Methodology for Bare Soil Detection and Discrimination by Landsat TM Image

Demattê, José Alexandre Melo; Huete, Alfredo; Ferreira, Laerte Guimarães; Nanni, Marcos Rafael; Alves, Marcelo Cardoso; Fiorio, Peterson Ricardo

doi:10.2174/1875413901002010024

Cited by 22 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, this slope and intercept are also close to those from the simulated SL using quantile regression methods (fixed and floating tau) for the study area, especially at the end of growing season (i.e., Table 4, 2013/08/10). Indeed, these data are in agreement with [56,57], for whom soils with different textures appear at different positions in the SL extracted from Landsat TM (Figure 10b,c). The pixels in Figure 10 are taken from bare soil, and it is interesting to note that they are spread all over the soil line, which indicates that the pixels have different textures.…”

Section: Remote Sens 2016 8 738supporting

confidence: 87%

“…The results of Landsat 8 OLI for the barley field shown in Figure 10a and the results of field spectrometry shown in Figure 6 confirms this fact, with fewer points on the lower left of the scatter plot. However, Figure 10a also shows that most of the points are located in the middle, which represents loamy soil, according to the Demattê et al [56] pattern and Figure 10c, but our granulometric analysis shows that the amount of silt is approximately 17% and the barley soil is mostly sandy. This result might be because our granulometric analysis was based on a few samples that were taken from a small area within the fields or could be attributed to a direct response to the variability in soil moisture.…”

Section: Remote Sens 2016 8 738mentioning

confidence: 63%

“…As observed from Figure 10d [58], differences in soil moisture cause a shift of the values along the SL with moister values in the lower portion and drier values in the higher red and NIR regions. The Demattê et al, [56] pattern in Figure 10b also shows the scattered sandy soil points along the SL due to spread albedos. The differences in albedo can be attributed again to differences in soil moisture.…”

Section: Remote Sens 2016 8 738mentioning

confidence: 79%

“…The pixels at the top are sandier than the ones at the bottom. According to Demattê et al, [56] the pattern (i.e., Figure 10b) values on the lower left of the scatter plot (i.e., black circles) represent clay soil. The results of granulometric analysis for barley soil in Figure 7 (bottom table) confirm that barley soil has a notably low percentage of clay (approximately 2%).…”

Section: Remote Sens 2016 8 738mentioning

confidence: 90%

See 3 more Smart Citations

A New Concept of Soil Line Retrieval from Landsat 8 Images for Estimating Plant Biophysical Parameters

Ahmadian

Demattê

et al. 2016

Remote Sensing

View full text Add to dashboard Cite

Extraction of vegetation information from remotely sensed images has remained a long-term challenge due to the influence of soil background. To reduce this effect, the slope and intercept of the soil line (SL) should be known to calculate SL-related vegetation indices (VIs). These VIs can be used to estimate the biophysical parameters of agricultural crops. However, it is a difficult task to retrieve the SL parameters under the vegetation canopy. A feasible method for retrieving these parameters involves extracting the bottom boundary line in two-dimensional spectral spaces (i.e., red and near-infrared bands). In this study, the slope and intercept of the SL was extracted from Landsat 8 OLI images of a test site in northeastern Germany. Different statistical methods, including the Red-NIR min method, quantile regression method (using a floating tau with the smallest p-value), and a new approach proposed in this paper using a fixed quantile tau known as the diffuse non-interceptance (DIFN) value, were applied to retrieve the SL parameters. The DIFN value describes the amount of light visible below the canopy that reaches the soil surface. Therefore, this value can be used as a threshold for retrieving the bottom soil line. The simulated SLs were compared with actual ones extracted from ground truth data, as recorded by a handheld spectrometer, and were also compared with the SL retrieved from bare soil pixels of the Landsat 8 image collected after harvest. Subsequently, the SL parameters were used to separately estimate the dry biomasses of winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.) at the local and field scales using different SL-related vegetation indices. The SL can be retrieved more accurately at the local scale compared with the field scale, and its simulation can be critical in the field due to significant differences from the actual SL. Moreover, the slope and intercept of the simulated SLs found using the floating and fixed quantile tau (slope ≈ 1.1 and intercept ≈ 0.05) show better agreement with the actual SL parameters (slope ≈ 1.2 and intercept ≈ 0.03) in the late growing stages (i.e., end of ripening and senescence stages) of crops. The slope and intercept of the soil line extracted from bare soil pixels of the Landsat 8 OLI data after harvest (slope = 1.3, intercept = 0.03, and R 2 = 0.94) are similar to those of the simulated SL. The correlation coefficient (R 2 ) of the simulated SLs are greater than 0.97 during different growing stage and all of the SL parameters are statistically significant (p < 0.05) at the local scale. The results also imply the need for different vegetation indices to best retrieve the crop biomass depending on the growing stage, but relatively small differences in performances were observed in this study.

