Soil moisture (SM) plays a central role in water cycle dynamics and land-atmosphere interactions, acting across local and regional scales. Few studies have explored the use of the ground-based global navigation satellite system reflectometry (GNSS-R) interference pattern technique (IPT) for SM estimation. In these studies, SM was estimated from the GPS elevation angle where lower reflectivity occurs (notch), which is difficult to determine in real GNSS-R interference power (IP) acquisitions. This study introduces the use of IP amplitude at vertical polarization (V-pol), readily extracted from the IP oscillations, as an alternative for SM estimation beneath vegetation cover. An empirical model was developed for estimating SM in irrigated grassland using a GNSS-R receiver with a linearly polarized antenna. The experiment, conducted between June 6 and August 8, 2022, covered the grassland’s growth phase and preharvesting and postharvesting. The study incorporated normalized difference water index (NDWI) from the Sentinel-2 satellite to account for vegetation’s impact on IP amplitude. Results indicated that the IP amplitude at V-pol accurately estimates SM (RMSE =0.04 m3/m3). Moreover, the results show that the vegetation layer mainly attenuates the IP amplitude with a nonsignificant scattered contribution to the IP, allowing for the simplification of the empirical model by ignoring the scattered contribution of vegetation. The simplified empirical model can be numerically resolved to estimate the NDWI if the SM is known. In summary, this study highlights the effectiveness of the ground-based IPT for close-range sensing of SM and a biomass proxy, such as NDWI.

Soil moisture (SM) is an important state variable in land surface models. Here, we investigate the potential of a ground-based global navigation satellite system receiver with two linearly polarized antennas that measure the interference power (IP) of direct and reflected signals in horizontal polarization (H-pol) and vertical polarization (V-pol) to estimate SM. The coefficient of determination between the IP waveforms at H-pol and V-pol ($\boldsymbol {R}_{ \boldsymbol {v}\mathbf {/} \boldsymbol {h}}^{\mathbf {2}}$) was used as a predictor of SM. A coherent specular reflection model was employed to first explore the relationship between$\boldsymbol {R}_{ \boldsymbol {v}\mathbf {/} \boldsymbol {h}}^{\mathbf {2}}$and SM for different values of soil roughness. That relationship was subsequently applied to estimate SM from$\boldsymbol {R}_{ \boldsymbol {v}\mathbf {/} \boldsymbol {h}}^{\mathbf {2}}$determined from global positioning system (GPS) signals acquired continuously by a ground-based receiver between May and December 2022 for an area with very smooth bare soil. The results show that the proposed method can estimate the SM of the upper 10-cm layer with high accuracy (with a root-mean-square error (RMSE) of approximately 1.5 vol.%) and demonstrate the potential of the ground-based IP technique as a practical system solution for proximal remote sensing of SM over bare soils.

Sentinel-1 backscatter observations were assimilated into the Global Land Evaporation Amsterdam Model (GLEAM) using an ensemble Kalman filter. As a forward operator, which is required to simulate backscatter from soil moisture and leaf area index (LAI), we evaluated both the traditional water cloud model (WCM) and the support vector regression (SVR). With SVR, a closer fit between backscatter observations and simulations was achieved. The impact on the correlation between modeled andin situsoil moisture measurements was similar when assimilating the Sentinel data using WCM ($\Delta R = +0.037$) or SVR ($\Delta R = +0.025$).

In recent years, there has been a push to increase agricultural productivity together with water efficiency. The most viable means to achieve this goal is by employing remote sensing technologies. Currently, multiple satellite platforms can provide the spatial information to crop water use models, each of them having diverse spatial resolutions and radiometric characteristics. Here we analyze the spatial scale effects on crop water use (via evaporation) from three satellite platforms (Landsat 8, Sentinel 2, and CubeSats) finding that each estimate shows different spatial variability and that flux aggregation can result in relative mean absolute error up to 120%.

Very high-resolution satellite imagery from the latest generation commercial platforms provides an unprecedented capacity for imaging the Earth with very high spatial detail. However, these data are generally expensive, particularly if large areas or temporal sequences are required. In recent years, lower quality imagery has been enabled through the launch of constellations of small satellites with short revisit time. In this article, we apply for the first time a statistical approach to downscale and bias-correct these multispectral satellite data using the information contained in a limited training set of very high-resolution images. The technique, based on the direct sampling algorithm, aims at extending the coverage of high-resolution images by sampling data from a training data set, where similar lower resolution data patterns are found. Unlike the majority of the current downscaling techniques, the approach does not require colocated fine-resolution data, but it is based on the use of training images similar to the target zone. A novel specific setup is proposed, which is adaptive to different types of landscapes with no additional user effort. The results show that the proposed technique can generate more realistic images than the traditional approaches based on the parametric bias correction and bicubic interpolation. In particular, properties such as the intensity histogram, spatial correlation, and connectivity are accurately preserved. The proposed approach can be used to extend the footprint of the high-resolution images to generate new time frames or to downscale the remote sensing imagery based on a distant but structurally similar training image.

