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The leaf area index (LAI) is not only an important parameter for monitoring crop growth, but also an important input parameter for crop yield prediction models and hydrological and climatic models. Several studies have recently been conducted to estimate crop LAI using unmanned aerial vehicle (UAV) multispectral and hyperspectral data. However, there are few studies on estimating the LAI of winter wheat using unmanned aerial vehicle (UAV) RGB images. In this study, we estimated the LAI of winter wheat at the jointing stage on simple farmland in Xinjiang, China, using parameters derived from UAV RGB images. According to gray correlation analysis, UAV RGB-image parameters such as the Visible Atmospherically Resistant Index (VARI), the Red Green Blue Vegetation Index (RGBVI), the Digital Number (DN) of Blue Channel (B) and the Green Leaf Algorithm (GLA) were selected to develop models for estimating the LAI of winter wheat. The results showed that it is feasible to use UAV RGB images for inverting and mapping the LAI of winter wheat at the jointing stage on the field scale, and the partial least squares regression (PLSR) model based on the VARI, RGBVI, B and GLA had the best prediction accuracy (R2 = 0.776, root mean square error (RMSE) = 0.468, residual prediction deviation (RPD) = 1.838) among all the regression models. To conclude, UAV RGB images not only have great potential in estimating the LAI of winter wheat, but also can provide more reliable and accurate data for precision agriculture management.

Nitrogen is the key biochemical component of chlorophyll, protein and enzymes, and it is widely used as an indicator of photosynthesis and plant nutrient levels. Hyper-spectral data-based estimation of nitrogen allows for a low-cost, effective and environmentally beneficial diagnosis of plant growth. In this paper, a novel approach to characterize the Total Nitrogen Content (TNC) from canopy spectral reflectance through a fractional order derivative (FOD) and optimized spectral indices (NDSI, RSI) is proposed. A total of 60 sampling plots designed in field experiments, canopy spectral data and total nitrogen content are tested for each plot. Optimized remote sensing indices derived from FOD spectra were applied to investigate sensitive wavebands; finally, a Support Vector Machine Regression model for estimating cotton TNC was generated. Our results showed that small FOD orders improved the spectral resolution and provided abundant absorption features; as the orders increased, the spectral strength decreased and the curves were smoothed gradually. The coefficient of correlation (R) peak appeared at the 1.25 order with a value of 0.652. The coefficient of determination (R2) between TNCs and optimized spectral indices peaked at NDSI beyond the 1.5 order (R2 = 0.592). Fourteen TNC estimation models were created via SVR methods using optimized spectral indices. Modeling results indicated that the optimal model was original reflectance-RSI, where the highest R2 was 0.784, the lowest root mean square error (RMSE) was 1.333, and the residual prediction deviation (RPD) was 1.80. Overall, FOD can potentially exploit spectral characteristics and eliminate spectral redundancy. However, original reflectance still shows a high potential for accurate predictions of the total nitrogen content in cotton.

Significant anthropogenic and biophysical changes have caused fluctuations in the soil salinization area of the Keriya Oasis in China. The Driver-Pressure-State-Impact-Response (DPSIR) sustainability framework and Bayesian networks (BNs) were used to integrate information from anthropogenic and natural systems to model the trend of secondary soil salinization. The developed model predicted that light salinization (vegetation coverage of around 15–20%, soil salt 5–10 g/kg) of the ecotone will increase in the near term but decelerate slightly in the future, and that farmland salinization will decrease in the near term. This trend is expected to accelerate in the future. Both trends are attributed to decreased water logging, increased groundwater exploitation, and decreased ratio of evaporation/precipitation. In contrast, severe salinization (vegetation coverage of around 2%, soil salt ≥20 g/kg) of the ecotone will increase in the near term. This trend will accelerate in the future because decreased river flow will reduce the flushing of severely salinized soil crust. Anthropogenic factors have negative impacts and natural causes have positive impacts on light salinization of ecotones. In situations involving severe farmland salinization, anthropogenic factors have persistent negative impacts.

