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

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.

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.

<p>A Critical Zone Observatory (CZO) was recently established in the Alento River Catchment (ARC; southern Italy) within the TERENO (TERrestrial ENvironmental Observatories) long-term ecosystem infrastructure network. In 2016 SoilNet wireless sensor networks and cosmic ray neutron probes (CRNP) were installed in the upper part of this catchment and specifically in two experimental sub-catchments (MFC2 and GOR1) characterized by different topographic, pedological, land-use, and weather conditions. The Soilnet end-devices are monitoring soil moisture and matric potential at two different soil depths (15 cm and 30 cm) in 20 locations around the cosmic ray neutron probe. We evaluated the the relationship between Soil Moisture Index (SMI) and rainfall deficits (considered as rainfall minus potential evapotranspiration) at monthly time scale. The cropland site on the south-facing hillslope of ARC is characterized by more extreme dry and wet conditions. Another goal is to identify the dominant controls that most govern the spatial soil moisture patterns in these two different sites. The relationship between the CRNP-based soil moisture and spatial variability of SoilNet-based soil moisture is nearly linear in the case of the cropland site (MFC2) but follows a fairly concave curve in the case of the forestland site (GOR1). The majority of the spatial variance in MFC2 is explained by terrain attributes, i.e. slope-induced during wet conditions and aspect-induced during dry conditions. In GOR1 the spatial variance of soil moisture data is mostly explained by topographic factors under wet conditions during the rainy season. In both sites the soil texture is able to explain only less than 10% of spatial variability of soil moisture data.</p>

Regional scale spatio-temporal variability of soil moisture and its relationship with meteorological factors over the Korean peninsula

Deep soil (>2 m) moisture is relatively stable water that supports tree growth and water management in the loess hilly–gully area of China. Characterizing the spatial variation of deep soil moisture is important for water management in apple orchards. Using a space‐for‐time substitution approach, we analysed the spatial variation of soil moisture content ( θ ) in 0–8 m soil profiles in a dry year (2015, precipitation: 392 mm) in rainfed apple ( Malus pumila Mill.) orchards of various ages (mainly established in 2009, 2006, 2003, 1997 and 1994). The soil moisture surveys revealed: (a) four soil layers according to soil moisture distribution characteristics at 0–8.0 m depth: shallow rapidly changing layer (0–0.6 m), rainfall redistribution layer (0.6–2.0 m), transition layer (2.0–4.0 m) and stable layer (4.0–8.0 m); (b) the most critical zone for water consumption is the 0.6–2.0 m soil layer, with the lowest θ in the orchards, while water in the 4.0–8.0 m soil layer remained stable; and (c) significant ( p < .01) positive correlations between θ s in the 0.6–2.0 m, 2.0–4.0 m and 4.0–8.0 m layers. In addition, the θ s showed a significant ( p < .01) negative relationship with orchard age in the deep soil layers (>2 m), and especially in the 0.6–2.0 m soil layer. Therefore, effective water management through various water regulation measures at the 0.6–2.0 m soil layer is essential for the sustainable development of apple orchards and reducing drought stress on the Loess Plateau and, potentially, other semi‐arid orchards.

Soil moisture plays a vital role in land surface energy and the water cycle. Microwave remote sensing is widely used because of the physically based relationship between the land surface emission observed and soil moisture. However, the application of retrieved soil moisture data is restricted by its coarse spatial resolution. To overcome this weakness, downscaling methods should be developed to disaggregate coarse resolution microwave soil moisture data to fine resolution. The traditional method is the microwave-optical/IR synergistic approach, in which land surface temperature, vegetation index, and surface albedo are key parameters. Five purely empirical methods based on the triangle feature are selected in this study. To evaluate their performance on downscaling microwave soil moisture, these methods are applied to the Zoige Plateau in China using the Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) Land Parameter Retrieval Model (LPRM) soil moisture product and Moderate Resolution Imaging Spectroradiometer (MODIS) optical/IR products. The coarse-resolution AMSR-E LPRM soil moisture data are disaggregated into the high resolution of the MODIS product, and the surface soil moisture measurements of the Maqu soil moisture observation network located in the plateau are used to validate the downscaling results. Results show that (1) the relationship models used in these methods can generally capture the variation in soil moisture, with R2 around 0.6, but have a relatively high uncertainty under conditions of high soil moisture; (2) the methods can provide high-resolution soil moisture distribution, but the downscaled soil moisture presents a low level correlation with field measurements at different spatial and temporal scales. This comparative study provides insight into the performance of popular purely empirical downscaling methods on enhancing the spatial resolution of soil moisture on the Tibetan Plateau. Although synergistic methods can improve the spatial resolution of AMSR-E soil moisture data, additional studies are needed to exclude the uncertainty from AMSR-E soil moisture estimation, the low sensitivity of the relationship model under high soil moisture, and the spatial representativeness difference between coarse pixels and point measurement.

