Search

Soil salinization is prominent environmental issue in arid and semi-arid regions, such as Xinjiang in Northwest China. Salinization severely restricts economic and agricultural development and would lead to ecosystem degradation. Finding a method of rapidly and accurately determining soil salinity (SS) is one of the main challenges in salinity evaluation, saline soil development, and utilization. In situ visible and near infrared (Vis-NIR) spectroscopy has proven to be a promising technique for detecting soil properties since it can realize real-time, rapid detection of SS. However, it still remains challenging whether Vis-NIR in situ spectroscopy can invert SS with high accuracy due to the interference of environmental factors (e.g., light, water vapor, solar altitude angle, etc.) on the spectral in the field. To fill this knowledge gap, we collected Vis-NIR in situ spectral and lab-measured SS data from 135 surface soil samples in the Kongterik Pasture Nature Reserve (KPNR) in the desert oasis ecotone of southern Xinjiang, China. We used genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) algorithms to select the feature bands of SS. Subsequently, we combined extreme learning machines (ELM), back-propagation neural networks (BPNN), and convolutional neural networks (CNN) to build inversion models of SS. The results showed that different feature bands selection methods could improve the Vis-NIR in situ spectral prediction model accuracy. Either SS inversion models were built using full-band spectral data or feature-band spectral data. Compared with the full-band (401–2400 nm) spectral modeling, the validation set R2 of ELM, BPNN, and CNN models built selected feature bands selected by PSO, GA, and SA, respectively, were improved by more than 0.06. The accuracy of predicting SS varied widely among modeling methods. The accuracy of CNN model was obviously higher than that of BPNN and ELM models. The optimal hybrid model for predicting SS constructed in this study is SA-CNN model (R2 = 0.79, RMSE = 9.41 g kg−1, RPD = 1.81, RPIQ = 2.37). This study showed that the spectral feature bands selection methods can reduce the influence of environmental factors on in situ spectroscopy and significantly enhance the inversion accuracy of SS. The present study provided that estimating SS using in situ Vis-NIR spectral is feasible. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) plays an important role in the global carbon cycle and soil fertility supply. Rapid and accurate estimation of SOC content could provide critical information for crop production, soil management and soil carbon pool regulation. Many researchers have confirmed the feasibility and great potential of visible and near-infrared (Vis-NIR) spectroscopy in evaluating SOC content rapidly and accurately. Here, to evaluate the feasibility of different spectral bands variable selection methods for SOC prediction, we collected a total of 330 surface soil samples from the cotton field in the Alar Reclamation area in the southern part of Xinjiang, which is located in the arid region of northwest China. Then, we estimated the SOC content using laboratory Vis-NIR spectral. The Particle Swarm optimization (PSO), Competitive adaptive reweighted sampling (CARS) and Ant colony optimization (ACO) were adopted to select SOC feature bands. The partial least squares regression (PLSR), random forest (RF) and convolutional neural network (CNN) inversion models were constructed by using full-bands (400–2400 nm) spectra (R) and feature bands, respectively. And we also analyzed the effects of spectral feature band selection methods and modeling methods on the prediction accuracy of SOC. The results indicated that: (1) There are significant differences in the feature bands selected using different methods. The feature bands selected methods substantially reduced the spectral variable dimensionality and model complexity. The models built by the feature bands selected by CARS, PSO and ACO methods showed the different potential of improvement in model accuracy compared with the full-band models. (2) The CNN model had the best performance for predicting SOC. The R2 of the optimal CNN model is 0.90 in the validation, which was improved by 0.05 and 0.04 in comparison with the PLSR and RF model, respectively. (3) The highest prediction accuracy was archived by the CNN model using the feature bands selected by CARS (validation set R2 = 0.90, RMSE = 0.97 g kg−1, RPD = 3.18, RPIQ = 3.11). This study indicated that using the CARS method to select spectral feature bands, combined with the CNN modeling method can well predict SOC content with higher accuracy. [ABSTRACT FROM AUTHOR]

