Remote detection of bare soil moisture using a surface-temperature-based soil evaporation transfer coefficient

In large river basins where in situ data were limited or absent, satellite-based soil moisture estimates can be used to supplement ground measurements for land and water resource management solutions. Consistent soil moisture estimation can aid in monitoring droughts, forecasting floods, monitoring crop productivity, and assisting weather forecasting. Satellite-based soil moisture estimates are readily available at the global scale but are provided at spatial scales that are relatively coarse for many hydrological modeling and decision-making purposes. Soil moisture data are obtained from NASA’s soil moisture active passive (SMAP) mission radiometer as an interpolated product at 9 km gridded resolution. This study implements a soil moisture downscaling algorithm that was developed based on the relationship between daily temperature change and average soil moisture under varying vegetation conditions. It applies a look-up table using global land data assimilation system (GLDAS) soil moisture and surface temperature data, and advanced very high resolution radiometer (AVHRR) and moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and land surface temperature (LST). MODIS LST and NDVI are used to obtain downscaled soil moisture estimates. These estimates are then used to enhance the spatial resolution of soil moisture estimates from SMAP 9 km to 1 km. Soil moisture estimates at 1 km resolution are able to provide detailed information on the spatial distribution and pattern over the regions being analyzed. Higher resolution soil moisture data are needed for practical applications and modelling in large watersheds with limited in situ data, like in the Lower Mekong River Basin (LMB) in Southeast Asia. The 1 km soil moisture estimates can be applied directly to improve flood prediction and assessment as well as drought monitoring and agricultural productivity predictions for large river basins.

The suitability of using Moderate Resolution Imaging Spectroradiometer (MODIS) images for surface soil moisture estimation to investigate the importance of soil moisture in different applications, such as agriculture, hydrology, meteorology and natural disaster management, is evaluated in this study. Soil moisture field measurements and MODIS images of relevant dates have been acquired. Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI) are calculated from MODIS images. In addition, MODIS Land Surface Temperature (LST) data (MOD11A1) are used in this analysis. Four different soil moisture estimation models, which are based on NDVI–LST, EVI–LST, NDVI–LST–NDWI and EVI–LST–NDWI, are developed and their accuracies are assessed. Statistical analysis shows that replacing EVI with NDVI in the model that is based on LST and NDVI increases the accuracy of soil moisture estimation. Accuracy evaluation of soil moisture estimation using check points shows that the model based on LST, EVI and NDWI values gives a higher accuracy than that based on LST and EVI values. It is concluded that the model based on the three indices is a suitable model to estimate soil moisture through MODIS imagery.

<p>Severely dry climate plays an important role in the occurrence of wildfires in Thailand. Soil water deficits increase dry conditions, resulting in more intense and longer burning wildfires. The temperature vegetation dryness index (TVDI) and the normalized difference drought index (NDDI) were used to estimate soil moisture during the dry season to explore its use for wildfire risk assessment. The results reveal that the normalized difference wet index (NDWI) and land surface temperature (LST) can be used for TVDI calculation. Scatter plots of both NDWI/LST and the normalized difference vegetation index (NDVI)/LST exhibit the triangular shape typical for the theoretical TVDI. However, the NDWI is more significantly correlated to LST than the NDVI. Linear regression analysis, carried out to extract the maximum and minimum LSTs (LST<sub>max</sub>, LST<sub>min</sub>), indicate that LST<sub>max </sub>andLST<sub>min</sub> delineated by the NDWI better fulfill the collinearity requirement than those defined by the NDVI. Accordingly, the NDWI-LST relationship is better suited to calculate the TVDI. This modified index, called TVDI<sub>NDWI-LST</sub>, was applied together with the NDDI to establish a regression model for soil moisture estimates. The soil moisture model fulfills statistical requirements by achieving 76.65% consistency with the actual soil moisture and estimated soil moisture generated by our model. The relationship between soil moisture estimated from our model and leaf fuel moisture indicates that soil moisture can be used as a complementary dataset to assess wildfire risk, because soil moisture and fuel moisture content (FMC) show the same or similar behavior under dry conditions. <strong></strong></p>

