Effect of errors in positioning the sampling points on the assessment of the relationships between the remote sensing data and the soil properties

2013 ◽  
Vol 46 (5) ◽  
pp. 484-490 ◽  
Author(s):  
V. P. Samsonova ◽  
Yu. L. Meshalkina ◽  
Yu. N. Blagoveshchenskii
Keyword(s):  
Remote Sensing ◽  
Soil Properties ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Sampling Points

Effect of Slope Mesorelief on the Spatial Variability of Soil Properties and Vegetation Index Based on Remote Sensing Data

2019 ◽  
Vol 55 (9) ◽  
pp. 1329-1337
Author(s):  
N. V. Gopp ◽  
T. V. Nechaeva ◽  
O. A. Savenkov ◽  
N. V. Smirnova ◽  
V. V. Smirnov
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Soil Properties ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Effect of Open Soil Surface Patterns on Soil Detectability Based on Optical Remote Sensing Data

Proceedings ◽  
2018 ◽  
Vol 2 (7) ◽  
pp. 357 ◽  
Author(s):  
Elena Prudnikova ◽  
Igor Savin
Keyword(s):  
Remote Sensing ◽  
Soil Properties ◽  
Remote Sensing Data ◽  
Optical Remote Sensing ◽  
Spectral Variation ◽  
Least Squares Regression ◽  
Arable Soils ◽  
Sensing Data ◽  
Surface Patterns ◽  
Soil Reflectance

Arable soils are subjected to the altering influence of agricultural and natural processes determining surface feedback patterns therefore affecting their ability to reflect light. However, remote soil mapping and monitoring usually ignore information on surface state at the time of data acquisition. Conducted research demonstrates the contribution of surface feedback dynamics to soil reflectance and its relationship with soil properties. Analysis of variance showed that the destruction surface patterns accounts for 71% of spectral variation. The effect of surface smoothing on the relationships between soil reflectance and its properties varies. In the case of organic matter and medium and coarse sand particles, correlation decreases with the removement of surface structure. For particles of fine sand and coarse silt, grinding changes spectral areas of high correlation. Partial least squares regression models also demonstrated variations in complexity, R2cv and RMSEPcv. Field dynamics of surface feedback patterns of arable soils causes 22–46% of soil spectral variations depending on the growing season and soil type. The directions and areas of spectral changes seem to be soil-specific. Therefore, surface feedback patterns should be considered when modelling soil properties on the basis of optical remote sensing data to ensure reliable and reproducible results.

Spatial Prediction of Surface Soil Properties Using Terrain and Remote Sensing Data

2008 ◽  
Vol 8 (6) ◽  
pp. 1000-1006 ◽  
Author(s):  
Jafar Yasrebi ◽  
Mahboub Saffari ◽  
Hamed Fathi ◽  
Mostafa Emadi ◽  
Majid Bagherneja ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Properties ◽  
Surface Soil ◽  
Remote Sensing Data ◽  
Spatial Prediction ◽  
Sensing Data ◽  
Surface Soil Properties

Fusion of Soil and Remote Sensing Data to Model Soil Properties

2015 ◽  
pp. 1-109 ◽  
Author(s):  
Sabine Grunwald ◽  
Gustavo M. Vasques ◽  
Rosanna G. Rivero
Keyword(s):  
Remote Sensing ◽  
Soil Properties ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Model Soil

USING REMOTE SENSING DATA TO EVALUATE SURFACE SOIL PROPERTIES IN ALABAMA ULTISOLS

Soil Science ◽  
2005 ◽  
Vol 170 (12) ◽  
pp. 954-968 ◽  
Author(s):  
Dana G. Sullivan ◽  
Joey N. Shaw ◽  
Doug Rickman ◽  
Paul L. Mask ◽  
Jeffrey C. Luvall
Keyword(s):  
Remote Sensing ◽  
Soil Properties ◽  
Surface Soil ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Surface Soil Properties
Sign in / Sign up

Export Citation Format

Share Document