Agricultural fields are essential in providing human beings with paramount food and other materials. Quick and accurate identification of agricultural fields from the remote sensing images is a crucial task in digital and precision agriculture. Deep learning methods have the advantages of fast and accurate image segmentation, especially for extracting the agricultural fields from remote sensing images. This paper proposed a deep neural network with a dual attention mechanism and a multi-scale feature fusion (Dual Attention and Scale Fusion Network, DASFNet) to extract the cropland from a GaoFen-2 (GF-2) image of 2017 in Alar, south Xinjiang, China. First, we constructed an agricultural field segmentation dataset from the GF-2 image. Next, seven evaluation indices were selected to assess the extraction accuracy, including the location shift, to reveal the spatial relationship and facilitate a better evaluation. Finally, we proposed DASFNet incorporating three ameliorated and novel deep learning modules with the dual attention mechanism and multi-scale feature fusion methods. The comparison of these modules indicated their effects and advantages. Compared with different segmentation convolutional neural networks, DASFNet achieved the best testing accuracy in extracting fields with an F1-score of 0.9017, an intersection over a union of 0.8932, a Kappa coefficient of 0.8869, and a location shift of 1.1752 pixels. Agricultural fields can be extracted automatedly and accurately using DASFNet, which reduces the manual record of the agricultural field information and is conducive to further farmland surveys, protection, and management. [ABSTRACT FROM AUTHOR]

Climate change has proven to have a profound impact on the growth of vegetation from various points of view. Understanding how vegetation changes and its response to climatic shift is of vital importance for describing their mutual relationships and projecting future land–climate interactions. Arid areas are considered to be regions that respond most strongly to climate change. Xinjiang, as a typical dryland in China, has received great attention lately for its unique ecological environment. However, comprehensive studies examining vegetation change and its driving factors across Xinjiang are rare. Here, we used the remote sensing datasets (MOD13A2 and TerraClimate) and data of meteorological stations to investigate the trends in the dynamic change in the Normalized Difference Vegetation Index (NDVI) and its response to climate change from 2000 to 2019 across Xinjiang based on the Google Earth platform. We found that the increment rates of growth-season mean and maximum NDVI were 0.0011 per year and 0.0013 per year, respectively, by averaging all of the pixels from the region. The results also showed that, compared with other land use types, cropland had the fastest greening rate, which was mainly distributed among the northern Tianshan Mountains and Southern Junggar Basin and the northern margin of the Tarim Basin. The vegetation browning areas primarily spread over the Ili River Valley where most grasslands were distributed. Moreover, there was a trend of warming and wetting across Xinjiang over the past 20 years; this was determined by analyzing the climate data. Through correlation analysis, we found that the contribution of precipitation to NDVI (R2 = 0.48) was greater than that of temperature to NDVI (R2 = 0.42) throughout Xinjiang. The Standardized Precipitation and Evapotranspiration Index (SPEI) was also computed to better investigate the correlation between climate change and vegetation growth in arid areas. Our results could improve the local management of dryland ecosystems and provide insights into the complex interaction between vegetation and climate change. [ABSTRACT FROM AUTHOR]

Soil salinization, one of the most severe global land degradation problems, leads to the loss of arable land and declines in crop yields. Monitoring the distribution of salinized soil and degree of salinization is critical for management, remediation, and utilization of salinized soil; however, there is a lack of thorough assessment of various data sources including remote sensing and landscape characteristics for estimating soil salinity in arid and semi-arid areas. The overall goal of this study was to develop a framework for estimating soil salinity in diverse landscapes by fusing information from satellite images, landscape characteristics, and appropriate machine learning models. To explore the spatial distribution of soil salinity in southern Xinjiang, China, as a case study, we obtained 151 soil samples in a field campaign, which were analyzed in laboratory for soil electrical conductivity. A total of 35 indices including remote sensing classifiers (11), terrain attributes (3), vegetation spectral indices (8), and salinity spectral indices (13) were calculated or derived and correlated with soil salinity. Nine were used to model and estimate soil salinity using four predictive modelling approaches: partial least squares regression (PLSR), convolutional neural network (CNN), support vector machine (SVM) learning, and random forest (RF). Testing datasets were divided into vegetation-covered and bare soil samples and were used for accuracy assessment. The RF model was the best regression model in this study, with R2 = 0.75, and was most effective in revealing the spatial characteristics of salt distribution. Importance analysis and path modeling of independent variables indicated that environmental factors and soil salinity indices including digital elevation model (DEM), B10, and green atmospherically resistant vegetation index (GARI) showed the strongest contribution in soil salinity estimation. This showed a great promise in the measurement and monitoring of soil salinity in arid and semi-arid areas from the integration of remote sensing, landscape characteristics, and using machine learning model. [ABSTRACT FROM AUTHOR]

