Search

Mapping soil organic carbon (SOC) stock can serve as a resilience indicator for climate change. As part of the carbon dioxide (CO2) sink, soil has recently become an integral part of the global carbon agenda to mitigate climate change. We used hyperspectral remote sensing to model the SOC stock in the Sele River plain located in the Campania region in southern Italy. To this end, a soil spectral library (SSL) for the Campania region was combined with an aerial hyperspectral image acquired with the AVIRIS–NG sensor mounted on a Twin Otter aircraft at an altitude of 1433 m. The products of this study were four raster layers with a high spatial resolution (1 m), representing the SOC stocks and three other related soil attributes: SOC content, clay content, and bulk density (BD). We found that the clay minerals' spectral absorption at 2200 nm has a significant impact on predicting the examined soil attributes. The predictions were performed by using AVIRIS–NG sensor data over a selected plot and generating a quantitative map which was validated with in situ observations showing high accuracies in the ground-truth stage (OC stocks [RPIQ = 2.19, R2 = 0.72, RMSE = 0.07]; OC content [RPIQ = 2.27, R2 = 0.80, RMSE = 1.78]; clay content [RPIQ = 1.6 R2 = 0.89, RMSE = 25.42]; bulk density [RPIQ = 1.97, R2 = 0.84, RMSE = 0.08]). The results demonstrated the potential of combining SSLs with remote sensing data of high spectral/spatial resolution to estimate soil attributes, including SOC stocks. [ABSTRACT FROM AUTHOR]

The continuous monitoring of soil water content is commonly carried out using low-frequency capacitance sensors that require a site-specific calibration to relate sensor readings to apparent dielectric bulk permittivity (Kb) and soil water content (θ). In fine-textured soils, the conversion of Kb to θ is still challenging due to temperature effects on the bound water fraction associated with clay mineral surfaces, which is disregarded in factory calibrations. Here, a multi-point calibration approach accounts for temperature effects on two soils with medium to high clay content. A calibration strategy was developed using repacked soil samples in which the Kb-θ relationship was determined for temperature (T) steps from 10 to 40 °C. This approach was tested using the GS3 and TEROS-12 sensors (METER Group, Inc. Pullman, WA, USA; formerly Decagon Devices). Kb is influenced by T in both soils with contrasting T-Kb relationships. The measured data were fitted using a linear function θ = a K b + b with temperature-dependent coefficients a and b. The slope, a(T), and intercept, b(T), of the loam soil were different from the ones of the clay soil. The consideration of a temperature correction resulted in low RMSE values, ranging from 0.007 to 0.033 cm3 cm−3, which were lower than the RMSE values obtained from factory calibration (0.046 to 0.11 cm3 cm−3). However, each experiment was replicated only twice using two different sensors. Sensor-to-sensor variability effects were thus ignored in this study and will be systematically investigated in a future study. Finally, the applicability of the proposed calibration method was tested at two experimental sites. The spatial-average θ from a network of GS3 sensors based on the new calibration fairly agreed with the independent area-wide θ from the Cosmic Ray Neutron Sensor (CRNS). This study provided a temperature-corrected calibration to increase the accuracy of commercial sensors, especially under dry conditions, at two experimental sites. [ABSTRACT FROM AUTHOR]

We propose practical guidelines to predict biome-specific potential evapotranspiration (ETp) from the knowledge of grass-reference evapotranspiration (ET0) and a crop coefficient (Kc) in Mongolia. A paucity of land-based weather data hampers use of the Penman–Monteith equation (FAO-56 PM) based on the Food and Agriculture Organization (FAO) guidelines to predict daily ET0. We found that the application of the Hargreaves equation provides ET0 estimates very similar to those from the FAO-56 PM approach. The Kc value is tabulated only for crops in the FAO-56 guidelines but is unavailable for steppe grasslands. Therefore, we proposed a new crop coefficient, Kc adj defined by (a) net solar radiation in the Gobi Desert (Kc adjD) or (b) leaf area index in the steppe region (Kc adjS) in Mongolia. The mean annual ETp obtained using our approach was compared to that obtained by FAO-56 guidelines for forages (not steppe) based on tabulated Kc values in 41 locations in Mongolia. We found the differences are acceptable (RMSE of 0.40 mm d−1) in northern Mongolia under high vegetation cover but rather high (RMSE of 1.69 and 2.65 mm d−1) in central and southern Mongolia. The FAO aridity index (AI) is empirically related to the ETp/ET0 ratio. Approximately 80% and 54% reduction of ET0 was reported in the Gobi Desert and in the steppe locations, respectively. Our proposed Kc adj can be further improved by considering local weather data and plant phenological characteristics. [ABSTRACT FROM AUTHOR]

