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Mamat Sawut
College of Resources and Environmental Science, Xinjiang University, Urumqi 830046, Xinjiang, China

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Journal article
Published: 10 March 2020 in Computers and Electronics in Agriculture
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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.

ACS Style

Yierxiati Abulaiti; Mamat Sawut; Baidengsha Maimaitiaili; Ma Chunyue. A possible fractional order derivative and optimized spectral indices for assessing total nitrogen content in cotton. Computers and Electronics in Agriculture 2020, 171, 105275 .

AMA Style

Yierxiati Abulaiti, Mamat Sawut, Baidengsha Maimaitiaili, Ma Chunyue. A possible fractional order derivative and optimized spectral indices for assessing total nitrogen content in cotton. Computers and Electronics in Agriculture. 2020; 171 ():105275.

Chicago/Turabian Style

Yierxiati Abulaiti; Mamat Sawut; Baidengsha Maimaitiaili; Ma Chunyue. 2020. "A possible fractional order derivative and optimized spectral indices for assessing total nitrogen content in cotton." Computers and Electronics in Agriculture 171, no. : 105275.

Journal article
Published: 02 December 2019 in Sustainability
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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.

ACS Style

Umut Hasan; Mamat Sawut; Shuisen Chen. Estimating the Leaf Area Index of Winter Wheat Based on Unmanned Aerial Vehicle RGB-Image Parameters. Sustainability 2019, 11, 6829 .

AMA Style

Umut Hasan, Mamat Sawut, Shuisen Chen. Estimating the Leaf Area Index of Winter Wheat Based on Unmanned Aerial Vehicle RGB-Image Parameters. Sustainability. 2019; 11 (23):6829.

Chicago/Turabian Style

Umut Hasan; Mamat Sawut; Shuisen Chen. 2019. "Estimating the Leaf Area Index of Winter Wheat Based on Unmanned Aerial Vehicle RGB-Image Parameters." Sustainability 11, no. 23: 6829.

Journal article
Published: 28 February 2018 in Sustainability
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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.

ACS Style

Jumeniyaz Seydehmet; Guang Lv; İlyas Nurmemet; Tayierjiang Aishan; Abdulla Abliz; Mamat Sawut; Abdugheni Abliz; Mamattursun Eziz. Model Prediction of Secondary Soil Salinization in the Keriya Oasis, Northwest China. Sustainability 2018, 10, 656 .

AMA Style

Jumeniyaz Seydehmet, Guang Lv, İlyas Nurmemet, Tayierjiang Aishan, Abdulla Abliz, Mamat Sawut, Abdugheni Abliz, Mamattursun Eziz. Model Prediction of Secondary Soil Salinization in the Keriya Oasis, Northwest China. Sustainability. 2018; 10 (3):656.

Chicago/Turabian Style

Jumeniyaz Seydehmet; Guang Lv; İlyas Nurmemet; Tayierjiang Aishan; Abdulla Abliz; Mamat Sawut; Abdugheni Abliz; Mamattursun Eziz. 2018. "Model Prediction of Secondary Soil Salinization in the Keriya Oasis, Northwest China." Sustainability 10, no. 3: 656.

Journal article
Published: 13 July 2015 in Remote Sensing
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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.

ACS Style

İlyas Nurmemet; Abduwasit Ghulam; Tashpolat Tiyip; Racha Elkadiri; Jian-Li Ding; Matthew Maimaitiyiming; Abdulla Abliz; Mamat Sawut; Fei Zhang; Abdugheni Abliz; Qian Sun. Monitoring Soil Salinization in Keriya River Basin, Northwestern China Using Passive Reflective and Active Microwave Remote Sensing Data. Remote Sensing 2015, 7, 8803 -8829.

AMA Style

İlyas Nurmemet, Abduwasit Ghulam, Tashpolat Tiyip, Racha Elkadiri, Jian-Li Ding, Matthew Maimaitiyiming, Abdulla Abliz, Mamat Sawut, Fei Zhang, Abdugheni Abliz, Qian Sun. Monitoring Soil Salinization in Keriya River Basin, Northwestern China Using Passive Reflective and Active Microwave Remote Sensing Data. Remote Sensing. 2015; 7 (7):8803-8829.

Chicago/Turabian Style

İlyas Nurmemet; Abduwasit Ghulam; Tashpolat Tiyip; Racha Elkadiri; Jian-Li Ding; Matthew Maimaitiyiming; Abdulla Abliz; Mamat Sawut; Fei Zhang; Abdugheni Abliz; Qian Sun. 2015. "Monitoring Soil Salinization in Keriya River Basin, Northwestern China Using Passive Reflective and Active Microwave Remote Sensing Data." Remote Sensing 7, no. 7: 8803-8829.