Soil salinization and the shortage of water resources have become important factors affecting the sustainable development of agriculture in the southern Xinjiang. Agricultural flooding in winter or spring is the main means to reduce the damage of secondary soil salinization in southern Xinjiang. However, the spatial heterogeneity of soil salinization is not considered in previous irrigation strategy. Excessive irrigation always leads to a serious waste of agricultural water resources. Irrigation zones delineation based on the degree of soil salinization is beneficial for the management of agricultural salinized land and the improvement of water use efficiency in arid areas. This study used 1599 sets of apparent electrical conductivity (ECa) data obtained before winter irrigation and after crop harvest to characterize soil salinity directly. And the spatial heterogeneity of soil salinity in the study area was analyzed directly using ECa values and statistical as well as geostatistical methods. Three main variables obtained from apparent electrical conductivity measurements (EC 0.375 , EC 0.75 , EC 1.5), vegetation information (NDVI, GNDVI, SAVI), which indirectly reflected the degree of salinity, and environmental factors (Clay, Sand, Silt, DEM), which affecting the distribution of salinity, were used as auxiliary variables. Four zoning models including soil information, soil + vegetation information, soil information + environmental factors and soil + vegetation information + environmental factors were constructed. A multi-scale segmentation algorithm was used to delineate for four zone patterns and evaluate the results of management zones based on the different information. The results showed that there was a strong spatial heterogeneity of soil salinity in the study area, with the coefficients of variation of ECa all higher than 74 %. But the mean of coefficient of variation intra-zone in each zone after delineation was less than 24 %, which was significantly lower than the value before delineation. The zoning result based on only soil information was more fragmented than other zoning results, and the homogeneity intra-zone and heterogeneity among zones were both lower. This result was not desirable to the development and effective implementation of water resource allocation. Management zones delineation with additional vegetation information or environmental factors showed a better zoning result. Delineation using multisource data of soil, vegetation information and environmental factors not only had the highest homogeneity intra-zone and heterogeneity among zones, but also facilitated the implementation of variable-rate irrigation. Consequently, salinity management zoning, which integrates soil, vegetation information and environmental factors, will facilitate the implementation of efficient irrigation with variable irrigating rate. • We use different information variables to delineate salinity management zones. • The multi-scale segmentation algorithm can effectively delineate homogeneous regions. • Management zones delineation using multisource information is more scientific and reasonable. • Multi-source information on salinity management zones facilitates the provision of effective irrigation strategies. [ABSTRACT FROM AUTHOR]

[Display omitted] • All four deep learning models can perform excellent potential for estimating SIC. • LSTM was the best-performing deep learning algorithm for estimating SIC. • The variable selection algorithm could improve the estimation of SIC. • The IRF was the best algorithm to select spectral variables relevant to SIC. Soil inorganic carbon (SIC) is the primary component of the soil carbon pool in arid and semiarid regions and strongly impacts the global carbon cycle, ecosystem services, and soil functions. The global climate change and intensify of human activities, could substantially change SIC, which highlights the importance of monitoring SIC. Rapid and accurate estimation of SIC concentration is critical for soil inorganic carbon pool monitoring. Currently, visible near-infrared (Vis-NIR) spectroscopy is a promising technique for estimating SIC via a rapid and cost-effective manner. Thus, in this study, we collected 315 topsoil samples from the Alar Reclamation Area in South Xinjiang, China, and measured their Vis-NIR spectra and SIC content. Then, we used deep learning algorithms, including a one-dimensional convolutional neural network (1D-CNN), two-dimensional convolutional neural network (2D-CNN), long short-term memory network (LSTM), and deep belief network (DBN), combined with variable selection algorithms (particle swarm algorithm (PSO), interval random frog (IRF), competitive adaptive reweighting algorithm (CARS), ant colony algorithm (ACO), and iteratively retaining informative variables (IRIV) to estimate SIC. Results showed that all five variable selection algorithms could effectively extract the featured spectral information of SIC, and reduce the number of spectral variables by >97%, simplifying the model structure. The variable selection algorithm could markedly improve the SIC spectral estimation accuracy, and the corresponding estimation accuracy follows the order: IRF > IRIV > PSO > CARS > ACO. All four deep learning models have high prediction accuracy, and the modeling accuracy of each method follow the order: LSTM > 1D-CNN > 2D-CNN > DBN. The combined IRF and LSTM model achieved the highest estimation accuracy (R2 = 0.93, RMSE = 1.26 g kg−1 in the calibration dataset; R2 = 0.92, RMSE = 1.37 g kg−1 in the validation dataset). This study demonstrated that deep learning combined with variable selection algorithms can detect SIC content quickly and accurately. [ABSTRACT FROM AUTHOR]