Soil moisture is an important component of the soil-vegetation-atmosphere continuum (SPAC). It is a key factor to determine the water status of terrestrial ecosystems, and is also the main source of water supply for crops. In order to estimate soil moisture at different soil depths at a station scale, based on the energy balance equation and the water deficit index (WDI), a soil moisture estimation model was established in terms of the remote sensing data (the normalized difference vegetation index and surface temperature) and air temperature. The soil moisture estimation model was validated based on the data from the drought process experiment of summer maize (Zea mays) responding to different irrigation treatments carried out during 2014 at Gucheng eco-agrometeorological experimental station of China Meteorological Administration. The results indicated that the soil moisture estimation model developed in this paper was able to evaluate soil relative humidity at different soil depths in the summer maize field, and the hypothesis was reasonable that evapotranspiration deficit ratio (i.e., WDI) linearly depended on soil relative humidity. It showed that the estimation accuracy of 0-10 cm surface soil moisture was the highest (R2=0.90). The RMAEs of the estimated and measured soil relative humidity in deeper soil layers (up to 50 cm) were less than 15% and the RMSEs were less than 20%. The research could provide reference for drought monitoring and irrigation management.

Accurate soil moisture estimation is fundamental for optimizing irrigation strategies, enhancing crop yields, and managing water resources efficiently. This study harnesses time-series RGB-thermal imagery to assess soil moisture throughout various growth stages of corn, emphasizing depth-specific soil moisture estimation and time-series analysis of canopy information such as canopy structure and canopy spectral across growth stages. By integrating a comprehensive dataset that covers the full spectrum of the growing season from early to late stages. we evaluated soil moisture at multiple depths including 10, 20, 30, and 40 cm. Sophisticated regression models such as Gradient Boosting Machines (GBM), Least Absolute Shrinkage and Selection Operator (Lasso), and Support Vector Machines (SVM) were employed to analyze the effects of spectral indices, land surface temperature (LST), and structural canopy variables on soil moisture estimation accuracy. Our results reveal that thermal variables, particularly LST, exhibit significant correlations with soil moisture at shallower depths, especially in non-irrigated plots where moisture variability tends to be greater. The GBM model performed exceptionally well, achieving a coefficient of determination (R²) of 0.79 and a root mean square error (RMSE) of 1.86 % at a depth of 10 cm, showcasing its precision in moisture prediction. At a depth of 30 cm, the GBM model still demonstrated robust performance with an R² of 0.69 and an RMSE of 3.38 %, adapting effectively to different canopy densities and soil conditions. As canopy density increased, the effectiveness of LST in predicting soil moisture decreased, underscoring the dynamic interaction between plant growth stages and moisture estimation accuracy.

The complex interactions between soil moisture and precipitation are difficult to observe, and consequently there is a lack of consensus as to the sign, strength, and location of these interactions. Inconsistency between soil moisture–precipitation interaction studies can be attributed to a multitude of factors, including the difficulty of demonstrating causal relationships, dataset differences, and precipitation autocorrelation. The purpose of this study is to explore these potential confounding factors and determine which are most important for consideration when assessing statistical coupling between soil moisture and precipitation. Soil moisture is assessed via three remote sensing datasets: the Advanced Microwave Scanning Radiometer for Earth Observing System, the Tropical Rainfall Measuring Mission Microwave Imager, and the Essential Climate Variable Soil Moisture. Estimates of soil moisture are coupled with afternoon thunderstorm events identified by the Thunderstorm Observation by Radar (ThOR) algorithm, and dry soil or wet soil preferences for convection initiation are determined for over 16 000 thunderstorm events between 2005 and 2007. Differences in soil moisture datasets were found to have the largest impact with regard to determining wet or dry soil preferences. Precipitation autocorrelation is prevalent in the data; however, precipitation autocorrelation did not influence the results with regard to dry or wet soil preferences. Consideration of the convective environment (i.e., weakly or synoptically forced) did result in significant differences in wet/dry soil preference, but only for certain soil moisture datasets. The results suggest that observation-driven soil moisture–precipitation interaction studies should both consider the convective environment and implement multiple soil moisture datasets to assure robust results.

