A Soil Sampling Method Based on Field Measurements, Remote Sensing Images and Kriging Technique

2011 ◽  
Vol 383-390 ◽  
pp. 5350-5356
Author(s):  
Quan Quan ◽  
Bing Shen
Keyword(s):  
Remote Sensing ◽  
Soil Properties ◽  
Soil Salinity ◽  
Sampling Method ◽  
Land Reclamation ◽  
Management Practice ◽  
Field Measurements ◽  
Soil Sampling ◽  
Soil Reclamation ◽  
Remote Sensing Images

How to determine a representative and economical soil sampling method that combines soil properties with advanced technologies has been an unsolved issue in soil related studies. This paper proposes a new method for soil sampling based on some measured salinity data and remote sensing images, as well as analysis of spatial distributions of soil properties in Lubotan land reclamation area in Shaanxi, China. The results showed that with the available data sets of 33 points, up to 101 unknown points can be estimated, and further interpolation of 343 points displayed spatial distribution of soil salinity in the study area, the coefficients of determination (R2) for the predicted soil salinity were 0.817, respectively. A further Kriging analysis for top soil salinity distribution in the study area showed that soil salinity has a medium degree of autocorrelation and low variability. The study in this paper may help understand the effect of soil reclamation efforts and local water management practice.

Analysis of Land Use Change in Binhai New Area Based on Remote Sensing Images

2014 ◽  
Vol 933 ◽  
pp. 1014-1018
Author(s):  
Chun Zhe Xia ◽  
Xiao Shen Zheng ◽  
Meng Yin Zhao
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Economic Development ◽  
Land Use Change ◽  
Land Reclamation ◽  
Ecological Environment ◽  
Remote Sensing Images ◽  
Construction Sites ◽  
Salt Works ◽  
Unused Land

Based on the TM remote sensing images in autumn 1992, 2001 and 2009, the land use change of Binhai New Area is analyzed through the ENVI software. During remote sensing images processed, Binhai New Area is collected according to the administrative zoning maps. The results of land use change are vegetation cover and water changing little, which show the ecological environment remained stable in overall Binhai New Area. At that time, the area of unused land and salt works area is reduced, and the area of land reclamation and construction sites is increased, which indicates the rapid economic development of Binhai new area in past 20 years.

scholarly journals Understanding Geographic Information Systems and Global Positioning Systems in Horticultural Applications

1999 ◽  
Vol 9 (4) ◽  
pp. 539-547 ◽  
Author(s):  
Ronnie W. Heiniger
Keyword(s):  
Remote Sensing ◽  
Information Systems ◽  
Soil Properties ◽  
Crop Management ◽  
Soil Sampling ◽  
Geographical Information ◽  
Global Positioning Systems ◽  
Positioning Systems ◽  
Application Systems ◽  
Global Positioning

New technologies such as differential global positioning systems (DGPS) and geographical information systems (GIS) are making it possible to manage variability in soil properties and the microenvironment within a field. By providing information about where variability occurs and the patterns that exist in crop and soil properties, DGPS and GIS technologies have the potential of improving crop management practices. Yield monitoring systems linked to DGPS receivers are available for several types of horticultural crops and can be used in variety selection and/or improving crop management. Precision soil sampling and remote sensing technologies can be used to scout for infestations of insects, diseases, or weeds, to determine the distribution of soil nutrients, and to monitor produce quality by measuring crop vigor. Combined with variable rate application systems, precision soil sampling and remote sensing can help direct fertilizer, herbicide, pesticide, and/or fungicide applications to only those regions of the field that require soil amendments or are above threshold levels. This could result in less chemical use and improved crop performance. As with any information driven system, the data must be accurate, inexpensive to collect, and, most importantly, must become part of a decision process that results in improvements in crop yield, productivity, and/or environmental stewardship.

scholarly journals Detection and modeling of soil salinity variations in arid lands using remote sensing data

2021 ◽  
Vol 13 (1) ◽  
pp. 443-453
Author(s):  
Abduldaem S. Alqasemi ◽  
Majed Ibrahim ◽  
Ayad M. Fadhil Al-Quraishi ◽  
Hakim Saibi ◽  
A’kif Al-Fugara ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Prediction Models ◽  
Field Measurements ◽  
Soil Salinization ◽  
Landsat 8 ◽  
Salinity Variations

Abstract Soil salinization is a ubiquitous global problem. The literature supports the integration of remote sensing (RS) techniques and field measurements as effective methods for developing soil salinity prediction models. The objectives of this study were to (i) estimate the level of soil salinity in Abu Dhabi using spectral indices and field measurements and (ii) develop a model for detecting and mapping soil salinity variations in the study area using RS data. We integrated Landsat 8 data with the electrical conductivity measurements of soil samples taken from the study area. Statistical analysis of the integrated data showed that the normalized difference vegetation index and bare soil index showed moderate correlations among the examined indices. The relation between these two indices can contribute to the development of successful soil salinity prediction models. Results show that 31% of the soil in the study area is moderately saline and 46% of the soil is highly saline. The results support that geoinformatic techniques using RS data and technologies constitute an effective tool for detecting soil salinity by modeling and mapping the spatial distribution of saline soils. Furthermore, we observed a low correlation between soil salinity and the nighttime land surface temperature.

scholarly journals Research on the Prediction of Several Soil Properties in Heihe River Basin Based on Remote Sensing Images