Water infiltration rate (WIR) into the soil profile was investigated through a comprehensive study harnessing spectral information of the soil surface. As soil spectroscopy provides invaluable information on soil attributes, and as WIR is a soil surface-dependent property, field spectroscopy may model WIR better than traditional laboratory spectral measurements. This is because sampling for the latter disrupts the soil-surface status. A field soil spectral library (FSSL), consisting of 114 samples with different textures from six different sites over the Mediterranean basin, combined with traditional laboratory spectral measurements, was created. Next, partial least squares regression analysis was conducted on the spectral and WIR data in different soil texture groups, showing better performance of the field spectral observations compared to traditional laboratory spectroscopy. Moreover, several quantitative spectral properties were lost due to the sampling procedure, and separating the samples according to texture gave higher accuracies. Although the visible near-infrared–shortwave infrared (VNIR–SWIR) spectral region provided better accuracy, we resampled the spectral data to the resolution of a Cubert hyperspectral sensor (VNIR). This hyperspectral sensor was then assembled on an unmanned aerial vehicle (UAV) to apply one selected spectral-based model to the UAV data and map the WIR in a semi-vegetated area within the Alento catchment, Italy. Comprehensive spectral and WIR ground-truth measurements were carried out simultaneously with the UAV–Cubert sensor flight. The results were satisfactorily validated on the ground using field samples, followed by a spatial uncertainty analysis, concluding that the UAV with hyperspectral remote sensing can be used to map soil surface-related soil properties. [ABSTRACT FROM AUTHOR]

The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in situ Earth observations (EOs): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources? An integrative information flow (i.e., iAqueduct theoretical framework) is developed to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources. The integrated iAqueduct framework aims to address the abovementioned scientific questions by combining medium-resolution (10 m–1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations, analytical- and physical-based models, as well as big-data analytics with machine learning algorithms. This paper provides a general overview of the iAqueduct theoretical framework and introduces some preliminary results. [ABSTRACT FROM AUTHOR]

Assessing the relationship between climate forcings and groundwater recharge (GR) rates in semi-arid regions is critical for water resources management. This study presents the impact of climate forecasts on GR within a probabilistic framework in a site-specific study in the Nebraska Sand Hills (NSH), the largest stabilized sand dune region in the USA containing the greatest recharge rates within the High Plains Aquifer. A total of 19 downscaled climate projections were used to evaluate the impact of precipitation and reference evapotranspiration on GR rates simulated by using HYDRUS 1-D. The analysis of the decadal aridity index (AI) indicates that climate class will likely remain similar to the historic average in the RCP2.6, 4.5, and 6.0 emission scenarios but AI will likely decrease significantly under the worst-case emission scenario (RCP8.5). However, GR rates will likely decrease in all of the four emission scenarios. The results show that GR generally decreases by ~25% under the business-as-usual scenario and by nearly 50% in the worst-case scenario. Moreover, the most likely GR values are presented with respect to probabilities in AI and the relationship between annual-average precipitation and GR rate were developed in both historic and projected scenarios. Finally, to present results at sub-annual time resolution, three representative climate projections (dry, mean and wet scenarios) were selected from the statistical distribution of cumulative GR. In the dry scenario, the excessive evapotranspiration demand in the spring and precipitation deficit in the summer can cause the occurrence of wilting points and plant withering due to excessive root-water-stress. This may pose significant threats to the survival of the native grassland ecology in the NSH and potentially lead to desertification processes if climate change is not properly addressed. [ABSTRACT FROM AUTHOR]