<p>Soil physical properties can show high heterogeneity in forest soils, implying a high spatial variability of soil moisture and plant water availability. Legacy effects of past land use, resulting in a small-scale mosaic of anthropogenically modified and unmodified soils in forest areas, further increase this spatial heterogeneity. The soils associated with relict charcoal hearths (RCH), most prominently characterized by a technogenic layer rich in charcoal fragments, are a widespread example for such land use legacy soils in forest areas. </p><p>The objective of our study was to characterize the soil moisture regime of forest soils and RCH soils on sandy substrates in the northeastern German lowlands; and in particular to assess the long-term effect of the prolonged summer drought period in 2018 on spatial and temporal variability of soil moisture. We monitored matric potentials, soil water contents and soil temperature from June 2018 in forest soils in the Tauersche Forst (Brandenburg, Germany). Three soil profiles (one reference forest soil profile and two characteristic RCH soil profiles) were instrumented, sampled for laboratory analyses of bulk density, pore size distribution, saturated hydraulic conductivity, and persistence of water repellency; and root density distribution in the profiles was recorded. </p><p>The soils in all profiles show low porosity and plant available water contents. On RCH soils, overall porosity is clearly higher compared with the reference soils, mainly related to larger volumes of coarse and fine pores. Soil moisture monitoring shows very high spatial and temporal variability of the recorded data after the prolonged dry period in 2018, with gradual rewetting over the winter periods and only short-term fluctuations of soil moisture in reaction to high-intensity precipitation events in the summer periods. The comparison of in-situ and laboratory-based water retention data shows strong hysteresis effects during the rewetting, with increasingly clear reactions of soil water contents to precipitation events over the years following the dry period, especially in the charcoal-rich substrates. The differences in soil moisture between the dry and the wetter periods were clearly higher in the RCH soils, which, compared with the reference soils, showed higher water contents under wet conditions and lower water contents under dry conditions. The results affirm that land use legacies can clearly increase the spatial and temporal variations of soil moisture in forest areas.</p>

Abstract. Past studies on soil moisture spatial variability have been mainly conducted at catchment scales where soil moisture is often sampled over a short time period; as a result, the observed soil moisture often exhibited smaller dynamic ranges, which prevented the complete revelation of soil moisture spatial variability as a function of mean soil moisture. In this study, spatial statistics (mean, spatial variability and skewness) of in situ soil moisture, modeled and satellite-retrieved soil moisture obtained in a warm season (198 days) were examined over three large climate regions in the US. The study found that spatial moments of in situ measurements strongly depend on climates, with distinct mean, spatial variability and skewness observed in each climate zone. In addition, an upward convex shape, which was revealed in several smaller scale studies, was observed for the relationship between spatial variability of in situ soil moisture and its spatial mean when statistics from dry, intermediate, and wet climates were combined. This upward convex shape was vaguely or partially observable in modeled and satellite-retrieved soil moisture estimates due to their smaller dynamic ranges. Despite different environmental controls on large-scale soil moisture spatial variability, the correlation between spatial variability and mean soil moisture remained similar to that observed at small scales, which is attributed to the boundedness of soil moisture. From the smaller support (effective area or volume represented by a measurement or estimate) to larger ones, soil moisture spatial variability decreased in each climate region. The scale dependency of spatial variability all followed the power law, but data with large supports showed stronger scale dependency than those with smaller supports. The scale dependency of soil moisture variability also varied with climates, which may be linked to the scale dependency of precipitation spatial variability. Influences of environmental controls on soil moisture spatial variability at large scales are discussed. The results of this study should be useful for diagnosing large scale soil moisture estimates and for improving the estimation of land surface processes.

