Estimation of available moisture holding capacity of upland soils using landsat TM data

Hatanaka, Takeshi; Nishimune, A.; Nira, Rikiya; Fukuhara, Minoru

doi:10.1080/00380768.1995.10419619

Cited by 14 publications

(9 citation statements)

References 4 publications

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“…The relationship between SSCCs and satellite image data was analyzed by means of linear, logarithmic, exponential, and power regression for reflectance at each wavelength (green, red, near-infrared, and mid-infrared). In our study area, visible and near-infrared reflectance are strongly and negatively correlated with SSCCs for all parent materials (Hatanaka et al 1989). However, soil reflectance at visible and near-infrared wavelengths depends on the parent material (Henderson et al 1992).…”

Section: Soil Surveymentioning

confidence: 68%

“…Remote sensing technologies are labor-saving methods that have been used previously to estimate soil carbon. For example, soil carbon has been estimated from visible to near-infrared reflectance (mainly 450-900 nm) (Baumgardner et al 1985;Hatanaka et al 1989;Bhatti et al 1991;Chen et al 2000;Fox and Sabbagh 2002;Niwa et al 2004), and other studies have estimated soil carbon by using reflectance in various other wavelength ranges (Ben-Dor et al 2002, 400-2500Kooistra et al 2003Kooistra et al , 400-2500Chen et al 2008, 450-12 000 nm;Gomez et al 2008Gomez et al , 400-2500. However, these studies mainly aimed to estimate soil carbon concentrations.…”

Section: Introductionmentioning

confidence: 99%

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Estimating soil carbon stocks in an upland area of Tokachi District, Hokkaido, Japan, by satellite remote sensing

Niwa

Yokobori

Hongo

et al. 2011

Soil Science and Plant Nutrition

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Soil carbon stocks (SCSs) in an upland area of Tokachi District, Hokkaido, Japan, were estimated by satellite remote sensing and a soil survey. The soil parent materials in the studied area were alluvial deposits, volcanic ash plus alluvial deposits, and volcanic ash. Surface soil carbon concentrations (SSCCs) were negatively correlated with satellite image data (green, red, and near-infrared reflectance) for each parent material. The highest correlations between reflectance and SSCCs were obtained from red wavelength reflectance for alluvial deposits (r ¼-0.82, p < 0.01) and volcanic ash plus alluvial deposits (r ¼-0.91, p < 0.01), and from near-infrared reflectance for volcanic ash (r ¼-0.90, p < 0.01). We generated an SSCC map of the study area using the regression equations and satellite reflectance data. The soil survey results showed that SSCCs were positively correlated with SCSs in the 0-30 cm depth interval for each parent material (best-fit regressions: alluvial deposits, r ¼ 0.97; volcanic ash plus alluvial deposits, r ¼ 0.97; volcanic ash, r ¼ 0.97), and they were also positively correlated with SCSs in the 30-95 cm depth interval for volcanic ash (r ¼ 0.94). We were therefore able to generate a map of estimated SCSs from the SSCC map and the regression equations developed between SSCCs and SCSs. The estimated and measured SCSs in both depth intervals showed an almost 1:1 relationship, with root-mean-square errors of 19.5 (0-30 cm) and 28.6 Mg carbon (C) ha-1 (30-95 cm). According to the SCS maps, SCSs at 0-30 cm depth in areas of alluvial deposits, volcanic ash plus alluvial deposits, and volcanic ash were mostly 50-150, 100-200, and 50-250 Mg C ha-1 , respectively, whereas at 30-95 cm depth in the volcanic ash area they were mostly less than 250 Mg C ha-1 .