Developments in computer vision, such as structure from motion and multiview stereo reconstruction, have enabled a range of photogrammetric applications using unmanned aerial vehicles (UAV)-based imagery. However, some specific cases still present reconstruction challenges, including survey areas composed of steep, overhanging, or vertical rock formations. Here, the suitability and geometric accuracy of four UAV-based image acquisition and data processing scenarios for topographic surveying applications in complex terrain are assessed and compared. The specific cases include the use of: 1) nadir imagery; 2) nadir and oblique imagery; 3) nadir and façade imagery; and 4) nadir, oblique, and façade imagery to reconstruct a topographically complex natural surface. Results illustrate that including oblique and façade imagery to supplement the more traditional nadir collections significantly improves the geometric accuracy of point cloud data reconstruction by approximately 35% when assessed against terrestrial laser scanning data of near-vertical rock walls. Most points (99.41%) had distance errors of less than 50 cm between the point clouds derived from the nadir imagery and nadir–oblique–façade imagery. Apart from delivering enhanced spatial resolution in façade details, the geometric accuracy improvements achieved from integrating nadir, oblique, and façade imagery provide value for a range of applications, including geotechnical and geohazard investigations. Such gains are particularly relevant for studies assessing rock integrity and stability, and engineering design, planning, and construction, where information on the position of rock cracks, joints, faults, shears, and bedding planes may be required.

The hyperspectral satellite sensing capacity is expected to increase substantially in the near future with the planned deployment of hyperspectral systems by both space agencies and commercial companies. These enhanced observational resources will offer new and improved ways to monitor the dynamics and characteristics of terrestrial ecosystems. This study investigates the utility of time series of hyperspectral imagery, acquired by Hyperion onboard EO-1, for quantifying variations in canopy chlorophyll (Chlc), plant productivity, and yield over an intensive farming area in the desert of Saudi Arabia. Chlcis estimated on the basis of predictive multi-variate empirical models established via a machine learning approach using a training dataset of in-situ measured target variables and explanatory hyperspectral indices. Resulting time series of Chlcare translated into Gross Primary Productivity (GPP) and Yield based on semi-empirical relationships, and evaluated against ground-based observations. Results indicate significant benefit in utilizing the full suite of hyperspectral indices over multi-spectral indices constructible from Landsat-8 and Sentinel-2.

This paper presents a flexible tool for spatio-temporal enhancement of coarse resolution leaf area index (LAI) products, which is readily adaptable to different land cover types, landscape heterogeneities and cloud cover conditions. The framework integrates a rule-based regression tree approach for estimating Landsat-scale LAI from existing 1 km resolution LAI products, and the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to intelligently interpolate the downscaled LAI between Landsat acquisitions. Comparisons against in-situ records of LAI measured over corn and soybean highlights its utility for resolving sub-field LAI dynamics occurring over a range of plant development stages.

Over the last few decades, the Kingdom of Saudi Arabia (KSA) has witnessed a dramatic expansion of its agricultural sector. In common with many other developing countries, this has been driven by a combination of population increases and the related effects on consumption as well as a demand for increased food security. Inevitably, the sector growth has come at the expense of a parallel increase in water consumption. Indeed, it is estimated that more than 80% of all of the water used in the Kingdom relates to agricultural production. More concerning is that the vast majority of this water is derived from non-renewable fossil groundwater extraction. To exacerbate the problem, groundwater extraction is largely unmonitored, meaning that there is very little accounting of water use on a routine basis. In the absence of techniques to directly quantify abstractions related to agriculture at large spatial scales, a mechanism for inferring crop water use as an indirect surrogate is required.

Evaporation provides the link between the energy and water budgets at the land surface. Accurate measurements of evaporation rates at large spatial scales are central to understanding land and atmosphere feedback. However, with the paucity of available surface observations for many portions of the globe, the use of modeled evaporation using satellite-based remotely sensed inputs is a potentially viable surrogate. The surface energy balance system (SEBS), which estimates atmospheric turbulent heat fluxes and evaporative fraction using satellite derived radiation fluxes and surface temperatures coupled with near-surface meteorological variables, is used to estimate surface energy fluxes over the Oklahoma region of the USA during the warm season. These simulations are assessed by comparison with observations from the ARM-CART energy balance Bowen ratio (EBBR).