[Display omitted] • Potential of spectral estimation of SOC was explored in the arid and semi-arid area. • Deep learning outperformed Random Forest in SOC prediction. • Combinational use of feature selection and deep learning can improve SOC prediction. • The sensitivity of various deep learning algorithms depended on the sample size. Soil organic carbon (SOC) plays an important role in soil functioning and also global C balance. Visible-near-infrared (Vis-NIR) spectroscopy can be regarded as a cost-effective alternative to monitor the SOC content. Previously, application of Vis-NIR spectroscopy in the quantitative estimation of SOC in arid and semi-arid regions has received relatively little attention. Here, three different sample sizes of dataset (i.e., 330, 660, and 990) with SOC contents and Vis-NIR spectroscopy measured in the laboratory were obtained from Southern Xinjiang, China. Eight feature selection methods, including Interval Random Frog (IRF), were used to extract the optimal spectral feature subset. Six deep learning (DL) algorithms (e.g., Long Short-Term Memory Neural Networks, LSTM; Deep Belief Networks, DBN) and one machine learning method (Random Forest, RF) were utilized to relate SOC to spectral predictors. The overall objective of this work was to compare the predicted potentials of seven modeling algorithms combined with eight feature selection methods for spectral prediction of SOC. In addition, this paper also investigated the influence of different calibration sample size on the final modeling accuracy for SOC. Results indicated that the DL algorithms outperformed RF for SOC prediction. Among the six DL approaches, the LSTM model performed the best, while the DBN model performed the worst. The one-dimensional-Convolutional Neural Network (1D-CNN), 2D-CNN, Recurrent Neural Network, and DBN algorithms were sensitive to different sample sizes. For the largest dataset (i.e., 990 samples), four of the eight feature selection methods combined with the DL algorithms could improve the prediction for SOC, relative to the corresponding full-spectrum DL models. Among all models developed for SOC, the IRF-LSTM model achieved the optimal prediction, with the validation R2 of 0.89. Our findings provided both theoretical and technical guidance for the spectral estimation of SOC with the relatively low values in arid and semi-arid area. [ABSTRACT FROM AUTHOR]

Traditional farmland management approaches treat farmland as a homogeneous unit, leading to uneven distribution and waste of resources, which is contrary to precision agriculture. Management zones (MZ) delineation is an essential method of site-specific management (SSM) for accomplishing variable rate input of resources in subregions by recognizing the heterogeneity of farmland soils. Aimed at the soil salinization problem in Xinjiang, this study estimates the salt content of soil profiles and essential nutrient content using time series Sentinel-2 images and machine learning methods, and describes crop growth conditions through dynamic time warping. Subsequently, geographically weighted principal component analysis and possibilistic fuzzy C-means algorithm were employed to outline MZ based on soil-crop characteristics. Three scales (10 m, 100 m, and field scales divided by ridges) were examined to obtain the optimal scale of delineation. Soil-crop properties were estimated at a spatial resolution of 10 m using machine learning methods with R2 > 0.65. In the zoning process, the field scale was selected with accuracy and practicality in agricultural practices, with comparatively higher homogeneity within zones and variation between zones. The soils in the study area were categorized into three zones at field scale with a fuzzy performance index of 0.68, a normalized classification entropy of 0.71, and fuzzy class of 0.30. Soil function is primarily restricted by salinity and fertility. Fertilizer recommendation strategies were given for the three zones. This study uses time-series remote sensing images to obtain a high-precision estimation of soil-crop characteristics at 10 m, and optimize the scale of farmland management zoning, which provides a valuable technology and scale selection for SSM for cotton management zone delineation in saline farmland in arid and semi-arid regions. • Soil nutrient content was estimated at a scale of 10 m using remote sensing and machine learning. • Salinity content and crop phenology conditions were described using times-series remote sensing. • Geographically weighted principal component analysis was used to characterize spatial heterogeneity of soil-crop attributes. • Possibilistic fuzzy C-means algorithm were used to delineate management zones based on time-series soil-crop attributes. • Three scales (10 m, 100 m, field scales) were evaluated to obtain the optimal scale of delineation. [ABSTRACT FROM AUTHOR]