a b s t r a c t Sand content is a textural property of soils closely related to soil quality. A fast determination of sand content at large scales is paramount importance for monitoring soil degradation to improve agricultural practices. The main objective of this study is to evaluate the ability of the thermal infrared region (TIR) to estimate sand content of soils. Thermal infrared spectra obtained in the field from a Fourier Transform Spectrometer are used to develop a partial least square regression model (PLSR) that translates thermal emittance to soil texture properties. Our results show that the 9.435-9.473 m wavelength regions hold a great promise for prediction of sand content. Coefficient of determination R2 is 0.87 and standard error (SE) is 2.79. We also show that second derivative of thermal spectral profiles is very useful to detect kaolinite in sand dominated soils. The results of this study provide further insights for developing future thermal sensors aimed at predicting soil quality as indicated by the sand content and other textural properties.

The interaction between land use/cover change and landscape pattern is pivotal in research concerning global environmental change. This study uses three different Landsat images of 1989, 1998 and 2009 to study the land use/cover and landscape pattern changes in the middle reaches of the Tarim River basin. envi®, erdas®, ArcGIS® and fragstats® software were used to analyse the land use/cover changes. The objectives of study were to map and study the changes in land use/cover and landscape pattern, and propose some possible factors in making the land use/cover changes from 1989 to 2009. Seven different types of land use/cover are analysed, and the results are listed in tables. From 1989 to 1998, the percentage of farmland, slight–moderate saline land, heavy saline land and water areas have increased; woodland, desert and the undeveloped land have decreased. From 1998 to 2009, farmland, heavy saline land and the undeveloped land have increased; the other types of land use/cover have decreased. The gravity centre of each land use/cover types has shifted. The farthest shifting of the gravity centre was heavy saline land, which occurred between 1989 and 1998. The transformation and changes of land use/covers and landscape occurred more frequently from 1989 to 2009. Other types of land use and land cover changes to saline land have increased, which implied that a serious salinization took place in the Tarim Basin. The results from this study would show the adverse environmental changes (e.g. salinization and desertification) and they can be used for future sustainable management of land resources. Copyright © 2013 John Wiley & Sons, Ltd.

Vegetation fractional coverage (VFC) is an important index to describe and evaluate the ecological system. The vegetation index is widely used to monitor vegetation coverage in the field of remote sensing (RS). In this paper, the author conducted a case study of the delta oasis of Weigan and Kuqa rivers, which is a typical saline area in the Tarim River Watershed. The current study was based on the TM/ETM+ images of 1989, 2001, and 2006, and supported by Geographic Information System (GIS) spatial analysis, vegetation index, and dimidiate pixel model. In addition, VBSI (vegetation, bare soil and shadow indices) suitable for TM/ETM+ images, constructed with FCD (forest canopy density) model principle and put forward by ITTO (International Tropical Timber Organization), was used, and it was applied to estimate the VFC. The estimation accuracy was later proven to be up to 83.52%. Further, the study analyzed and appraised the changes in vegetation patterns and revealed a pattern of spatial change in the vegetation coverage of the study area by producing the map of VFC levels in the delta oasis. Forest, grassland, and farmland were the three main land-use types with high and extremely-high coverage, and they played an important role in maintaining the vegetation. The forest area determined the changes of the coverage area, whereas the other two land types affected the directions of change. Therefore, planting trees, protecting grasslands, reclaiming farmlands, and controlling unused lands should be included in a long-term program because of their importance in keeping regional vegetation coverage. Finally, the dynamic variation of VFC in the study area was evaluated according to the quantity and spatial distribution rendered by plant cover digital images to deeply analyze the reason behind the variation.

