal elements exhibited the characteristics of accumulation in the surface profile; (2) among the six heavy elements, the contents of Cd were in a moderately to heavily polluted state. The contents of Cd in some soil profiles exceeded the control standard for agricultural land soil pollution. The contents of Zn and Ni were from slightly to moderately polluted in areas with frequent agricultural activities; (3) according to the PCA and PMF model, there were three main sources of heavy metals in the study area. Among them, Cd, Cu, Ni, and Zn are related to agricultural activities; the elements As, Cd, Cr, and Hg are closely related to geological background; Pb and Zn are mainly affected by atmospheric sedimentation of transportation. Agricultural activities and natural geological background are the main contribution sources of heavy metals in soil. Human activities are the main factors that cause the accumulation of heavy metals in soil. This research has theoretical guidance and practical significance for the prevention and control of soil heavy metal pollution and the protection of farmland environmental quality in the region.

Comparing algorithms are crucial for enhancing the accuracy of remote sensing estimations of forest biomass in regions with high heterogeneity. Herein, Sentinel 2A, Sentinel 1A, Landsat 8 OLI, and Digital Elevation Model (DEM) were selected as data sources. A total of 12 algorithms, including 7 types of learners, were utilized for estimating the aboveground biomass (AGB) of Pinus yunnanensis forest. The results showed that: (1) The optimal algorithm (Extreme Gradient Boosting, XGBoost) was selected as the meta-model (referred to as XGBoost-stacking) of the stacking ensemble algorithm, which integrated 11 other algorithms. The R² value was improved by 0.12 up to 0.61, and RMSE was decreased by 4.53 Mg/ha down to 39.34 Mg/ha compared to the XGBoost. All algorithms consistently showed severe underestimation of AGB in the Pinus yunnanensis forest of Yunnan Province when AGB exceeded 100 Mg/ha. (2) XGBoost-Stacking, XGBoost, BRNN (Bayesian Regularized Neural Network), RF (Random Forest), and QRF (Quantile Random Forest) have good sensitivity to forest AGB. QRNN (Quantile Regression Neural Network), GP (Gaussian Process), and EN (Elastic Network) have more outlier data and their robustness was poor. SVM-RBF (Radial Basis Function Kernel Support Vector Machine), k-NN (K Nearest Neighbors), and SGB (Stochastic Gradient Boosting) algorithms have good robustness, but their sensitivity was poor, and QRF algorithms and BRNN algorithm can estimate low values with higher accuracy. In conclusion, the XGBoost-stacking, XGBoost, and BRNN algorithms have shown promising application prospects in remote sensing estimation of forest biomass. This study could provide a reference for selecting the suitable algorithm for forest AGB estimation.

Manganese (Mn), an essential trace element for plants in which it is involved in redox reactions as a cofactor for many enzymes, represents an important factor in environmental contamination. Excess Mn can lead to toxicity conditions in natural and agricultural sites. Manganese toxicity is one of the most severe growth limiting factors in acid soil, which accounts for 21% of the total arable lands in China. The more significant part of Mn-toxicity is its interactions with other mineral elements, in particular with phosphorus (P), calcium (Ca) and iron (Fe). The application of P or Ca can be beneficial in the detoxification of manganese, whereas Mn seems to interfere with Fe metabolism. Manganese toxicity varies with plant species, nutrients, and the soil environment. Mango is the main economic fruit in the karst area of the subtropical region of China. The karst soil in the mango orchard is characterized by high Fe, Mn and Mg. In order to explore the interaction among Fe, Mg, and Mn in karst soil and mango systems under high Mn conditions, a typical mango orchard in the karst depression landform in Baise in southern China was selected to study the effects of Fe and Mg on the toxic expression of Mn in mango plants and the interaction mechanism of Fe-Mn-Mg in mango plants. The results show that: (1) the mango growth status is closely correlated with Fe²⁺ (active iron) and Mg under the same soil Mn concentration; (2) The black spots on mango leaves were mainly caused by Fe and Mn. There is a lot of Fe³⁺ and Mn³⁺ in the black spots, which accounts for more than 90% of the total; (3) In addition, the studies also showed that the Fe and Mg inhibited the expression of Mn toxicity in mango. Conclusively, the interaction effect of Fe, Mn, and Mg is an important factor that affects mango growth, which can indicate the status of the soil and plants.

The balance of the mineral nutrition in mango leaves during the flowering period affects the flowering of mango trees and fruit production. Because the soil in karst areas has a slow and unbalanced supply rate of nutrients, mango orchards in a karst area generally have a low yield. There are few studies on the fertilization of mango orchards in karst areas, especially on the diagnosis of leaf mineral nutrition. In this study, mango orchards in the typical karst areas of Guangxi province, one of the main mango-producing areas in China, were selected from the low-yielding and medium-yielding mango orchards. Surface soil samples and leaf samples from mango orchards in full bloom were collected to test for macronutrients and micronutrients. The Diagnosis and Recommendation Integrated System (DRIS) graphical method, the DRIS method, the Modified DRIS (M-DRIS), and the Deviation from Optimum Percentage (DOP) index diagnostic methods were applied to the leaves. The results showed that the DRIS graphical analysis yielded appropriate ratios of N, P, K, Mg, S, Fe, Mn, Cu with the corresponding three elements, Ca, Zn, and B, which can be used as reference diagnostic criteria. Based on the values of the DRIS diagnostic criteria for high-yielding orchards, the critical ranges of the suitable values of the mineral nutrients in the Tainong mango leaves during flowering were determined as N (14.87–17.27 g/kg), P (0.69–0.89 g/kg), K (4.45–6.90 g/kg), Ca (9.51–16.55 g/kg), Mg (1.44–2.20 g/kg), S (0.75–1.06 g/kg), Fe (0.10–0.13 g/kg), Mn (0.61–1.02 g/kg), Cu (5.41–8.89 mg/kg), Zn (7.91–18.95 mg/kg), and B (8.38–16.23 mg/kg). The results of the DRIS, M-DRIS, and DOP index methods were analyzed to determine the order of the fertilizer requirements for the low-yielding orchards: Mg > Fe > S > Zn > B > Cu > K > N > P > Mn > Ca, and for the medium-yielding orchards: Mg > Fe > B > Zn > S > Cu > N > Mn > K > P > Ca. The soil and leaf correlation analysis showed that the soil exchangeable Ca and effective Fe were significantly negatively correlated. Leaf Ca and Fe elements had a mutually antagonistic effect, leaf Mn-rich contents inhibited the uptake of the Mg and Fe elements, and low-yielding orchards had an excess of Mn and a deficiency of Mg. We found that there is lack of the Mg and Fe, a low content of the S and B, and an excess of the Ca and Mn in the mango orchards of the Baise karst area. We suggested that the DRIS graphical method is suitable for the diagnosis of three nutrient elements, and either the DRIS or M-DRIS index method can be chosen. The present research can be used for the precise fertilization of mango orchards in karst areas to improve the yield and quality of local mango orchards.