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Section: Soil Surveymentioning

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Estimating soil carbon stocks in an upland area of Tokachi District, Hokkaido, Japan, by satellite remote sensing

Niwa

Yokobori

Hongo

et al. 2011

Soil Science and Plant Nutrition

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“…The commonly used methods consist of spectral reflectivity method [9,10], temperature vegetation dryness index [11], thermal inertia method [12,13], apparent thermal inertia method [14], crop shortage water index method [15,16], vegetation condition index [17], etc.. But the poor ability of the visible-infrared band in penetrating clouds, vegetation and soil limits the application of the above-mentioned methods.…”

Section: Introductionmentioning

confidence: 99%

Use of RADARSAT-2 data to retrieve soil moisture over bare soil agricultural fields

Yin

Jiang

Zhan

2013

2013 21st International Conference on Geoinformatics

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Soil moisture estimation is important to agricultural production. The existing inversion models based on SAR (Synthetic Aperture Radar) images are available for non-periodic surfaces but not available for periodic surfaces yet. The backscattering coefficients from periodic surfaces are not only related to SAR sensor parameters and soil surface parameters, but also related to the azimuth angle  of crop rows. The authors analyzed the response behavior to an azimuth angle in different polarization modes. Cross-polarization (VH) showed no dependence on  , yet like-polarization (HH or VV) showed sensitive behavior to  , and then the authors provided the response functions. Furthermore, the authors built a parameter to remove the influence of  and established an inversion model of soil moisture for periodic surfaces. Lastly, they proved the validity by field data.

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“…Additionally, recent Landsat satellites (TM, ETM+, and OLI) provide adequate spatial and temporal resolution data for the detection of the seasonal variation of objects on the land surface at large scales, free of charge. For soil moisture monitoring purposes, Landsat 5 and 7 have been rarely used to estimate SMC directly from their spectral bands or band ratios [9,[12][13][14], but they are more widely estimated through the use of a combination of vegetation-based indices, such as the Normalized Difference Vegetation Index (NDVI), the Temperature Vegetation Dryness Index (TVDI), with the Land Surface Temperature (LST), retrieved from these sensors' signals [15][16][17][18]. The newest Landsat, Landsat 8 (L8), which was launched recently in 2013, was also explored for SMC estimation [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Effective Band Ratio of Landsat 8 Images Based on VNIR-SWIR Reflectance Spectra of Topsoils for Soil Moisture Mapping in a Tropical Region

vThi

Dang

et al. 2019

Remote Sensing

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Effective mapping and monitoring of soil moisture content (SMC) in space and time is an expected application of remote sensing for agricultural development and drought mitigation, particularly in the context of global climate change impact, given that agricultural drought is occurring more frequently and severely worldwide. This study aims to develop a regional algorithm for estimating SMC by using Landsat 8 (L8) imagery, based on analyses of the response of soil reflectance, by corresponding L8 bands with the change of SMC from dry to saturated states, in all 103 soil samples taken in the central region of Vietnam. The L8 spectral band ratio of the near-infrared band (NIR: 850–880 nm, band 5) versus the short-wave infrared 2 band (SWIR2: 2110 to 2290 nm, band 7) shows the strongest correlation to SMC by a logarithm function (R2 = 0.73 and the root mean square error, RMSE ~ 12%) demonstrating the high applicability of this band ratio for estimating SMC. The resultant maps of SMC estimated from the L8 images were acquired over the northern part of the Central Highlands of Vietnam in March 2015 and March 2016 showed an agreement with the pattern of severe droughts that occurred in the region. Further discussions on the relationship between the estimated SMC and the satellite-based retrieved drought index, the Normal Different Drought Index, from the L8 image acquired in March 2016, showed a strong correlation between these two variables within an area with less than 20% dense vegetation (R2 = 0.78 to 0.95), and co-confirms the bad effect of drought on almost all areas of the northern part of the Central Highlands of Vietnam. Directly estimating SMC from L8 imagery provides more information for irrigation management and better drought mitigation than by using the remotely sensed drought index. Further investigations on various soil types and optical sensors (i.e., Sentinel 2A, 2B) need to be carried out, to extend and promote the applicability of the prosed algorithm, towards better serving agricultural management and drought mitigation.

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Estimation of available moisture holding capacity of upland soils using landsat TM data

Cited by 14 publications

References 4 publications

Estimating soil carbon stocks in an upland area of Tokachi District, Hokkaido, Japan, by satellite remote sensing

Estimating soil carbon stocks in an upland area of Tokachi District, Hokkaido, Japan, by satellite remote sensing

Use of RADARSAT-2 data to retrieve soil moisture over bare soil agricultural fields

Effective Band Ratio of Landsat 8 Images Based on VNIR-SWIR Reflectance Spectra of Topsoils for Soil Moisture Mapping in a Tropical Region

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