There has been growing interest in the use of reflectance spectroscopy as a rapid and inexpensive tool for soil characterization. In this study, 53 soil samples were collected from the oasis in the Weigan and Kuqa River delta along the middle reaches of Tarim River to investigate the level of soil chemical components in relation to soil spectral. An approach combining spectral technology and multi-variant statistical analysis was used to determine the reflectance spectral features of saline soil. The spectral data was first pretreated to remove noises and absorption bands from water, which eliminated influence from instrument errors and other external background factors. Several spectral absorption features were calculated for several saline soil samples to confirm that soil at the same salinity level had similar absorption spectral properties. Secondly, a correlation relationship between reflectance spectra and salinity factors was estimated by bivariate correlation method. Fourteen salinity factors including eight major ions and soil electrical conductivity (EC), soil salt content (SSC), pH, and total dissolved solid (TDS) in the saline soil were evaluated. Datasets of the salinity factors that correlated significantly with field data measurements of reflectance rate and the corresponding spectrum data were used to construct quantitative regression models. According to the multiple linear regression analysis, SSC, SO4 2−, TDS, and EC had a correlation coefficient at 0.746, 0.908, 0.798, and 0.933 with the raw spectral data, respectively, which confirmed strong correlation between salinity factors and soil reflectance spectrum. Findings from this study will have significant impact on characterization of spectral features of saline soil in oasis in arid land.

In this paper, the spatial and temporal variability of groundwater depth and groundwater salinity associated with agricultural activities and land cover and land use (LCLU) in the Keriya Oasis, Xinjiang Uyghur Autonomous Region of China, was investigated. A decision tree classification was developed for LCLU mapping by using Landsat 8 imagery. Radial basis function, Pearson’s correlation coefficient, Moran’s I statistics and geographically weighted regression were employed to identify the spatial and temporal relationship between topsoil salinity and LCLU types. The results showed that groundwater depth in about 75 % of the study area was less than 3.0 m. Groundwater salinity showed an increasing trend with decreasing groundwater depth. A relatively higher positive correlation (r = 0.87) was found between groundwater salinity and topsoil salinity. Topsoil salinity was exacerbated by agricultural activities at the upstream of seasonal rivers, causing severe soil salinization and soil degradation in the downstream. Both groundwater depth and soil salinity showed seasonal fluctuations with the shallowest groundwater level occurring in spring, while during summer and autumn, groundwater depth increased due to the extensive of agricultural water consumption and the regional evapotranspiration, which was followed by a winter recharge; non-saline groundwater region was 21 % of the research area in spring, which dropped to 1.1, 1.0 and 1.2 %, respectively, during the summer, autumn and winter. The results also demonstrated that shallow groundwater depth of 2.5 m is the critical depth value for soil salinization in the Keriya Oasis.

Aiming at the remote sensing application has been increasingly relying on ground object spectral characteristics. In order to further research the spectral reflectance characteristics in arid area, this study was performed in the typical delta oasis of Weigan and Kuqa rivers located north of Tarim Basin. Data were collected from geo-targets at multiple sites in various field conditions. The spectra data were collected for different soil types including saline-alkaline soil, silt sandy soil, cotton field, and others; vegetations of Alhagi sparsifolia, Phragmites australis, Tamarix, Halostachys caspica, etc., and water bodies. Next, the data were processed to remove high-frequency noise, and the spectral curves were smoothed with the moving average method. The derivative spectrum was generated after eliminating environmental background noise so that to distinguish the original overlap spectra. After continuum removal of the undesirable absorbance, the spectrum curves were able to highlight features for both optical absorbance and reflectance. The spectrum information of each ground object is essential for fully utilizing the multispectrum data generated by remote sensing, which will need a representative spectral library. In this study using ENVI 4.5 software, a preliminary spectral library of surface features was constructed using the data surveyed in the study area. This library can support remote sensing activities such as feature investigation, vegetation classification, and environmental monitoring in the delta oasis region. Future plan will focus on sharing and standardizing the criteria of professional spectral library and to expand and promote the utilization of the spectral databases.

