Towards decadal soil salinity mapping using Landsat time series data

Fan, Xingwang; Weng, Yongling; Tao, Jinmei

doi:10.1016/j.jag.2016.05.009

Cited by 36 publications

(16 citation statements)

References 66 publications

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“…Therefore, the spectral band conversions can be improved for NDVI conversions. Aside from linear regression between analogous spectral bands, Gao and Kaufman [47] and Fan et al [56] use two bands for conversion. This method is useful for analogous bands that show major SRF differences, such as narrow MODIS SRFs vs. wide AVHRRs SRFs.…”

Section: Implications For Spectral Band Conversionsmentioning

confidence: 99%

Using a MODIS Index to Quantify MODIS-AVHRRs Spectral Differences in the Visible Band

Fan

Liu

2018

Remote Sensing

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Spectral band differences are a critical issue for progressing into an integrated earth observation framework and in particular, in sensor intercalibration. The differences are currently normalized using spectral band adjustment factor (SBAF) that is generated from hyperspectral data. In this context, the current study proposes a method for calculating moderate-resolution imaging spectroradiometer (MODIS)-advanced very high resolution radiometers (AVHRRs) SBAF in the visible band, using the MODIS surface reflectance data. The method involves a uniform ratio index calculated using the MODIS 552-nm and 645-nm bands, and a sensor-specific quadratic equation, producing SBAF data at 500-m spatial resolution. The calculated SBAFs are in good agreement at site scale with literature reported data (relative error < 1.0%), and at local scale with Hyperion-derived data (total uncertainty ≈ 0.001), and significantly improve MODIS-AVHRR surface reflectance data consistency in the visible band (better than 1.0% reflectance units). The calculation is more sensitive to atmospheric effects over the vegetated areas. At global scale, MODIS-AVHRRs SBAFs are generally large (>1.0) over densely vegetated areas and extremely low over deserts and barren lands (0.96-0.98), indicative of large MODIS-AVHRRs differences. Deserts show temporally stable SBAF values, while still suffer from intra-annual BRDF effects and short-term cloud contamination. By means of daily MODIS data, the proposed method can produce ongoing SBAF data at a spatial scale that is comparable to AVHRRs. It increases the sampling of MODIS-AVHRRs image pairs for intercalibration, and offers insight into spectral band conversion, finally contributing to an integrated earth observation at moderate spatial resolutions.

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Section: Implications For Spectral Band Conversionsmentioning

confidence: 99%

Using a MODIS Index to Quantify MODIS-AVHRRs Spectral Differences in the Visible Band

Fan

Liu

2018

Remote Sensing

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“…Different indices of salinity and vegetation were created by combining spectral bands to identify salt-affected regions and they have been assessed with different results, as salinity level and extent of the vegetation cover differs for each case study, choosing and using a similar index may not attain the best outcome in all situations [8]. In most cases, multiple bands have been combined into one index, which is more sensitive to soil salinity than a single band [9,10]. The spectral absorption characteristics are related to the amount of salinization.…”

Section: Introductionmentioning

confidence: 99%

“…The Terrian environment plays an important role in the distribution and redistribution of salt. Although high and steep terrain is conducive to salt migration, low-lying terrain is lead to salt accumulation [10]. In Digital Soil Mapping (DSM), collecting auxiliary variables to predict soil salinity parameters is a prerequisite for improving the prediction accuracy of spatial distribution modeling, which is composed of RS data, DEM and their derivatives (e.g.…”

Section: Introductionmentioning

confidence: 99%

Modeling And Mapping of Soil Salinity And Alkalinity Using Remote Sensing Data And Topographic Factors

Shahrayini

Noroozi

2021

Preprint

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Soil salinity and alkalinity seriously threaten crop production, soil productivity and sustainable agriculture, especially in arid and semi-arid areas, leading to land degradation, therefore, spatial distribution of these parameters are really important for successful management of such areas. The surface soil salinity and sodium adsorption ratio (SAR) have been modeled in this article. Auxiliary data were terrain attributes derived from digital elevation model (DEM), remote sensing spectral bands, and indices of vegetation and salinity derived from Landsat 8 OLI satellite. In total, 118 soil samples were collected from depth of 0-15 cm in homogenous units at Doviraj plain in the southern part of Ilam province, western Iran. Saturated electrical conductivity (ECe), SAR and other soil properties were analyzed and calculated. To model ECe and SAR parameters with the auxiliary data, stepwise multi linear regression (SMLR) and random forest (RF) regression were applied. The highest accuracy were obtained through RF model with validation coefficient of determination (R2val) =0.82 and 0.83 and validation root mean square error (RMSEval)=7.40 dS/m and 11.20 for ECe and SAR respectively. Furthermore, results indicated that strongest influence on the prediction of soil salinity followed by Band10, principal component analysis (PC3), Vertical Distance to Channel Network (VDCN) and Analytical Hill Shading (AH). Also, Band10, Band11, Flow Accumulation (FA) and Topographic Wetness Index (TWI) were the important covariate in alkalinity prediction through RF model. Finally, it is suggested that similar techniques can be used to map and monitor soil salinity and alkalinity in other parts of arid regions.

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“…Moreover, climate change is exacerbating the deterioration of arable land in arid and semi-arid regions. Especially over dry land areas, soil salinization is more likely to occur because of harsh climatic conditions and irrational irrigation practices (Fan, X et al, 2016).…”

Section: -Introductionmentioning

confidence: 99%

تأثیر الظروف المناخیة السائدة على کشف ورصد ملوحة التربة فی واحة الخارجة على أساس القمر الصناعی لاندسات 8 Oli والسلسلة الزمنیة المناخیة

إسماعيل¹

2020

مجلة البحث العلمی فی الآداب

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Towards decadal soil salinity mapping using Landsat time series data

Cited by 36 publications

References 66 publications

Using a MODIS Index to Quantify MODIS-AVHRRs Spectral Differences in the Visible Band

Using a MODIS Index to Quantify MODIS-AVHRRs Spectral Differences in the Visible Band

Modeling And Mapping of Soil Salinity And Alkalinity Using Remote Sensing Data And Topographic Factors

تأثیر الظروف المناخیة السائدة على کشف ورصد ملوحة التربة فی واحة الخارجة على أساس القمر الصناعی لاندسات 8 Oli والسلسلة الزمنیة المناخیة

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