Search Results (17)

Soil moisture is a core climate variable in land surface processes and has a strong influence on the energy balance and water exchange between the land surface–vegetation–atmosphere columns. However, the low spatial resolution of soil moisture remote sensing products cannot satisfy the requirements of research and applications based on hydro-meteorological and eco-hydrological simulations and the management of water resources at the watershed scale. A feasible solution is to downscale soil moisture products derived from microwave remote sensing, which often requires the support of auxiliary variables. Meanwhile, during the validation process of remote sensing products, the spatial scales between in situ observations and remote sensing pixel retrievals are inconsistent; thus, in situ observations should be translated to ground truths at a pixel scale via reasonable upscaling methods. Many auxiliary variables can serve as proxies in the scaling of soil moisture, although few studies have analyzed their feasibility and application conditions. In this paper, a SiB2 (Simple Biosphere Model-II) simulation for the Daman superstation from 1 May to 30 September 2013, was employed to calculate seven auxiliary variables related to soil moisture: ATIs and ATIc (Apparent Thermal Inertias based on surface soil temperature and canopy temperature), E (Evaporation), E/ET_a (Ratio of Evaporation and Actual Evapotranspiration), E/ET_p (Ratio of Evaporation and Potential Evapotranspiration), EF (Evaporative Fraction) and AEF (Actual Evaporative Fraction). The applicability of these variables was then evaluated via a correlation analysis between the variables and soil moisture. The results indicated that E is highly sensitive to soil moisture at Phase I (R² ≥ 0.67), whereas ATIs is the greatest indicator of soil moisture at Phase II (R² ≥ 0.51). Considering both the correlation and computability of these auxiliary variables, the EF (R² ≥ 0.56) and AEF (R² ≥ 0.54) are recommended as proxies for Phase I, while ATIs (R² ≥ 0.51) is also recommended for Phase II. Full article

The use of UAV-based remote sensing for soil moisture has developed rapidly in recent decades, with advantages such as high spatial resolution, flexible work arrangement, and ease of operation. In bare and low-vegetation-covered soils, the apparent thermal inertia (ATI) method, which adopts thermal infrared data from UAV-based remote sensing, has been widely used for soil moisture estimation at the field scale. However, the ATI method may not perform well under inconsistent weather conditions due to inconsistency of the intensity of the soil surface energy input. In this study, an improvement of the ATI method (ATI-R), considering the variation in soil surface energy input, was developed by the incorporation of solar radiation measurements. The performances of the two methods were compared using field experiment data during multiple heating processes under various weather conditions. It showed that on consistently sunny days, both ATI-R and ATI methods obtained good correlations with the volumetric water contents (VWC) (R²_ATI-R = 0.775, RMSE_ATI-R = 0.023 cm³·cm⁻³ and R²_ATI = 0.778, RMSE_ATI = 0.018 cm³·cm⁻³) on cloudy or a combination of sunny and cloudy days as long as there were significant soil-heating processes despite the different energy input intensities; the ATI-R method could perform better than the ATI method (cloudy: R²_ATI-R = 0.565, RMSE_ATI-R = 0.024 cm³·cm⁻³ and R²_ATI = 0.156, RMSE_ATI = 0.033 cm³·cm⁻³; combined: R²_ATI-R = 0.673, RMSE_ATI-R = 0.028 cm³·cm⁻³ and R²_ATI = 0.310, RMSE_ATI = 0.032 cm³·cm⁻³); and on overcast days, both the ATI-R and ATI methods could not perform satisfactorily (R²_ATI-R = 0.027, RMSE_ATI-R = 0.024 cm³·cm⁻³ and R²_ATI = 0.027, RMSE_ATI = 0.031 cm³·cm⁻³). The results indicate that supplemental solar radiation data could effectively expand applications of the ATI method, especially for inconsistent weather conditions. Full article

A simple soil moisture (SM) estimation method is proposed using apparent thermal inertia (ATI) and evapotranspiration (ET) data. Among the methods of estimating SM by using thermal infrared (TIR) remote sensing, the ATI method is widely used in bare soil and low vegetation areas. However, large surface ET will cause ATI error, resulting in lower accuracy of SM estimation. To overcome this problem, the potential of ATI-ET space for estimating the SM of bare and vegetated farmland in the dry season (no irrigation) is studied. ATI and ET data were used to construct triangle feature space, and six distance parameters are extracted from the positions of random pixels in the triangle. Some correlation estimates were made to derive those parameters that were useful for SM estimation, which were three in total. The SM estimation model consisting of these three parameters was built. Compared with the ATI model, the ATI-ET triangle model can not only be applied to areas with high ET, but also has higher accuracy in estimating SM. The ATI-ET triangle model is more suitable for application in bare soil and low vegetation areas. As the Normalized Difference Vegetation Index increases, the accuracy of the model estimates decreases. To show the high portability of the proposed model for SM estimation, we chose another set of in situ SM data acquired in Tibetan Plateau. The results proved the effectiveness of the model in other similar study regions. Full article