Soil salinization is one of the most widespread soil degradation processes on Earth, especially in arid and semi-arid areas. The salinized soil in arid to semi-arid Xinjiang Uyghur Autonomous Region in China accounts for 31% of the area of cultivated land, and thus it is pivotal for the sustainable agricultural development of the area to identify reliable and cost-effective methodologies to monitor the spatial and temporal variations in soil salinity. This objective was accomplished over the study area (Keriya River Basin, northwestern China) by adopting technologies that heavily rely on, and integrate information contained in, a readily available suite of remote sensing datasets. The following procedures were conducted: (1) a selective principle component analysis (S-PCA) fusion image was generated using Phased Array Type L-band SAR (PALSAR) backscattering coefficient (σ°) and Landsat Enhanced Thematic Mapper Plus (ETM+) multispectral image of Keriya River Basin; and (2) a support vector machines (SVM) classification method was employed to classify land cover types with a focus on mapping salinized soils; (3) a cross-validation method was adopted to identify the optimum classification parameters, and obtain an optimal SVM classification model; (4) Radarsat-2 (C band) and PALSAR polarimetric images were used to analyze polarimetric backscattering behaviors in relation to the variation in soil salinization; (5) a decision tree (DT) scheme for multi-source optical and polarimetric SAR data integration was proposed to improve the estimation and monitoring accuracies of soil salinization; and (6) detailed field observations and ground truthing were used for validation of the adopted methodology, and quantity and allocation disagreement measures were applied to assess classification outcome. Results showed that the fusion of passive reflective and active microwave remote sensing data provided an effective tool in detecting soil salinization. Overall accuracy of the adopted SVM classifier with optimal parameters for fused image of ETM+ and PALSAR data was 91.25% with a Kappa coefficient of 0.89, which was further improved by the DT data integration and classification method yielding an accuracy of 93.01% with a Kappa coefficient of 0.92 and lower disagreement of quantity and allocation.

Soil phosphorus provides nutrient elements for plants, is one of important parameters for evaluating soil quality. The traditional method for soil total phosphorus content (STPC) measurement is not effective and time-consuming. However, remote sensing (RS) enables us to determine STPC in a fast and efficient way. Studies on the estimation of STPC in near-infrared spectroscopy have been developed by scholars, but model accuracy is still poor due to the low absorption coefficient and unclear absorption peak of soil phosphorus in near-infrared. In order to solve the deficiency which thermal-infrared emissivity estimate desert soil total phosphorus content, and could improve precision of estimation deserts soil total phosphorus. In this paper, characteristics of soil thermal-infrared emissivity are analyzed on the basis of laboratory processing and spectral measurement of deserts soil samples from the eastern Junggar Basin. Furthermore, thermal-infrared emissivity based RS models for STPC estimation are established and accuracy assessed. Results show that: when STPC is higher than 0.200 g x kg(-1), the thermal-infrared emissivity increases with the increase of STPC on the wavelength between 8.00 microm and 13 microm, and the emissivity is more sensitive to STPC on the wavelength between 9.00 and 9.6 microm; the estimate mode based on multiple stepwise regression was could not to estimate deserts soil total phosphorus content from thermal-infrared emissivity because the estimation effects of them were poor. The estimation accuracy of model based on partial least squares regression is higher than the model based on multiple stepwise regression. However, the accuracy of second-order differential estimation model based on partial least square regression is higher than based on multiple stepwise regression; The first differential of continuous remove estimation model based on partial least squares regression is the best model with R2 of correction and verification are up to 0.97 and 0.82 respectively, and RMSE of correction and verification are only 0.0106 and 0.015 7 respectively, RPD is 2.62. Research results provide optimized models for remotely sensed analysis on deserts soil total phosphorus content and could realize timeliness and effective monitoring on the space-time dynamic of deserts soil total phosphorus content for future regional ecological restoration.