The use of spectral features to estimate leaf area index (LAI) is generally considered a challenging task for hyperspectral data. In this study, the hyperspectral reflectance of winter wheat was selected to optimize the selection of spectral features and to evaluate their performance in modeling LAI at various growth stages during 2008 and 2009. We extracted hyperspectral features using different techniques, including reflectance spectra and first derivative spectra, absorption and reflectance position and vegetation indices. In order to find the best subset of features with the best predictive accuracy, partial least squares regression (PLSR) and variable importance in projection (VIP) were applied to estimated LAI values. The results indicated that the red edge–NIR spectral region (680 nm–1300 nm) was the most sensitive to LAI. Most features in this region exhibited a high correlation with LAI and had higher VIP values, especially the first derivative waveband at 750 nm (r = 0.900, VIP = 1.144). Adding a large number of features would not significantly improve the accuracy of the PLSR model. The PLSR model based on the fourteen features with the highest VIP values predicted LAI with a mean bootstrapped R² value of 0.880 and a mean RMSE of 0.943 on the validation dataset and produced an estimated LAI result better than that, including the entire 54-feature dataset with a mean R² of 0.875 and a mean RMSE of 0.965. The results of this study thus suggest that the use of only a few of the best features by VIP values is sufficient for LAI estimation.

Spatially continuous soil thickness data at large scales are usually not readily available and are often difficult and expensive to acquire. Various machine learning algorithms have become very popular in digital soil mapping to predict and map the spatial distribution of soil properties. Identifying the controlling environmental variables of soil thickness and selecting suitable machine learning algorithms are vitally important in modeling. In this study, 11 quantitative and four qualitative environmental variables were selected to explore the main variables that affect soil thickness. Four commonly used machine learning algorithms (multiple linear regression (MLR), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGBoost) were evaluated as individual models to separately predict and obtain a soil thickness distribution map in Henan Province, China. In addition, the two stacking ensemble models using least absolute shrinkage and selection operator (LASSO) and generalized boosted regression model (GBM) were tested and applied to build the most reliable and accurate estimation model. The results showed that variable selection was a very important part of soil thickness modeling. Topographic wetness index (TWI), slope, elevation, land use and enhanced vegetation index (EVI) were the most influential environmental variables in soil thickness modeling. Comparative results showed that the XGBoost model outperformed the MLR, RF and SVR models. Importantly, the two stacking models achieved higher performance than the single model, especially when using GBM. In terms of accuracy, the proposed stacking method explained 64.0% of the variation for soil thickness. The results of our study provide useful alternative approaches for mapping soil thickness, with potential for use with other soil properties.

The total nitrogen concentration (NC, g/100 g) of wetland plants is an important parameter to estimate the wetland health status and to calculate the nitrogen storage of wetland plants. Remote sensing has been widely used to estimate biophysical, physiological, and biochemical parameters of plants. However, current studies place little emphasis on NC estimations by only taking nitrogen’s vertical distribution into consideration, resulting in limited accuracy and decreased practical value of the results. The main goal of this study is to develop a model, considering a non-uniform vertical nitrogen distribution to estimate the total NC of the reed canopy, which is one of the wetland’s dominant species, using hyperspectral data. Sixty quadrats were selected and measured based on an experimental design that considered vertical layer divisions within the reed canopy. Using the measured NCs of different leaf layers and corresponding spectra from the quadrats, the results indicated that the vertical distribution law of the NC was distinct, presenting an initial increase and subsequent decrease from the top layer to the bottom layer. The spectral indices MCARI/MTVI2, TCARI/OSAVI, MMTCI, DCNI, and PPR/NDVI had high R² values when related to NC (R² > 0.5) and low R² when related to LAI (R² < 0.2) and could minimize the influence of LAI and increase the sensitivity to changes in NC of the reed canopy. The relative variation rates (R_v, %) of these spectral indices, calculated from each quadrat, also indicated that the top three layers of the reed canopy were an effective depth to estimate NCs using hyperspectral data. A model was developed to estimate the total NC of the whole reed canopy based on PPR/DNVI with R² = 0.88 and RMSE = 0.37%. The model, which considered the vertical distribution patterns of the NC and the effective canopy layers, has demonstrated great potential to estimate the total NC of the whole reed canopy.