show abstract

Section: Remote Sens 2016 8 738supporting

confidence: 87%

Section: Remote Sens 2016 8 738mentioning

confidence: 63%

Section: Remote Sens 2016 8 738mentioning

confidence: 79%

Section: Remote Sens 2016 8 738mentioning

confidence: 90%

See 2 more Smart Citations

A New Concept of Soil Line Retrieval from Landsat 8 Images for Estimating Plant Biophysical Parameters

Ahmadian

Demattê

et al. 2016

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Over the years, novel methodologies related to bare soil exposure detection have been proposed, progressing into multi-temporal methodologies for higher coverage of bare topsoil [13,[26][27][28][29][30]. In agricultural areas, with crop rotations or bare/ vegetated states alternation, multi-temporal images present a solution to increase the surface of detected bare soil [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Bare Soil Occurrence in Arable Croplands for Remote Sensing Topsoil Applications

et al. 2021

View full text Add to dashboard Cite

A better comprehension of soil properties and processes permits a progress in agricultural management effectiveness, together with a diminution of environmental damage and more beneficial use of resources. This research investigated the usage of multispectral (Sentinel-2 MSI) satellite data at the farm/regional level, for the identification of agronomic bare soil presence, utilizing bands of the spectral range from visible to shortwave infrared. The research purpose was to assess the frequency of cloud-free bare soil time-series images available during the year in typical agricultural areas, needed for the development of digital soil mapping (DSM) approaches for agricultural applications, using hyperspectral satellite missions such as current PRISMA and the planned EnMAP or CHIME. The research exploited the Google Earth Engine platform, by processing all available cloud-free Sentinel-2 images throughout a time span of four years. Two main results were obtained: (i) bare soil frequency, indicating where and when a pixel (or an agricultural field) was detected as bare surface in three representative agricultural areas of Italy, and (ii) a temporal sensitivity analysis, providing the acquisition frequency of useful bare soil images applicable for the retrieval of soil variables of interest. It was shown that, in order to provide for an effective agricultural soil monitoring capability, a revisit frequency in the range of five to seven days is required, which is less than the planned specifications e.g., of PRISMA or CHIME hyperspectral missions, but could be addressed by combining data from the two sensors.

show abstract

Dynamic evaluation of cropland degradation risk by combining multi-temporal remote sensing and geographical data in the Black Soil Region of Jilin Province, China

Shi

Xing

et al. 2023

Applied Geography

View full text Add to dashboard Cite

Methodology for Bare Soil Detection and Discrimination by Landsat TM Image

Cited by 22 publications

References 0 publications

A New Concept of Soil Line Retrieval from Landsat 8 Images for Estimating Plant Biophysical Parameters

A New Concept of Soil Line Retrieval from Landsat 8 Images for Estimating Plant Biophysical Parameters

An Analysis of Bare Soil Occurrence in Arable Croplands for Remote Sensing Topsoil Applications

Dynamic evaluation of cropland degradation risk by combining multi-temporal remote sensing and geographical data in the Black Soil Region of Jilin Province, China

Contact Info

Product

Resources

About