2021 ◽  
Vol 13 (24) ◽  
pp. 13930
Author(s):  
Zhihui Li ◽  
Yang Yang ◽  
Siyu Gu ◽  
Boyu Tang ◽  
Jing Zhang
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Properties ◽  
Bulk Density ◽  
River Basin ◽  
Environmental Modeling ◽  
Heihe River Basin ◽  
Experimental Results ◽  
Heihe River ◽  
Remote Sensing Images

Soil property monitoring is useful for sustainable agricultural production and environmental modeling. It is possible to automatically predict soil properties in a wide range based on remote sensing images. Heihe River Basin was chosen as the research area. Measurements on three soil properties, which were pH, organic carbon, and bulk density, were available there. Two kinds of attributes were extracted, which were the remote sensing index and terrain attributes. The prediction models were constructed by random forest algorithms. The features were determined by combining correlation statistics with prediction error, and different features were selected for each of the three properties. The validation experimental results are presented. The error results were as follows: pH (MAE = 0.28, RMSE = 0.39, R2 = 0.41), organic carbon (MAE = 4.75, RMSE = 8.26, R2 = 0.75), and bulk density (MAE = 0.11, RMSE = 0.13, R2 = 0.70). Through the analysis and comparison of the experimental results, it was proven that the algorithm in this paper had a good performance in the prediction of organic carbon and bulk density.

scholarly journals A Multi-Level Non-Uniform Spatial Sampling Method for Accuracy Assessment of Remote Sensing Image Classification Results

2020 ◽  
Vol 10 (16) ◽  
pp. 5568
Author(s):  
Zhenhua Wang ◽  
Lizhi Xu ◽  
Qing Ji ◽  
Wei Song ◽  
Lingqun Wang
Keyword(s):  
Remote Sensing ◽  
Spatial Correlation ◽  
Sampling Method ◽  
Sampling Methods ◽  
Accuracy Assessment ◽  
Remote Sensing Image ◽  
Spatial Sampling ◽  
Remote Sensing Images ◽  
Multi Level ◽  
Sample Points

Accuracy assessment of classification results has important significance for the application of remote sensing images, which can be achieved by sampling methods. However, the existing sampling methods either ignore spatial correlation or do not consider spatial heterogeneity. Here, we proposed a multi-level non-uniform spatial sampling method (MNSS) for the accuracy assessment of classification results. Taking the remote sensing image of Kobo Askov, Texas, USA, as an example, the classification result of this image was obtained by Support Vector Machine (SVM) classifier. In the proposed MNSS, the studied spatial region was zoned from high to low resolution based on the features of spatial correlation. Then, the sampling rate of each zone was deduced from the low to high resolution based on the spatial heterogeneity. Finally, the positions of sample points were allocated in each zone, and the classification results of the sample points were obtained. We also used other sampling methods, including a random sampling method (SRS), stratified sampling method (SS), and spatial sampling of the gray level co-occurrence matrix method (GLCM), to obtain the classification results of the sample points (2-m resolution). Five categories of ground objects in the same region were used as the ground truth data. We than calculated the overall accuracy, Kappa coefficient, producer accuracy, and user accuracy to estimate the accuracy of the classification results. The results showed that MNSS was the strictest inspection method as shown by the minimum value of accuracy. Moreover, MNSS overcame the shortcoming of SRS, which did not consider the spatial correlation of sample points, and overcame the shortcomings of SS and GLCM, which had redundant information between sample points. This paper proposes a novel sampling method for the accuracy assessment of classification results of remote sensing images.

scholarly journals Evaluation of soil salinity and sodicity using electromagnetic conductivity imaging 

2021 ◽  
Author(s):  
Mohammad Farzamian ◽  
Francisco José Martinez Moreno ◽  
Tiago B. Ramos ◽  
Nadia Castanheira ◽  
Ana Marta Paz ◽  
...  
Keyword(s):  
Soil Properties ◽  
Soil Salinity ◽  
Electromagnetic Induction ◽  
Lower Cost ◽  
Sustainable Use ◽  
Soil Sampling ◽  
Soil Electrical Conductivity ◽  
Sodic Soils ◽  
Non Invasive ◽  
Soil Exchange Complex

<p>In order to prevent further soil degradation, it is important to understand the processes controlling salinization. Salt related problems in soils can refer to an excess of soluble salts (saline soils), a dominance of exchangeable sodium in the soil exchange complex (sodic soils), or a mixture of both situations (saline-sodic soils). These categories are important because the impacts and management vary accordingly. Traditional soil sampling methods –which require boreholes for soil sampling and analysis– difficultly lead to a comprehensive answer to this problem. This is because they cover only small and localized sites and may not be representative of the soil properties at the management scales. Furthermore, they are highly time and work consuming, resulting in costly surveys. Geophysical techniques such as electromagnetic induction (EMI) provide enormous advantages compared to soil sampling because they allow for in-depth and non-invasive analysis, covering large areas in less time and at a lower cost.</p><p>EMI surveys were performed in several regions in Portugal with historic soil salinity and sodicity problems to evaluate the salinization risk. We inverted field apparent conductivity data (σ<sub>a</sub>) in order to obtain electromagnetic conductivity images (EMCI) of the real soil electrical conductivity (σ) in depth. We evaluated the potential of EMCI in the estimation of soil salinity, sodicity, and other soil properties over large areas across regions with a very different range of salinity and sodicity.</p><p> </p><p><strong>Acknowledgments</strong></p><p>This work was developed in the scope of SOIL4EVER “Sustainable use of soil and water for improving crops productivity in irrigated areas” project supported by FCT, grant no. PTDC/ASP-SOL/28796/2017.</p><p> </p>

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