Exploring the landscape ecological security pattern and its driving mechanisms in key economic zones is of great significance for preventing and resolving landscape ecological risks and promoting regional sustainable development. This study quantitatively analyzed the land use change characteristics in the Huaihe River Eco-economic Belt from 1980 to 2020 using the land use transfer matrix and land use intensity index. Further, the evolution of ecological risks and their driving mechanisms were investigated using the landscape pattern index and hierarchical partitioning analysis. The results show that (1) in terms of absolute area, dryland, grassland, and paddy land decreased by 7075 km², 2708 km², and 1874 km², respectively, while urban–rural land and water area increased by 10,538 km² and 1336 km², respectively. In terms of change intensity, grassland, water area, urban–rural land, and unused land exhibited the most dramatic change, whereas forest land, paddy land, and dryland exhibited weaker change. (2) The conversions in the study area were primarily between dryland, paddy land, and urban–rural land. Paddy land and dryland tended to convert to urban–rural land, which is further likely to be transformed into dryland and unused land when converted. (3) The study area mainly presented medium to low ecological risk. Overall, the ecological risk remained stable throughout the study period. Nevertheless, Jining, Zaozhuang, and Bengbu show high ecological risks in the construction of the economic zone. (4) Forest land explained 40% of the variance in landscape risk, whereas urban–rural land and dryland each explained 20% of the variance. An increase in the proportion of urban–rural land and dryland will increase landscape ecological risk. However, after urban–rural land exceeds 15%, the ecological security risk does not increase significantly with increasing proportion of urban–rural land.

Vegetation plays an important role in linking water, atmosphere, and soil. The dynamic change in vegetation is an important indicator for the regulation of the terrestrial carbon balance and climate change. This study applied trend analysis, detrended correlation analysis, and the Hierarchical Partitioning Algorithm (HPA) to GIMMS NDVI3g data, meteorological data, and natural vegetation types for the period 1983 to 2015 to analyze the temporal and spatial changes in NDVI during the growing season and its driving factors in the arid region of northwestern China. The results showed that: (1) the growing season length (GSL) was delayed, with a regional trend of 8 d/33 a, due to a significant advancement in the start of the growing season (SOS, −7 d/33 a) and an insignificant delay to the end of growing season (EOS, 2 d/33 a). (2) The regional change in NDVI was mainly driven by temperature and precipitation, contributing to variations in NDVI of forest of 36% and 15%, respectively, and in the NDVI of grassland, of 35% and 21%, respectively. In particular, changes to forested land and medium-coverage grassland (Mgra) were closely related to temperature and precipitation, respectively. (3) The spatial distribution of the mean NDVI of forest was closely related with precipitation, temperature, and solar radiation, with these meteorological variables explaining 20%, 15%, and 10% of the variation in NDVI, respectively. Precipitation and solar radiation explained 29% and 17% of the variation in the NDVI of grassland, respectively. The study reveals the spatial–temporal evolution and driving mechanism of the NDVI of natural vegetation in the arid region of Northwest China, which can provide theoretical and data support for regional vegetation restoration and conservation.

Various climate and watershed characteristics determine the runoff coefficient (RC), and their interactions are complicated. Understanding the driving factors of the RC is important for understanding the long-term water balance and how it might change. Using the upper reaches of the Huaihe River Basin as the study area, remote sensing data were used to produce a RC map. The geographical detector was selected to quantify the individual and interactive influences of 13 driving factors on the RC. The results revealed that moderate resolution imaging spectroradiometer evapotranspiration (ET) data can be used to produce a mean average RC map based on the water balance equation. The dominant factors influencing the RC were found to vary at different scales. Precipitation had the largest correlation coefficient with the RC at the watershed scale. For the pixel scale, results from the geographical detector indicated that actual evapotranspiration (AET) and precipitation had the highest explanatory rate for the RC in the small watershed region and the whole study area (0.785 and 0.248, respectively). Climate factors, elevation, and normalized difference vegetation index had a substantial influence on the RC. Any two factors exhibited bilinear or nonlinear enhanced relationships in their interactions. The largest interactions between the factors were AET and precipitation, which exceeded 0.900. This study serves to better understand and explain runoff’s complex interrelationships.