Thermal inertia, which represents the resistance to change in temperature of the upper few centimeters of the surface, provides information to help understand the surficial geology and recent processes that are potentially still active today. It cannot be directly measured on Mars and is therefore usually modelled. We present a new analytical method based on Apparent Thermal Inertia (ATI), a thermal inertia proxy. Calculating ATI requires readily available input data: temperature, incidence angle, visible dust opacity, and a digital elevation model. Because of the high spatial resolution, the method can be used on sloping terrains, which makes possible thermal mapping using THEMIS in nearly any area of Mars. Comparison with results obtained by other approaches using modeled data shows similarity in flat areas and illustrates the significant influence of slope and aspect on albedo and diurnal temperature differences. Full article

Soil strength characterization is essential for any problem that deals with geomechanics, including terramechanics/terrain mobility. Presently, the primary method of collecting soil strength parameters through in situ measurements but sending a team of people out to a site to collect data this has significant cost implications and accessing the location with the necessary equipment can be difficult. Remote sensing provides an alternate approach to in situ measurements. In this lab study, we compare the use of Apparent Thermal Inertia (ATI) against a GeoGauge for the direct testing of soil stiffness. ATI correlates with stiffness, so it allows one to predict the soil strength remotely using machine-learning algorithms. The best performing regression algorithm among the ones tested with different predictor variable combinations was found to be KNN with an R² of 0.824 and a RMSE of 0.141. This study demonstrates the potential for using remote sensing to acquire thermal images that characterize terrain strength for mobility utilizing different machine-learning algorithms. Full article

Accuracy soil moisture estimation at a relevant spatiotemporal scale is scarce but beneficial for understanding ecohydrological processes and improving weather forecasting and climate models, particularly in arid and semi-arid regions like the Chinese Loess Plateau (CLP). This study proposed Criterion 2, a new method to improve relative soil moisture (RSM) estimation by identification of normalized difference vegetation index (NDVI) thresholds optimization based on our previously proposed iteration procedure of Criterion 1. Apparent thermal inertia (ATI) and temperature vegetation dryness index (TVDI) were applied to subregional RSM retrieval for the CLP throughout 2017. Three optimal NDVI thresholds (NDVI₀ was used for computing TVDI, and both NDVI_ATI and NDVI_TVDI for dividing the entire CLP) were firstly identified with the best validation results ($\overline{\mathrm{R}}$) of subregions for 8-day periods. Then, we compared the selected optimal NDVI thresholds and estimated RSM with each criterion. Results show that NDVI thresholds were optimized to robust RSM estimation with Criterion 2, which characterized RSM variability better. The estimated RSM with Criterion 2 showed increased accuracy (maximum $\overline{\mathrm{R}}$ of 0.82 ± 0.007 for Criterion 2 and of 0.75 ± 0.008 for Criterion 1) and spatiotemporal coverage (45 and 38 periods (8-day) of RSM maps and the total RSM area of 939.52 × 10⁴ km² and 667.44 × 10⁴ km² with Criterion 2 and Criterion 1, respectively) than with Criterion 1. Moreover, the additional NDVI thresholds we applied was another strategy to acquire wider coverage of RSM estimation. The improved RSM estimation with Criterion 2 could provide a basis for forecasting drought and precision irrigation management. Full article

Microwave remote sensing techniques provide a direct measurement of surface soil moisture (SM), with advantages for all-weather observations and solid physics. However, most satellite microwave soil moisture products fail to meet the requirements of land surface studies for high-resolution surface soil moisture data due to their coarse spatial resolutions. Although many approaches have been proposed to downscale the spatial resolution of satellite soil moisture products, most of them have been tested in flat areas where the surface is relatively homogeneous. Thus, those established approaches are often inapplicable for downscaling in cold alpine areas with complex terrain where multiple factors control the variations in surface soil moisture. In this work, we re-inferred and verified the mathematical assumption behind a semi-physical approach for downscaling satellite soil moisture data and extended this approach for cold alpine areas. Instead of directly deriving SM from proxy variables, this approach relies on a relationship between two standardized variables of SM and apparent thermal inertia (ATI), in which the sub grid standard deviation for SM is estimated by a physical hydraulic model taking soil texture data as input. The approach was applied to downscale the soil moisture active passive (SMAP) daily data in a typical cold alpine basin, i.e., the Babao River basin located in the Qilian Mountains of Northwest China. We observed good linearity between the computed ATI and SM observations on most wireless sensor network sites installed in the study basin, which justifies the underlying assumption. The sub grid standard deviations for the SMAP grid estimated through the Mualem-van Genuchten model can broadly represent the real characteristics. The downscaled 1-km resolution results correlated well with the in-situ SM observations, with an average correlation coefficient of 0.74 and a small root mean square error (0.096 cm³/cm³). The downscaled results show more and consistent textural details than the original SMAP data. After removal of biases in the original SMAP data even higher agreements with the observations can be achieved. These results demonstrate the adequacy of the proposed semi-physical approach for downscaling satellite soil moisture data in cold alpine areas, and the resultant fine-resolution data can serve as useful databases for land surface and hydrological studies in those areas. Full article

