Soil moisture estimation in a semiarid rangeland using ERS-2 and TM imagery

Wang, Cuizhen; Qi, Jiaguo; Moran, Susan; Marsett, Robin

doi:10.1016/j.rse.2003.12.001

Cited by 69 publications

(44 citation statements)

References 23 publications

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“…Wang et al, 2004, Sonobe et al, 2007. Then the reduction of roughness effect on backscattering coefficient was achieved.…”

Section: Soil Moisture Estimation Modelmentioning

confidence: 99%

“…The main challenge in soil moisture estimation with SAR HH polarization data is to reduce or remove surface roughness effects. Wang et al (2004) and Sonobe et al (2007) developed an approach to reduce the surface roughness effect using the temporal difference backscattering coefficient under the hypothesis that the variation of soil moisture in their study area is minimal. Furthermore, Shoshany et al (2000) developed the Normalized radar Backscatter soil Moisture Index (NBMI), which requires two backscatter coefficients at different times: a backscatter coefficient at volumetric soil moisture near 0% is used as one of them.…”

Section: Introductionmentioning

confidence: 99%

“…The objectives of this study are to examine whether the elevation data at 1.4 m intervals can be used as roughness data for Lband SAR data, to extend the approach developed by Wang et al (2004) and Sonobe et al (2007) for application to fields in Japan and to develop an approach for mapping soil moisture distribution with an error of less than 5%, which is often assumed to be an acceptable error (e.g. Oldak et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Soil Moisture for Bare Soil Fields Using ALOS/PALSAR HH Polarization Data

Sonobe

Tani

Wang

et al. 2008

Agricultural Information Research

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Soil moisture is important information for agricultural fields in which erosion of upper soil layers depends upon the soil moisture and in which the yield depends on soil water contents during sowing, growing, and harvest periods. Although many studies have estimated moisture in bare soil fields using synthetic aperture radar (SAR) imaging, few models are useful to estimate volumetric soil moisture for wide areas because of requests for detailed roughness data. This study is intended to estimate soil moisture in bare soil fields using ALOS/PALSAR imaging. In the active microwave domain, the measured signal (backscattering coefficient) over bare soil depends on the soil moisture and surface roughness. Reducing surface roughness effects on backscattering coefficients is the main challenge confronting soil moisture estimation using singlefrequency, single-polarization SAR data. First, we evaluated the relationships between the backscattering coefficient and RMS (Root Mean Square) height, an index of roughness, which is calculated with elevation data at 1.4-m intervals. Results showed that backscattering coefficients were correlated with RMS height. Consequently, backscattering in conditions with no volumetric soil moisture variation in the study area was simulated. The effect of roughness was reduced by subtracting the backscattering coefficients simulated using our approach from those of all obtained PALSAR images. Furthermore, we developed a linear approach using the regression line between the backscattering coefficient, which reduced the effects of roughness (Δσ o ), and measured volumetric soil moisture values. The approach developed in this study estimates soil moisture with 4.4% RMSE for validation data that were not used to produce the model.

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“…Wang et al, 2004, Sonobe et al, 2007. Then the reduction of roughness effect on backscattering coefficient was achieved.…”

Section: Soil Moisture Estimation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Soil Moisture for Bare Soil Fields Using ALOS/PALSAR HH Polarization Data

Sonobe

Tani

Wang

et al. 2008

Agricultural Information Research

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“…The second group have attempted to use remotely sensed thermal and LC data to improve atmospheric models for simulation of land surface parameters [7,[10][11][12]. The third group have used remotely sensed LST to study the near-surface T a [13][14][15] or surface soil moisture [16,17]. Some of these studies have related LC indirectly to T a looking at LST as the intermediate link [18].…”

Section: Introductionmentioning

confidence: 99%

“…LST itself is used to derive other surface parameters, such as the near-surface soil moisture (SM). The near-surface SM is a function of soil temperature [10], therefore, remotely sensed LST is widely used to study it under different soil conditions (e.g., [16,17]). Each LC type has its own unique thermal properties with daily cycles of heating and cooling, hence, the near-surface SM derivations can also be affected by LC types of the surface.…”

Section: Introductionmentioning

confidence: 99%

Analysis of MODIS LST Compared with WRF Model and in situ Data over the Waimakariri River Basin, Canterbury, New Zealand

2012

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In this study we examine the relationship between remotely sensed, in situ and modelled land surface temperature (LST) over a heterogeneous land-cover (LC) enclosed in alpine terrain. This relationship can help to understand to what extent the remotely sensed data can be used to improve model simulations of land surface parameters such as LST in mountainous areas. LST from the MODerate resolution Imaging Spectro-radiometer (MODIS), the modelled surface skin temperature by the Weather Research and Forecasting (WRF) mesoscale numerical model and the in situ measurements of surface temperature are used in the analysis. The test-site is located in a mountain valley in the Southern Alps of New Zealand. Geospatial analysis in GIS is used to relate pixels, grid-cells and points from the MODIS LST, model simulations and the in situ data, respectively. Differences between LST from MODIS, the WRF model and the in situ data are presented with respect to surface LC at different times of day. Initial results from regression analysis of the three datasets showed a goodness of fit R

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Impacts of Arctic Climate and Land Use Changes on Reindeer Pastoralism: Indigenous Knowledge and Remote Sensing

Maynard

Oskal

Turi

et al. 2010

Eurasian Arctic Land Cover and Land Use in a Changing Climate

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Soil moisture estimation in a semiarid rangeland using ERS-2 and TM imagery

Cited by 69 publications

References 23 publications

Estimation of Soil Moisture for Bare Soil Fields Using ALOS/PALSAR HH Polarization Data

Estimation of Soil Moisture for Bare Soil Fields Using ALOS/PALSAR HH Polarization Data

Analysis of MODIS LST Compared with WRF Model and in situ Data over the Waimakariri River Basin, Canterbury, New Zealand

Impacts of Arctic Climate and Land Use Changes on Reindeer Pastoralism: Indigenous Knowledge and Remote Sensing

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