Cross-estimation of Soil Moisture Using Thermal Infrared Images with Different Resolutions

Hsu, Wei-Ling; Chang, Kuan-Tsung

doi:10.18494/sam.2019.2090

Cited by 10 publications

(6 citation statements)

References 13 publications

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“…Figure 14 and Table A4 demonstrate that the combined model we applied for RSM retrieval shows the improvements solely over ATI-and TVDI-based models with the minimum difference between the estimated RSM and the observed RSM and maximum R-value (correlation coefficient) in calibration. The comparative analysis indicated that the RSM estimated by the TVDI-based model was closest to the actual RSM measured on-site compared to the ATI-based model, which shows a good agreement with Hsu's research results [18]. Jiao et al investigated the relationships between SM and vegetation coverage and suggested that excessive reliance on afforestation was risky for the improvement of vegetation coverage in arid and semi-arid regions and we, thus, need to focus on the soil moisture conditions during large-scale re-vegetation.…”

Section: Applicability Of the Ati/tvdi Joint Modelsupporting

confidence: 76%

“…TVDI is derived from vegetation indices (e.g., NDVI-normalized difference vegetation index) and land surface temperature (LST) [8][9][10], whereas ATI is obtained from LST and albedo [11][12][13][14][15][16]. 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].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, based on the relationship between remotely sensed LST and NDVI (generally called NDVI-LST space) [28], Sandholt et al defined TVDI [29], which can represent the degree of SM in the area with vegetation coverage. Since then, this index has been adopted by many researchers to estimate SM from Moderate Resolution Imaging Spectroradiometer (MODIS) data on global and regional scales [18,[30][31][32][33][34][35]. The potential of the MODIS-derived land surface dryness index for SM estimation over large geographical areas has been well demonstrated in specific seasons [20,36].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Applicability Of the Ati/tvdi Joint Modelsupporting

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Although the TVDI model has been used to estimate SWC using UAS images in recent years, its application in estimating SWC only incorporates the spectral characteristics of surface objects without considering the soil properties that affect SWC [27,43,[53][54][55]. For instance, soil texture, an important soil parameter affecting SWC, is not considered in the TVDI model [4,56].…”

Section: Introductionmentioning

confidence: 99%

Retrieving Surface Soil Water Content Using a Soil Texture Adjusted Vegetation Index and Unmanned Aerial System Images

Lin

Guo

et al. 2021

Remote Sensing

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Surface soil water content (SWC) is a major determinant of crop production, and accurately retrieving SWC plays a crucial role in effective water management. Unmanned aerial systems (UAS) can acquire images with high temporal and spatial resolutions for SWC monitoring at the field scale. The objective of this study was to develop an algorithm to retrieve SWC by integrating soil texture into a vegetation index derived from UAS multispectral and thermal images. The normalized difference vegetation index (NDVI) and surface temperature (Ts) derived from the UAS multispectral and thermal images were employed to construct the temperature vegetation dryness index (TVDI) using the trapezoid model. Soil texture was incorporated into the trapezoid model based on the relationship between soil texture and the lower and upper limits of SWC to form the texture temperature vegetation dryness index (TTVDI). For validation, 128 surface soil samples, 84 in 2019 and 44 in 2020, were collected to determine soil texture and gravimetric SWC. Based on the linear regression models, the TTVDI had better performance in estimating SWC compared to the TVDI, with an increase in R2 (coefficient of determination) by 14.5% and 14.9%, and a decrease in RMSE (root mean square error) by 46.1% and 10.8%, for the 2019 and 2020 samples, respectively. The application of the TTVDI model based on high-resolution multispectral and thermal UAS images has the potential to accurately and timely retrieve SWC at the field scale.

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“…Recently, amorphous silicon bolometer-type uncooled infrared image sensors have been developed in various fields for thermal imaging systems, night vision, surveillance, and medical imaging. (1)(2)(3)(4)(5)(6)(7) Amorphous silicon bolometer-type uncooled infrared image sensors have many advantages owing to their low cost, low power consumption, and easy integration. However, further advances are required in terms of pixel size and device weight.…”

Section: Introductionmentioning

confidence: 99%

Pixel Dark Current Calibration Method to Reduce Dark Fixed Pattern Noise in Amorphous Silicon Bolometer-type Uncooled Infrared Image Sensor

Kim¹,

Lee²,

Kwen³

et al. 2020

Sensors and Materials

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In this paper, we propose a pixel dark current calibration method to reduce dark fixed pattern noise in an amorphous silicon bolometer-type uncooled infrared image sensor. This dark current calibration method utilizes an active pixel, a reference pixel, and a calibration circuit. The active and reference pixels were fabricated entirely from amorphous silicon. Even if amorphous silicon components with the same resistance are manufactured, there may be variations in resistance owing to process deviations during the manufacture of the amorphous silicon pixels. Dark fixed pattern noise due to resistance variations causes non-uniformity among the output voltages of the pixels. The operating principle of the pixel dark current calibration method to reduce dark fixed pattern noise is thus based on the subtraction of the averaged dark current of the active pixel from that of the reference pixel. The proposed pixel dark current calibration method is implemented by a chip consisting of an amorphous silicon bolometer pixel array, a calibration circuit, and column readout circuits. The entire chip was fabricated using a 0.11 μm CMOS image sensor process and its performance was evaluated.

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Cross-estimation of Soil Moisture Using Thermal Infrared Images with Different Resolutions

Cited by 10 publications

References 13 publications

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

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

Retrieving Surface Soil Water Content Using a Soil Texture Adjusted Vegetation Index and Unmanned Aerial System Images

Pixel Dark Current Calibration Method to Reduce Dark Fixed Pattern Noise in Amorphous Silicon Bolometer-type Uncooled Infrared Image Sensor

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