Estimating Soil Moisture Conditions of the Greater Changbai Mountains by Land Surface Temperature and NDVI

Han, Yingjie; Wang, Yeqiao; Zhao, Yunsheng

doi:10.1109/tgrs.2010.2040830

Cited by 87 publications

(21 citation statements)

References 30 publications

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“…The GCM include a part of the northeast provinces of Heilongjiang, Jilin, and Liaoning in China and have the largest protected temperate forest in Northeast Asia with many rare animal and plant resources. This area has been a focal point of ecosystem and biodiversity research based on remote sensing applications [27]. The Greater Changbai Mountains (GCM) has a northeast-southwest orientation and extends in the region 38 • 46 -47 • 30 N latitude and 121 • 08 -134 • E longitude (Figure 1).…”

Section: Study Areamentioning

confidence: 99%

“…The abovementioned indices can be calculated from easily available satellite remote sensing data. Other researchers proposed the temperature-vegetation dryness index (TVDI) using the spatial relationship between the LST and NDVI based on the spectral reflectance of near-infrared (NIR) and red channels to indicate drought [26] and soil moisture conditions [27]. These indices take advantage of one or more aspects of droughts to reflect drought conditions; as a result, these indices have distinct characteristics that makes them suitable for different scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Droughts in the Greater Changbai Mountains Using Multiple Remote Sensing-Based Drought Indices

Han

Huang

et al. 2020

Remote Sensing

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Various drought indices have been developed to monitor drought conditions. Each index has typical characteristics that make it applicable to a specific environment. In this study, six popular drought indices, namely, precipitation condition index (PCI), temperature condition index (TCI), vegetation condition index (VCI), vegetation health index (VHI), scaled drought condition index (SDCI), and temperature–vegetation dryness index (TVDI), have been used to monitor droughts in the Greater Changbai Mountains(GCM) in recent years. The spatial pattern and temporal trend of droughts in this area in the period 2001–2018 were explored by calculating these indices from multi-source remote sensing data. Significant spatial–temporal variations were identified. The results of a slope analysis along with the F-statistic test showed that up to 20% of the study area showed a significant increasing or decreasing trend in drought. It was found that some drought indices cannot be explained by meteorological observations because of the time lag between meteorological drought and vegetation response. The drought condition and its changing pattern differ from various land cover types and indices, but the relative drought situation of different landforms is consistent among all indices. This work provides a basic reference for reasonably choosing drought indices for monitoring drought in the GCM to gain a better understanding of the ecosystem conditions and environment.

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Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monitoring Droughts in the Greater Changbai Mountains Using Multiple Remote Sensing-Based Drought Indices

Han

Huang

et al. 2020

Remote Sensing

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“…The index is proposed to demonstrate soil moisture detection and has been widely used in the drought monitoring. [57][58][59] MIMICS is proposed to depict the backscattering behavior of a forest based on radiative transfer theory. 26 Geometrically, this model divides the tree canopy into three components: crown layer, trunk, and underlying ground.…”

Section: Soil Moisture Estimations On Validation Pointsmentioning

confidence: 99%

Estimation of soil moisture using a vegetation scattering model in wheat fields

Tao

Wang

Chen

et al. 2019

J. Appl. Rem. Sens.

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We develop a vegetation scattering model to eliminate the effect of vegetation and surface roughness on the radar signal. The canopy water content is an important variable associated with the scattering effect of vegetation, and it can be calculated using the leaf area index, which is retrieved from PROSAILH optical model based on Landsat-8 images. The scattering model introduced the direct scattering contribution of underlying ground into the water cloud model. The experimental correlation length was replaced by a fitting parameter from C-band RADSARSAT-2 radar data to calculate the scattering contribution of underlying ground. Results demonstrate that the vegetation scattering model has a good performance in soil moisture retrieval with R 2 of 0.805 and root-mean-square error of 0.039 m 3 • m −3. The applicability and capability of the scattering model will provide the operational potential of C-band radar data for soil moisture retrieval in an agricultural region. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…Some studies (Gowarda, Xue, and Czajkowski 2002;Han, Wang, and Zhao 2010;Sandholt, Rasmussen, and Andersen 2002) considered that the M changes linearly with the NDVI/FVC in the feature space, as shown in Figure 3 (the blue line). The M changes from 0 to 1 at an interval of 0.2 along the direction of the arrow.…”

Section: Comparison Of Linear and Non-linear Interpolation Models In mentioning

confidence: 99%

Validation of a practical normalized soil moisture model with in situ measurements in humid and semi-arid regions

Zhang

Tang

et al. 2015

International Journal of Remote Sensing

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2015): Validation of a practical normalized soil moisture model with in situ measurements in humid and semi-arid regions, International Journal of Remote Sensing,Soil moisture is an important parameter that influences the exchange of water and energy fluxes between the land surface and the atmosphere. Through the simulation by a SoilVegetation-Atmosphere Transfer model, Carlson proposed the universal spatial information-based method to determine soil moisture that is insensitive to the initial atmospheric and surface conditions, net radiation, and atmospheric correction. In this study, a practical normalized soil moisture model is established to describe the relationship among the normalized soil moisture (M), the normalized land surface temperature (T*), and the fractional vegetation cover. The dry and wet points are determined using the surface energy balance principle, which has a robust physical basis. This method is applied to retrieve soil moisture for the Soil Moisture-Atmosphere Coupling Experiment campaign in the Walnut Creek watershed, which has a humid climate, and at the Linzestation, which has a semi-arid climate. The validation data are obtained on days of year (DOYs) 182 and 189 in 2002 in the humid region and on DOYs 148 and 180 in 2008 for the semi-arid region; these data collection days are coincident with the overpass of the Landsat Thematic Mapper/ Enhanced Thematic Mapper Plus. When the estimates are compared with the in situ measurements of soil water content, the root mean square error is approximately 0.10 m 3 m −3 with a bias of 0.05 m 3 m −3 for the humid region and 0.08 m 3 m −3 with a bias of 0.03 m 3 m −3 for the semi-arid region. These results demonstrate that the practical normalized soil moisture model is applicable in both humid and semi-arid regions.

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Estimating Soil Moisture Conditions of the Greater Changbai Mountains by Land Surface Temperature and NDVI

Cited by 87 publications

References 30 publications

Monitoring Droughts in the Greater Changbai Mountains Using Multiple Remote Sensing-Based Drought Indices

Monitoring Droughts in the Greater Changbai Mountains Using Multiple Remote Sensing-Based Drought Indices

Estimation of soil moisture using a vegetation scattering model in wheat fields

Validation of a practical normalized soil moisture model with in situ measurements in humid and semi-arid regions

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