为揭示艾比湖湿地土壤退化程度空间分布特征，在离艾比湖湖滨5~15 km，绕湖一周160 km范围内，以湖心质点为中心，将艾比湖划分为东北、东南、西南、西北4个区域，采用传统统计学和地统计学相结合的方法对表层（0~20 cm）土壤盐分、含水量与pH的空间分异特征进行研究.结果表明：绕湖一周不同区域的土壤盐分均属中等变异强度；土壤含水量在西北部属强变异性，而东北、东南和西南部均属中等变异强度；土壤pH在不同区域内均属弱变异强度.绕湖一周除西北部土壤盐分的半方差理论模型较符合球状模型外，其它区域土壤盐分、含水量和pH均符合高斯模型；受结构性因素影响，不同区域土壤盐分、含水量和pH均具有较强的空间相关性；西南部土壤盐分、含水量和pH的Moran's I系数比其它区域的波动大，表明空间相关性较强.艾比湖湿地常年大风、干旱、缺水及沙化盐化的自然因素与引水围堰、种植耐盐碱植物的人为活动造成了采样区表层土壤盐分、含水量和pH的空间分布多呈现不规则条带状格局.艾比湖湿地土壤以盐土为主，重度盐化土次之，土壤盐渍化日益严重.;Systematic research of soil water and salt is commonly lack in previous studies for the Lake Ebinur, thus in this study, in order to reveal the spatial distribution characteristics of soil degradation degree in the Lake Ebinur wetland, by taking the lake side of a distance to the Lake Ebinur 5-15 km, a circle area of 160 km² as research area. The spatial variability of soil salinity,pH and moisture in the surface layer(0-20 cm) of in the circle sampling area of Lake Ebinur was analyzed by using traditional statistics and geo-statistics. The results indicated that the variation in salinity was moderate in the different directions. The soil moisture in the Northeast, Southeast and Southwest belongs to the medium variation intensity, while the northwest belongs to strong variability. The soil pH of different directions has weak variation. Except that the soil salinity of northwest follows with the spherical model, soil salinity, water content and pH in other different directions were in line with the Gaussian model. Soil salinity, water content and pH of different directions showed strong spatial autocorrelation, which were mainly affected by structural factors. Moran's I coefficient analysis proved that soil salinity in the southwest, water content and pH was stronger in spatial correlation than that in the other directions. The natural factors including winds, drought, water scarcity, desertification, salinization, and human activities including diversion cofferdam, planting drought-resistant and salt-tolerant plants, were contributed to sampling area of Lake Ebinur wetland spatial distribution of surface soil salinity, water content and pH. Soil in the Lake Ebinur wetland is mainly of saline-based type, followed by the type of severe salinization and more serious salinization.

• We examined soil moisture relationships with forest structure at three spatial scales. • Our three study sites on sandy soils in the Netherlands include four density treatments. • We used terrestrial laser scanning to derive canopy cover, plant area index, and other attributes. • The crown-scale explained most variation in soil moisture across structures and time. Managing forest openness can enhance drought resilience during dry, hot summer periods by reducing competition for soil moisture among trees. The purpose of our study was to better understand how different components of forest structure influence soil moisture variability. In our study, we utilized Terrestrial Laser Scanning to quantify the relationships between five forest structural attributes and the spatial distribution of soil moisture within experimental forest plots dominated by Douglas fir, Scots pine, and common beech in The Netherlands. In these plots the canopy openness ranged from 0, 20, 80 to 100 %. Observations were conducted during the hot and dry summer of 2022. Our findings revealed that all forest structural attributes related negatively with soil moisture and that the crown features canopy cover and plant area index predicted between 30 and 60 % of the spatial variability of soil moisture. In addition, these crown features consistently predicted 17 % more variation than the trunk-centred features basal area, stem density, and a density-dependent competition index. The crown -scale, corresponding to the crown radius of the dominant tree species, consistently explained most variation in soil moisture across species and time. We expect that tree water uptake is the primary factor influencing the spatial variability of soil moisture, rather than throughfall or interception, yet direct measurements of these processes are needed to substantiate this. This study suggests that forest management could benefit from information on forest structural attributes to guide tree harvest and improve soil moisture availability, contributing to developing a climate-smart forest management strategy.

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.

Satellite remote sensing has been providing passive microwave soil moisture (SM) retrievals of a global spatial coverage and a high revisit frequency for research and applications in earth and environmental sciences, specifically after the soil moisture active and passive (SMAP) was launched in 2015. But, the spatial resolution of SM data is restricted to tens of kilometers, which is insufficient for regional or watershed scale studies. In this article, an SM downscaling algorithm was developed based on the vegetation modulated apparent thermal inertia relationship between SM and changes in land surface temperature (LST). The algorithm used data sets from the North America Land Data Assimilation System Noah model outputs and the advanced very high resolution radiometer data of the long term data record from 1981 to 2018. Here, the downscaling model was applied to visible&#x002F;infred LST data from the visible infrared imaging radiometer suite at 400-m and the moderate resolution imaging spectroradiometer at 1 km to downscale the L2 radiometer half-orbit 9 km SMAP SM from 2018 to 2019 for the contiguous United States. The 400-m&#x002F;1-km downscaled SM products were validated using 125 <italic>in situ</italic> SM ground measurements acquired from the International Soil Moisture Network. The validation results summarized by SM network show that the overall unbiased RMSE for 400-m of the improved&#x002F;original downscaling algorithms and 1-km SM outperform 9-km SM by 0.01, 0.007, and 0.012 m³&#x002F;m³ volumetric soil moisture, respectively, which indicates a fairly good performance of the downscaling algorithm. It is also found that precipitation has an impact on the 9-km SMAP SM.

Effect of vegetation index choice on soil moisture retrievals via the synergistic use of synthetic aperture radar and optical remote sensing

Spatial and temporal soil moisture and drought variability in the Upper Colorado River Basin

&lt;p&gt;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&lt;sub&gt;max&lt;/sub&gt;, LST&lt;sub&gt;min&lt;/sub&gt;), indicate that LST&lt;sub&gt;max &lt;/sub&gt;andLST&lt;sub&gt;min&lt;/sub&gt; 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&lt;sub&gt;NDWI-LST&lt;/sub&gt;, 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. &lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;