PDF HTML阅读 XML下载 导出引用 引用提醒 太湖流域丘陵区两种土地利用类型土壤水分分布控制因素 DOI: 10.5846/stxb201405181019 作者: 作者单位: 中科院南京地理与湖泊研究所,中科院南京地理与湖泊研究所,中科院南京地理与湖泊研究所,中科院南京地理与湖泊研究所 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金(41271109, 41301234); 中国科学院南京地理与湖泊研究所"一三五"重点项目(NIGLAS2012135005) The controlling factors of soil moisture distribution under two typical land-use hillslopes in a hilly region of Taihu Lake basin Author: Affiliation: Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences,,Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences,Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:为探究太湖流域丘陵区典型土地利用类型(如竹林地和茶园)土壤水分的控制因素,在不同深度土壤水分定期观测的基础上,根据前7d降雨量将研究时段划分为干旱状态和湿润状态,利用分类与回归树(CART)方法得出不同干湿状态下土壤水分分布的主控因子,并借助典范对应分析(CCA)定量分析不同土地利用类型、不同土壤深度土壤水分格局与环境因子关系。结果表明:(1)高程、土地利用类型和土层厚度对土壤水分分布的相对贡献率最大,但在不同干湿状态下其影响程度存在差异; (2)干旱状态时土壤水分主要受高程、坡度、地形湿度指数(TWI)和剖面曲率等地形因素的作用,而土层厚度和粘粒也分别为0-20 cm和20-40 cm深度土壤水分的主控因子; (3)在湿润状态下,茶园0-20 cm土壤水分的主控因素为地形因子,在20-40 cm则以土壤性质为主,竹林地两个深度的土壤水分受地形和土壤性质的作用都很强,其中20-40 cm深度土壤水分与环境因子的关系较0-20 cm深度更为复杂。 Abstract:Soil moisture is one of the most important factors in terrestrial ecosystems. Adequate knowledge of its spatio-temporal variability is critical to many scientific and practical applications. We explored the factors controlling moisture distribution in soils under typical land-use types (i.e., bamboo forest and tea garden) in a hilly region of Taihu Lake basin. In situ soil moisture measurements were made at different depths, and these were classified as dry or wet conditions, based on the precipitation during the previous 7 d. The main controlling factors were identified by using the classification and regression tree (CART) method. Canonical correspondence analysis (CCA) was then applied to quantitatively analyze the relationships between soil moisture and environmental factors under different land-uses at different soil depths. The results show that: (1) the relative influences of elevation, land use, and soil thickness on soil moisture distribution were larger than those of other environmental factors in most cases. Together these accounted for more than 50% of the total variation in soil moisture. However, their relative contributions differed between dry and wet conditions. (2) In dry conditions soil moisture was mainly affected by topographic indices such as elevation, slope, topographic wetness index (TWI) and profile curvature. Soil thickness and clay content also significantly affected soil moisture at 0-20 and 20-40 cm depths, respectively. The main factors controlling soil moisture in dry conditions varied with soil depth, but not with land-use type. This finding was different from that of a previous study, in which the study area had similar landforms and climatic conditions. This may be due to the effects of spatial scale. (3) In wet conditions, topographic indices and soil properties were the main factors controlling soil moisture in tea gardens at 0-20 and 20-40 cm depths, respectively. Both topographic indices and soil properties were the main controlling factors of soil moisture in a bamboo forest at these two depths. It is noted that the relationship between soil moisture and environmental factors at 20-40 cm depth is more complicated than that at 0-20 cm depth in bamboo forest. In contrast to dry conditions, both soil depth and land-use type had a significant impact on the environmental factors that control soil moisture distribution. This study is of great relevance to the sustainable utilization of water and soil resources and to the management of water and fertilizer in agriculture in this region. 参考文献 相似文献 引证文献

