Improved spatial resolution in soil moisture retrieval at arid mining area using apparent thermal inertia

Lei, Shaogang; Bian, Zhengfu; Daniels, John L.; Liu, Donglie

doi:10.1016/s1003-6326(14)63265-9

Cited by 26 publications

(16 citation statements)

References 11 publications

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“…It should be noted that about 70% of the annual total precipitation occurs in August and September. The primary soil type is sand and the area is geographically characterized by an aeolian landform with sparse vegetation (Lei et al, 2010(Lei et al, , 2014.…”

Section: Study Areamentioning

confidence: 99%

“…However, these sensors have a moderate spatial resolution (1 km). The Landsat TM/OLI images have higher spatial resolution (30 m) than the MODIS and NOAA/AVHRR images, but they cannot provide the day-night soil temperature difference (Lei et al, 2014). To solve this problem, we developed an improved thermal inertia model by integrating the land surface temperature derived from the TM/OLI images and a simple sine model to obtain the soil temperature difference.…”

Section: Improved Thermal Inertia Modelmentioning

confidence: 99%

“…One specific issue of this model is to obtain the soil temperature difference with high spatial resolution. Only few satellite sensors can provide the diurnal soil temperature difference, e.g., MODIS sensor and NOAA/AVHRR sensor (Lei et al, 2014). However, the deficiency of MODIS sensor and NOAA/AVHRR sensor is that both soil temperature difference and soil moisture retrieved from those images are at a relatively coarse spatial resolution (1 km).…”

Section: Introductionmentioning

confidence: 99%

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Investigating spatial and temporal variations of soil moisture content in an arid mining area using an improved thermal inertia model

2017

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Mining operations can usually lead to environmental deteriorations. Underground mining activities could cause an extensive decrease in groundwater level and thus a dramatic variation in soil moisture content (SMC). In this study, the spatial and temporal variations of SMC from 2001 to 2015 at two spatial scales (i.e., the Shendong coal mining area and the Daliuta Coal Mine) were analyzed using an improved thermal inertia model with a long-term series of Landsat TM/OLI (TM=Thematic Mapper and OLI=Operational Land Imager) data. Our results show that at large spatial scale (the Shendong coal mining area), underground mining activities had insignificant negative impacts on SMC and that at small spatial scale (the Daliuta Coal Mine), underground mining activities had significant negative impacts on SMC. Trend analysis of SMC demonstrated that areas with decreasing trend of SMC were mainly distributed in the mined area, indicating that underground mining is a primary cause for the drying trend in the mining region in this arid environment.

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Section: Study Areamentioning

confidence: 99%

Section: Improved Thermal Inertia Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigating spatial and temporal variations of soil moisture content in an arid mining area using an improved thermal inertia model

2017

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“…Soil thermal inertia (TI) is a factor that reflects the temperature change of soil surface and is highly correlated with SM [17,18]. Based on this principle, simplified TI, namely ATI, is routinely used to estimate the SM of bare and less-vegetated soil [19][20][21][22][23][24][25][26]. There are no strict limitations on the number and time of day of LST observations using the ATI-based model comparing to other methods [27].…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture Estimation for the Chinese Loess Plateau Using MODIS-derived ATI and TVDI

Yuan

Zhang

et al. 2020

Remote Sensing

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Timely and effective estimation and monitoring of soil moisture (SM) provides not only an understanding of regional SM status for agricultural management or potential drought but also a basis for characterizing water and energy exchange. The apparent thermal inertia (ATI) and Temperature Vegetation Dryness Index (TVDI) are two widely used indices to reflect SM from remote sensing data. While the ATI-based model is routinely used to estimate the SM of bare soil and sparsely vegetated areas, the TVDI-based model is more suitable for areas with dense vegetation coverage. In this study, we present an iteration procedure that allows us to identify optimal Normalized Difference Vegetation Index (NDVI) thresholds for subregions and estimate their relative soil moisture (RSM) using three models (the ATI-based model, the TVDI-based model, and the ATI/TVDI joint model) from 1 January to 31 December 2017, in the Chinese Loess Plateau. The initial NDVI (NDVI0) was first introduced to obtain TVDI value and two other thresholds of NDVIATI and NDVITVDI were designed for dividing the whole area into three subregions (the ATI subregion, the TVDI subregion, and the ATI/TVDI subregion). The NDVI values corresponding to maximum R-values (correlation coefficient) between estimated RSM and in situ RSM measurements were chosen as optimal NDVI thresholds after performing as high as 48,620 iterations with 10 rounds of 10-fold cross-calibration and validation for each period. An RSM map of the whole study area was produced by merging the RSM of each of the three subregions. The spatiotemporal and comparative analysis further indicated that the ATI/TVDI joint model has higher applicability (accounting for 36/38 periods) and accuracy than the ATI-based and TVDI-based models. The highest average R-value between the estimated RSM and in situ RSM measurements was 0.73 ± 0.011 (RMSE—root mean square error, 3.43 ± 0.071% and MAE—mean absolute error, 0.05 ± 0.025) on the 137th day of 2017 (DOY—day of the year, 137). Although there is potential for improved mapping of RSM for the entire Chinese Loess Plateau, the iteration procedure of identifying optimal thresholds determination offers a promising method for achieving finer-resolution and robust RSM estimation in large heterogeneous areas.