Remote sensing (RS) soil moisture (SM) products have been widely used in various environmental studies. Understanding the error structure of data is necessary to properly apply RS SM products in trend and variation analysis and data fusion. However, a spatially continuous assessment of RS SM datasets is impeded by the limited spatial distribution of ground-based observations. As an alternative, the RS apparent thermal inertia (ATI) data related to the SM are transformed into SM values to expand the validation space. To obtain error components, the ATI-based SM along with the Soil Moisture Active Passive Mission (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SM are applied with the triple-collocation (TC) method to evaluate the RS SM data regarding random errors and amplitude variances at the regional scale. When the ATI-based SM is regarded as the reference data, the amplitude biases of the other two datasets are determined. The mean bias is also estimated by calculating the mean value difference between the ATI-based and validated RS SM. The results show that the ATI-based SM is a reliable source of reference data that, when combined with the TC method, can correctly estimate the error structure of RS SM datasets in wide space, promoting the reasonable application and calibration of RS SM datasets. Full article

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 NDVI_ATI and NDVI_TVDI 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. Full article

Apparent thermal inertia (ATI) is a remote sensing-based thermophysical approximation used to estimate surface properties such as particle size or soil moisture, but is subject to oversimplifications if uniform surface materials are assumed. Geological surfaces realistically contain multiple types of materials that are represented by mixed pixels in a digital image that can dramatically affect the derived thermal response. Thus, the current surface uniformity assumption can lead to erroneous calculations. To define how these mixed particle size surfaces affect ATI, a multi-instrument, multi-spectral study was conducted at the North Coulee rhyolite flow, Mono Domes (California). This flow is compositionally homogenous with particle sizes ranging from silt size to boulders, making it an ideal location to understand the relationship between particle size distributions and ATI. Multispectral data from orbital sensors with increasing spatial resolutions were analyzed in combination with samples, GPS, and photogrammetry data collected in the field. The surface particle size characteristics divided into three broad categories (fine, moderate, and coarse) were mapped based on WorldView-2 visible data and field observations. Broad categories were validated using a 3-dimensional point cloud derived from structure-from-motion (SfM) methods using field photographs. The areal percentage of each category within an ATI pixel was derived to populate a lookup table (LUT) with the corresponding ATI values to quantify the effect of mixed pixels. Lower ATI values are dominated by fine particles (sand and dust) as expected, however, surfaces with the highest values were predominately moderate-sized cobbles. Pixels with a high areal percentage of coarse sizes display an intermediate ATI value, suggesting that either self-shadowing or trapping of fines lowers the ATI value. Alternatively, areas with a majority of moderate particle sizes have less shadowing and efficient vertical sorting of smaller material, as seen in field data, leaving a thermal derived response indicative of the dominating particle size. This study demonstrates how a uniform property assumption can cause erroneous derived thermal modeling, particularly over surfaces with a high percentage of boulders and large particle sizes. Future thermophysical studies of Earth or other planetary surfaces can greatly benefit from a multi-sensor approach combined with a higher spatial resolution visible dataset. Full article

Agricultural drought is triggered by a depletion of moisture content in the soil, which hinders photosynthesis and thus increases carbon dioxide (CO₂) concentrations in the atmosphere. The aim of this study is to analyze the relationship between soil moisture (SM) and vegetation activity toward quantifying CO₂ concentration in the atmosphere. To this end, the MODerate resolution imaging spectroradiometer (MODIS), an optical multispectral sensor, was used to evaluate two regions in South Korea for validation. Vegetation activity was analyzed through MOD13A1 vegetation indices products, and MODIS gross primary productivity (GPP) product was used to calculate the CO₂ flux based on its relationship with respiration. In the case of SM, it was calculated through the method of applying apparent thermal inertia (ATI) in combination with land surface temperature and albedo. To validate the SM and CO₂ flux, flux tower data was used which are the observed measurement values for the extreme drought period of 2014 and 2015 in South Korea. These two variables were analyzed for temporal variation on flux tower data as daily time scale, and the relationship with vegetation index (VI) was synthesized and analyzed on a monthly scale. The highest correlation between SM and VI (correlation coefficient (r) = 0.82) was observed at a time lag of one month, and that between VI and CO₂ (r = 0.81) at half month. This regional study suggests a potential capability of MODIS-based SM, VI, and CO₂ flux, which can be applied to an assessment of the global view of the agricultural drought by using available satellite remote sensing products. Full article