Soil moisture and evapotranspiration (ET) is affected by both water and energy balances in the soilvegetation- atmosphere system, it involves many complex processes in the nexus of water and thermal cycles at the surface of the Earth. These impacts may affect the recharge of the upper Floridian aquifer. The advent of urban hydrology and remote sensing technologies opens new and innovative means to undertake eventbased assessment of ecohydrological effects in urban regions. For assessing these landfalls, the multispectral Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing images can be used for the estimation of such soil moisture change in connection with two other MODIS products - Enhanced Vegetation Index (EVI), Land Surface Temperature (LST). Supervised classification for soil moisture retrieval was performed for Tampa Bay area on the 2 kmx2km grid with MODIS images. Machine learning with genetic programming model for soil moisture estimation shows advances in image processing, feature extraction, and change detection of soil moisture. ET data that were derived by Geostationary Operational Environmental Satellite (GOES) data and hydrologic models can be retrieved from the USGS web site directly. Overall, the derived soil moisture in comparison with ET time series changes on a seasonal basis shows that spatial and temporal variations of soil moisture and ET that are confined within a defined region for each type of surfaces, showing clustered patterns and featuring space scatter plot in association with the land use and cover map. These concomitant soil moisture patterns and ET fluctuations vary among patches, plant species, and, especially, location on the urban gradient. Time series plots of LST in association with ET, soil moisture and EVI reveals unique ecohydrological trends. Such ecohydrological assessment can be applied for supporting the urban landscape management in hurricane-stricken regions.

Tremendous progress has been made in estimating soil moisture (SM) from satellite remote sensing data. Several global-scale coarse-resolution products have also been generated and released for various applications in the Earth system. However, high-resolution SM estimation is still in its infancy. Currently, two main methods are used for this purpose: downscaling approaches and direct retrieval from microwave and optical/thermal data. Several studies have attempted to comprehensively evaluate the performance of these approaches and have found that each method has its own strengths and weaknesses, with no single method outperforming the others. In this study, we aim to investigate the advantages of integrating optical, thermal, and microwave data to estimate SM by leveraging an intensive SM network and triple collocation (TC) method. First, we determined the best-performing coarse-resolution microwave SM product through the TC approach. Second, we generated 1-km SM using a downscaling approach based on land surface temperature and vegetation index, utilizing the best-performing SMAP L3 descending product. Third, we evaluated the high-resolution downscaled SM, Sentinel 1 SM, and SMAP/Sentinel 1 combined SM products using SM measurements from the REMEDHUS station network, ETOPO1 elevation, Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) precipitation, and the European Space Agency (ESA) CCI land cover map. Finally, we investigated and demonstrated the advantages of merging these products through point-scale evaluation and large-scale spatial pattern comparison.

Soil moisture (SM) applications in terrestrial hydrology require higher spatial resolution soil moisture products than those provided by passive microwave remote sensing instruments (grid resolution of 9 km or larger). In this investigation, an innovative algorithm that uses visible/infrared remote sensing observations to downscale Advanced Microwave Scanning Radiometer 2 (AMSR2) coarse spatial resolution SM products was developed and implemented for use with data provided by the Advanced Microwave Scanning Radiometer 2 (AMSR2). The method is based on using the Normalized Difference Vegetation Index (NDVI) modulated relationships between day/night SM and temperature change at corresponding times. Land surface model output variables from the North America Land Data Assimilation System (NLDAS), remote sensing data from the Moderate-Resolution Imaging Spectroradiometer (MODIS), and Advanced Very High Resolution Radiometer (AVHRR) were used in this methodology. The functional relationships developed using NLDAS data at a grid resolution of 12.5 km were applied to downscale AMSR2 JAXA (Japan Aerospace Exploration Agency) SM product (25 km) using MODIS land surface temperature (LST) and NDVI observations (1 km) to produce the 1 km SM estimates. The downscaled SM estimates were validated by comparing them with ISMN (International Soil Moisture Network) in situ SM in the Black Bear–Red Rock watershed, central Oklahoma between 2015–2017. The overall statistical variables of the downscaled AMSR2 SM validation R2, slope, RMSE and bias, demonstrate good accuracy. The downscaled SM better characterized the spatial and temporal variability of SM at watershed scales than the original SM product.