[Display omitted] • A total of 47 parameters were comprehensively evaluated the correlation with soil salinity. • Five mathematical transformations were applied to enhance the spectral features in construction. • A novel index (ERSSI) was constructed to indicated salinity in residues remained farmland soil. • ERSSI contributes to estimation soil salinity with residues both in polynomial and ML regression. • Response of the novel index (ERSSI) to soil salinity was revealed according to sensitive bands. Remote sensing indices have been proposed to characterize soil salinity. However, the sensitivity of these indicators is unstable owing to differences in geographic environment and vegetation type. This study investigated the performance of several existing indices to estimate the salinity of topsoil with residues in southern Xinjiang, China. The results showed that these indices were not satisfactory. In order to construct an index that can be used to directly indicate soil salinity in a specific area, novel salinity indices were calculated using optical bands (blue, green, red, vegetation red edge, and shortwave infrared bands) derived from Sentinel-2 multispectral data and Sentinel-1 radar data (backscattering coefficient VV, VH). To enhance the sensitivity of the optical bands, five transformation methods (logarithmic, reciprocal, first-, second-, and third-derivative) were applied to the original spectra. Based on previous studies, statistical methods were used to construct two-, three-, and four-bands indices. One constructed three-bands index with the second-derivative transformation, called the Enhanced Residues Soil Salinity Index (ERSSI), showed the highest correlation with topsoil salinity (r = 0.65 and 0.68 in training and testing). ERSSI establishes a linear relationship in soil salinity estimation with an R2 of 0.53 and a LCCC of 0.65 in training dataset, with an R2 of 0.51 and a LCCC of 0.73 in testing dataset. And it shows contribution in random forest regression with an R2 of 0.80 and a LCCC of 0.86 in training dataset, with an R2 of 0.77 and a LCCC of 0.81 in testing dataset. The ERSSI consisted of the B, G, and SWIR 1 bands, and was sensitive to salinity variations in the residues remaining in farmland soils. This study provides a novel index and method for the accurate and robust assessment and mapping of salinity in farmland covered by crop residues. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel framework was developed to monitor total salt content in the soil profile to a depth of 1 m. • Field experiment and times-series remote sensing data were used to learn spatio-temporal variability. • A total of 42 parameters were evaluated the correlation with soil salinity. • Random Forest-Temporal Convolution Network model was employed for estimation. • Influencing factors of salt content of soil profiles were revealed at field- and parcel-scale. Soil salinization causes a deterioration in soil health and threatens crop growth. Rapid identification of salinization in farmlands is of great significance to improve soil functions and to maintain sustainable land management. As salt moves in soil profiles during plowing and irrigation, the commonly used protocol for measuring and monitoring salt content in topsoil does not provide a thorough assessment. In order to quantify and comprehensively evaluate the salt content in deep soil, this study developed a novel framework for monitoring total salt content in the soil profile to a depth of 1 m by combining information from time-series satellite images and machine learning. The field experiments were conducted in Alar, Southern Xinjiang, with a total of 120 soil samples and 582 measurements of EM38-MK2 apparent electrical conductivity in 2019 and 2020 to quantify the vertical variation in the salt content. A total of 42 covariates derived from time-series Sentinel-2 images, including 20 salinity indices, 10 soil indices, and 12 vegetation indices were used for modeling salinity in the soil profile. From the total covariates, 22 were selected using the Random Forest. Soil salinity which was modeled using a Temporal Convolution Network in 2019 and 2020 and forecast for 2021. The model effectively revealed the spatial and temporal variability of the salt content in the soil profile with R2 of 0.71 and 0.65 for 2019 and 2020, respectively. The proposed new framework provides an effective method to estimate the salt content in the soil profile for precision agriculture in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]