Daytime and nighttime thermal infrared observations acquired by the ASTER and MODIS instruments onboard the NASA Terra spacecraft have produced a dataset that can be used to map thermophysical properties across large regions, which have implications on surface processes, thermal environments and habitat suitability for desert species. ASTER scenes acquired between 2004 and 2012 are combined using new mosaicking and data-fusion techniques to produce a map of daytime and nighttime land surface temperature with coverage exclusive of the effects of clouds and weather. These data are combined with Landsat 7 visible imagery to generate a consistent map of apparent thermal inertia (ATI), which is related to the presence of exposed bedrock, rocks, fine-grained sediments and water on the surface. The resulting datasets are compared to known geomorphic units and surface types to generate an interpreted mechanical composition map of the entire Mojave Desert at 100 m per pixel that is most sensitive to large clast size distinctions in grain size distribution. Full article

Soil moisture (SM) plays a fundamental role in the terrestrial water cycle and in agriculture, with key applications such as the monitoring of crop growing and hydrogeological management. In this study, a calibration procedure was applied to estimate SM based on the integration of in situ and airborne thermal remote sensing data. To this aim, on April 2018, two airborne campaigns were carried out with the TASI-600 multispectral thermal sensor on the Petacciato (Molise, Italy) area. Simultaneously, soil samples were collected in different agricultural fields of the study area to determine their moisture content and the granulometric composition. A WorldView 2 high-resolution visible-near infrared (VNIR) multispectral satellite image was acquired to calculate the albedo of the study area to be used together with the TASI images for the estimation of the apparent thermal inertia (ATI). Results show a good correlation (R² = 0.62) between the estimated ATI and the SM of the soil samples measured in the laboratory. The proposed methodology has allowed us to obtain a SM map for bare and scarcely vegetated soils in a wide agricultural area in Italy which concerns cyclical hydrogeological instability phenomena. Full article

In this study, six satellite-based root zone soil moisture (RZSM) estimates from March 2015 to December 2016 were evaluated both temporally and spatially. The first two were the Soil Moisture Active Passive (SMAP) and the Soil Moisture and Ocean Salinity (SMOS) L4 RZSM products. The other four were obtained through the Soil Water Index (SWI) approach, which embedded surface soil moisture (SSM). The SMOS-Barcelona Expert Center (BEC) L4 SSM product and the apparent thermal inertia (ATI)-derived SSM from the Moderate Resolution Imaging Spectroradiometer (MODIS) data were used as SSM datasets. In the temporal analysis, the RZSM estimates were compared to in situ RZSM from 14 stations of the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS). Regarding the spatial assessment, the resulting RZSM maps of the Iberian Peninsula were compared between them. All RZSM values followed the temporal evolution of the ground-based measurements well, although SMOS and MODIS showed underestimation while SMAP displayed overestimation. The good results obtained from MODIS ATI are notable, notwithstanding they were not estimated through microwave radiometry. A very high agreement was found in terms of spatial patterns for the whole Iberian Peninsula except for the extreme north area, which is dominated by high mountains and dense forests. Full article

FengYun-3B (FY-3B) soil moisture product, retrieved from passive microwave brightness temperature data based on the Qp model, has rarely been applied at the catchment and region scale. One of the reasons for this is its coarse spatial resolution (25-km). The study in this paper presented a new method to obtain a high spatial resolution soil moisture product by downscaling FY-3B soil moisture product from 25-km to 1-km spatial resolution using the theory of Apparent Thermal Inertia (ATI) under bare surface or sparse vegetation covered land surface. The relationship between soil moisture and ATI was first constructed, and the coefficients were obtained directly from 25-km FY-3B soil moisture product and ATI derived from MODIS data, which is different from previous studies often assuming the same set of coefficients applicable at different spatial resolutions. The method was applied to Naqu area on the Tibetan Plateau to obtain the downscaled 1-km resolution soil moisture product, the latter was validated using ground measurements collected from Soil Moisture/Temperature Monitoring Network on the central Tibetan Plateau (TP-STMNS) in 2012. The downscaled soil moisture showed promising results with a coefficient of determination R² higher than 0.45 and a root mean-square error (RMSE) less than 0.11 m³/m³ when comparing with the ground measurements at 5 sites out of the 9 selected sites. It was found that the accuracy of downscaled soil moisture was largely influenced by the accuracy of the FY-3B soil moisture product. The proposed method could be applied for both bare soil surface and sparsely vegetated surface. Full article