An approach to correlate NDVI with soil colour for erosion process using NOAA/AVHRR data

Singh, Dharmendra; Herlin, Isabelle; Berroir, Jean-Paul; Silva, E.F; Meirelles, Margareth Simões Penello

doi:10.1016/s0273-1177(03)00468-x

Cited by 35 publications

(30 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, full coverage mapping of soils based on remote sensing data is difficult. Geostatistics and indirect correlations of soil properties with vegetation contribute to a solution (e.g., [5,62,63]), but require the development of new methods in order to retain the spatial detail that remote sensing data can offer.…”

Section: Discussion Of the Resultsmentioning

confidence: 99%

Barest Pixel Composite for Agricultural Areas Using Landsat Time Series

et al. 2017

View full text Add to dashboard Cite

Many soil remote sensing applications rely on narrow-band observations to exploit molecular absorption features. However, broadband sensors are invaluable for soil surveying, agriculture, land management and mineral exploration, amongst others. These sensors provide denser time series compared to high-resolution airborne imaging spectrometers and hold the potential of increasing the observable bare-soil area at the cost of spectral detail. The wealth of data coming along with these applications can be handled using cloud-based processing platforms such as Earth Engine. We present a method for identifying the least-vegetated observation, or so called barest pixel, in a dense time series between January 1985 and March 2017, based on Landsat 5, 7 and 8 observations. We derived a Barest Pixel Composite and Bare Soil Composite for the agricultural area of the Swiss Plateau. We analysed the available data over time and concluded that about five years of Landsat data were needed for a full-coverage composite (90% of the maximum bare soil area). Using the Swiss harmonised soil data, we derived soil properties (sand, silt, clay, and soil organic matter percentages) and discuss the contribution of these soil property maps to existing conventional and digital soil maps. Both products demonstrate the substantial potential of Landsat time series for digital soil mapping, as well as for land management applications and policy making.

show abstract

Section: Discussion Of the Resultsmentioning

confidence: 99%

Barest Pixel Composite for Agricultural Areas Using Landsat Time Series

et al. 2017

View full text Add to dashboard Cite

show abstract

“…The use of semi-automated image classification techniques such as vegetation indices (VIs) in particular have gained momentum in soil erosion research over the past few decades (Mathieu et al, 1997;Singh, 2004). Since its introduction in the 1970s by Rouse et al (1973), the Normalised Difference Vegetation Index (NDVI) has been used quite extensively in soil erosion-related research (Vaidyanathan et al, 2002;Li et al, 2010;Seutloali et al, 2016).…”

Section: South African Journal Ofmentioning

confidence: 99%

“…In this paper, satellite image-derived NDVI, SAVI, and SARVI are considered mainly because they are some of the simplest, cheapest, and quickest feature extraction techniques (Singh, 2004;South African Journal of Geomatics, Vol. 6.…”

Section: South African Journal Ofmentioning

confidence: 99%

Mapping soil erosion in a quaternary catchment in Eastern Cape using geographic information system and remote sensing

Phinzi¹,

Ngetar²

2017

SA J of Geomatics

View full text Add to dashboard Cite

show abstract

“…Numerous studies have investigated the relation between soil reflectance characteristics (e.g., soil color and soil brightness) and soil properties, particularly organic matter (OM), soil moisture, and texture [1,[20][21][22][23][24]. As proximal and remote sensing methods, diffuse reflectance spectroscopic techniques in the visible, near-and shortwave infra-red range were applied for OM measurements [25,26].…”

Section: Introductionmentioning

confidence: 99%

Organic Matter Modeling at the Landscape Scale Based on Multitemporal Soil Pattern Analysis Using RapidEye Data

et al. 2015

View full text Add to dashboard Cite

This study proposes the development of a landscape-scale multitemporal soil pattern analysis (MSPA) method for organic matter (OM) estimation using RapidEye time series data analysis and GIS spatial data modeling, which is based on the methodology of Blasch et al. The results demonstrate (i) the potential of MSPA to predict OM for single fields and field composites with varying geomorphological, topographical, and pedological backgrounds and (ii) the method conversion of MSPA from the field scale to the multifield landscape scale. For single fields, as well as for field composites, significant correlations between OM and the soil pattern detecting first standardized principal components were found. Thus, high-quality functional OM soil maps could be produced after excluding temporal effects by applying modified MSPA analysis steps. A regional OM prediction model was developed using four representative calibration test sites. The MSPA-method conversion was realized applying the transformation parameters of the soilpattern detection algorithm used at the four calibration test sites and the developed regional prediction model to a multi-field, multitemporal, bare soil image mosaic of all agrarian fields of the Demmin study area in Northeast Germany. Results modeled at the landscape scale were validated at an independent test site with a resulting prediction error of 1.4 OM-% for the main OM value range of the Demmin study area.

show abstract

An approach to correlate NDVI with soil colour for erosion process using NOAA/AVHRR data

Cited by 35 publications

References 10 publications

Barest Pixel Composite for Agricultural Areas Using Landsat Time Series

Barest Pixel Composite for Agricultural Areas Using Landsat Time Series

Mapping soil erosion in a quaternary catchment in Eastern Cape using geographic information system and remote sensing

Organic Matter Modeling at the Landscape Scale Based on Multitemporal Soil Pattern Analysis Using RapidEye Data

Contact Info

Product

Resources

About