The global agronomy community needs quick and frequent information on soil moisture variability and spatial trends in order to maximize crop production to meet growing food demands in a changing climate. However, in situ soil moisture measurement is expensive and labor intensive. Remote sensing based biophysical and predictive regression modeling approach have the potential for efficiently estimating soil moisture content over large areas. The study investigates the use of Moisture Stress Index (MSI) to estimate soil moisture variability in Alabama. In situ data were obtained from Soil Climate Analysis Network (SCAN) sites in Alabama and MSI developed from LANDSAT 8 OLI and LANDSAT 5 TM data. Pearson product moment correlation analysis showed that MSI strongly correlates with 16-day average growing season soil moisture measurements, with negative correlations of -0.519, -0.482 and -0.895 at 5, 10, and 20 cm soil depths respectively. The correlations of MSI and growing season moisture were low at sites where soil moisture was extremely low (<-0.3 at all depths). Simple linear regression model constructed for soil moisture at 20 cm depth (R²=0.79, p<0.05) correlated well with MSI values and was successfully used to estimate soil moisture percentage within a standard error of ± 3. Resulting MSI products were used to successfully produce the spatial distribution of soil moisture percentage at 20 cm depth. The study concludes that MSI is a good indicator of soil moisture conditions, and could be efficiently utilized in areas where in situ soil moisture data are unavailable.

Soil moisture helps to determine the crop growth, disaster management, climatology and ecology. Soil moisture is highly influenced by vegetation cover. The current study was performed in Varanasi district of Uttar Pradesh on March 29, 2023, to estimate soil moisture using Landsat 8 data and biophysical parameters. This study also assessed the relationship between the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) and their response to soil moisture. Additionally, it coupled NDVI-LST feature space-derived dry and wet edge best fit coefficient parameters with the Temperature Vegetation Dryness Index (TVDI) and Soil Moisture Index (SMI) equations to produce a spatial distribution of soil moisture availability. The LST and NDVI results help to extract Soil Surface Moisture (SSM) more effectively using an optical remote sensing approach. The soil moisture inversion model (TVDI) and SMI algorithm provide a better representation of the spatial distribution of soil moisture in Varanasi district. A linear and strong correlation exists between in-situ soil moisture data and TVDI (R2 = 0.6812), as well as a positive relationship between in-situ soil moisture data and SMI (R2 = 0.6848). It was found that LST and NDVI helped to generate the TVDI and SMI equations using the triangle model, and both equations demonstrated a strong correlation with in-situ data (R2 of TVDI = 0.6812, R2 of SMI = 0.6848).

The difficulty of accurate and large-scale measurement for surface parameters limits the regional surface soil moisture (SSM) estimation using synthetic aperture radar (SAR). Moreover, the coarse resolution of soil moisture products generated by existing methods, which fuse SAR and passive microwave products, cannot fully satisfy the requirement of specific regional applications. To solve this problem, an SAR-optical data fusion method for soil moisture estimation (SOFSME) based on a cascade neural network is proposed in this study. SOFSME obtains surface parameters from historical soil moisture images and related environmental images to estimate a SSM image with high resolution at large scale from Sentinel-1A C-band SAR data. Validation experiments in single and multiple land-use type areas showed that the SOFSME performed best on bare soil areas with a median root mean square error of 0.0203. The median universal image quality index of estimated soil moisture image was 0.1454, which was better for single cropland areas than multi-land-use type areas. The Pearson correlation coefficient showed a median value of 0.7645 in both experiments. These results showed that the SOFSME had high accuracy, availability, and stability in regional soil moisture estimation. Compared with existing methods, the SOFSME can provide high-quality soil moisture images and does not directly depend on field measurement data. Thus, the proposed SOFSME method is of great value for high-resolution soil moisture estimation in more regional applications.

Downscaling microwave soil moisture (SM) with optical/thermal remote sensing data has considerable application potential. Spatial correlations between SM and land surface temperature (LST) or LST-derived SM indexes (SMIs) are vital to the current optical/thermal and microwave fusion downscaling methods. In this study, the spatial correlations were evaluated at the same spatial scale using SMAPVEX12 SM data and MODIS day/night LST products. LST-derived SMIs was calculated using NLDAS-2 gridded meteorological data with conventional trapezoid and two-stage trapezoid models. Results indicated that (1) SM agrees better with daytime LST than the nighttime or the day-night differential LST; (2) the daytime LSTs on Aqua and Terra present very similar spatial agreement with SM and they have very similar performances as downscaling factors in simulating SM; (3) decoupling effect among SM, LST, and LST-derived SMIs occurs not only in very wet but also in very dry condition; and (4) the decoupling effect degrades the performance of LST as a downscaling factor. The future downscaling algorithms should consider net surface radiation and soil type to tackle the decoupling effect.

Retrieving soil moisture from grape growing areas using multi-feature and stacking-based ensemble learning modeling