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“…The basic principle of thermal infra-red (TIR) methods is that land surface temperature is sensitive to surface soil water content due to its impact on surface heating process (heat capacity and thermal conductivity) under bare soil or sparse vegetation cover conditions (Zhao & Li 2013). The common scheme most often utilized in TIR remote sensing of SM is to decouple the surface thermal properties from ambient temperature (daily temperature cycle) by calculating the thermal inertia (Minacapilli et al 2009; Qin et al 2013; Lei et al 2014). Thermal inertia essentially describes a physical property that characterizes the surface resistance to ambient temperature change (Soliman et al 2013).…”

Section: Methodsmentioning

confidence: 99%

The use of earth observation methods for estimating regional crop evapotranspiration and yield for water footprint accounting

Papadavid

Toulios

2017

J. Agric. Sci.

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Remote sensing can efficiently support the quantification of crop water requirements included in the goal of assessing water footprints, which is to analyse how human activities or specific products relate to issues of water scarcity and pollution and identify how activities and products can become more sustainable from a water perspective. Remote sensing techniques have become popular in estimating actual crop evapotranspiration and hence crop water requirements in recent decades due to the advantages they offer to users, e.g. low cost, regional data and use of maps instead of point measurements as well as saving time. The use of earth observation data supports models’ accuracy in the procedure for assessing water footprint, since no average values are used: instead, users have real values for the specific parameters.The present study provides two examples of how remote sensing techniques are used essentially for estimating evapotranspiration along with crop yield, two basic parameters, for assessing water footprint. Two different case studies have been illustrated to define the methodology proposed, which refers to Mediterranean conditions and can be applied after inferring the necessary field data of each crop. The first case study refers to the application of Surface Energy Balance Algorithm for Land (SEBAL) for estimating evapotranspiration, while the second refers to the Crop Yield prediction. Both elements, such as evapotranspiration and crop yield, are vital for water footprint accounting. Firstly, the SEBAL was adopted, under the essential adaptations for local soil and meteorological conditions for estimating groundnut water requirements. Landsat-5 TM, Landsat-7 Enhanced Thematic Mapper+ and Landsat 8 OLI images were used to retrieve the required spectral data. The SEBAL model is enhanced with empirical equations regarding crop canopy factors, in order to increase the accuracy of crop evapotranspiration estimation. Maps were created for evapotranspiration (ET) using the SEBAL modified model for the area of interest. The results were compared with measurements from an evaporation pan, used as a reference. Statistical comparisons showed that the modified SEBAL can predict ETc in a very effective and accurate way and provide water footprint modellers with high-level crop water data. Yield prediction plays a vital role in calculating water footprint. Having real values rather than taking reference (or averaged) values from FAO is an advantage that Earth Observation means can provide. This is very important in econometric or any other prediction models used for estimating water footprint because using average data reduces accuracy. In this context, crop and soil parameters along with remotely sensed data can be used to develop models that can provide users with accurate yield estimations. In a second step, crop and soil parameters along with the normalized difference vegetation index were correlated to examine whether crop yield can be predicted and to define the actual time-window to predict the yield. Statistical and remote sensing techniques were then applied to derive and map a model that can predict crop yield. The algorithm developed for this purpose indicates that remote sensing observations can predict crop yields effectively and accurately. Using the statistical Student's t test, it was found that there was no statistically significant difference between predicted and real values for crop yield.

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Improved spatial resolution in soil moisture retrieval at arid mining area using apparent thermal inertia

Cited by 26 publications

References 11 publications

Investigating spatial and temporal variations of soil moisture content in an arid mining area using an improved thermal inertia model

Investigating spatial and temporal variations of soil moisture content in an arid mining area using an improved thermal inertia model

Soil Moisture Estimation for the Chinese Loess Plateau Using MODIS-derived ATI and TVDI

The use of earth observation methods for estimating regional crop evapotranspiration and yield for water footprint accounting

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