Bare Soil Surface Research Articles

An Impervious Surface Spectral Index on Multispectral Imagery Using Visible and Near-Infrared Bands

The accurate mapping of urban impervious surfaces from remote sensing images is crucial for understanding urban land-cover change and addressing impervious-surface-change-related environment issues. To date, the authors of most studies have built indices to map impervious surfaces based on shortwave infrared (SWIR) or thermal infrared (TIR) bands from middle–low-spatial-resolution remote sensing images. However, this limits the use of high-spatial-resolution remote sensing data (e.g., GaoFen-2, Quickbird, and IKONOS). In addition, the separation of bare soil and impervious surfaces has not been effectively solved. In this article, on the basis of the spectra analysis of impervious surface and non-impervious surface (vegetation, water, soil and non-photosynthetic vegetation (NPV)) data acquired from world-recognized spectral libraries and Sentinel-2 MSI images in different regions and seasons, a novel spectral index named the Normalized Impervious Surface Index (NISI) was proposed for extracting impervious area information by using blue, green, red and near-infrared (NIR) bands. We performed comprehensive assessments for the NISI, and the results demonstrated that the NISI provided the best studied performance in separating the soil and impervious surfaces from Sentinel-2 MSI images. Furthermore, regarding impervious surfaces mapping accuracy, the NISI had an overall accuracy (OA) of 89.28% (±0.258), a producer’s accuracy (PA) of 89.76% (±1.754), and a user’s accuracy (UA) of 90.68% (±1.309), which were higher than those of machine learning algorithms, thus supporting the NISI as an effective measurement for urban impervious surfaces mapping and analysis. The results indicate the NISI has a high robustness and a good applicability.

Inversion model of surface bare soil temperature and water content based on UAV thermal infrared remote sensing

Soil moisture content is important to agricultural monitoring, ecological restoration and landslide warning. Traditional methods cannot monitor soil moisture content in large area, high precision and continuous data. Unmanned aerial vehicle (UAV) remote sensing monitoring has enabled wide-scale water content monitoring, improving the spatial resolution accuracy to centimeter level and the temporal resolution accuracy to minute level, which makes UAV remote sensing favorable for soil moisture content monitoring. In this paper, to achieve rapid and accurate inversion data of bare surface soil moisture content via UAV thermal infrared remote sensing, UAV is used to obtain a visible light map of the experiment area and a thermal infrared radiation map at 9:00 am, 1:00 pm, and 6:00 pm in a given day. Gray value of thermal infrared radiation is based on Planck’s blackbody radiation law and quadratic polynomial fitting to invert temperature data; the ground soil temperature and humidity sensor provides sample areas of the measured temperature and soil moisture content. The linear relationship between gray value of thermal infrared radiation, brightness temperature, measured temperature, and soil moisture content was obtained by analyzing the data. The experimental results show that there is a good linear relationship between the brightness temperature and water content, and the brightness t temperature R2 at 6 PM is better than the measured one. Finally, a model for calculating the surface bare soil moisture content based on brightness temperature is established. RMSE and RPD were used to verify the accuracy of the inversion model. The model accuracy verification results show that the inversion model at 6 pm has a good accuracy, which verifies the feasibility and accuracy of unmanned aerial vehicle thermal infrared remote sensing monitoring of surface bare soil moisture content.

Effects on Fluvial Geomorphology and Vegetation Cover following Hydroelectric Power Plant Operation: A Case Study in the Maule River (Chile)

The installation of hydroelectric plants has generated multiple environmental impacts on the world’s river systems. In central Chile, the impacts of hydroelectric reservoir operation have been documented in ecological and hydrologic regime terms. This investigation assesses the changes in channel morphology, vegetation distribution, and flows in the middle section of the Maule River during the period following the start-up of a hydroelectric plant. Changes in fluvial morphology (active area) and land cover are quantified using LANDSAT images, contrasted with a vegetation sampling and flow analysis. The results show a 12% decrease in active areas of the river, indicating a loss of geomorphological diversity. Within the active channel, there was a gradual increase in plant-covered surface area, which reached 159% between 1989 and 2018, mainly due to reductions in water (−61%), active bar (−35%), and bare soil surface areas (−29%). The changes were evident ten years after plant operations began and intensified during the period known as the megadrought in central Chile (2008–2018). The flow magnitudes present a decrease for exceedance probabilities (P) below 85% in the period after 1985, with a slight increase recorded for low flows (P > 85%). In the segments with superior stabilization, invasive species such as Acacia dealbata (silver wattle) predominated, which are specialists at taking advantage of disturbances to settle and stabilize active areas, narrowing the possibilities for morphological change.

Recognition of the Bare Soil Using Deep Machine Learning Methods to Create Maps of Arable Soil Degradation Based on the Analysis of Multi-Temporal Remote Sensing Data

The detection of degraded soil distribution areas is an urgent task. It is difficult and very time consuming to solve this problem using ground methods. The modeling of degradation processes based on digital elevation models makes it possible to construct maps of potential degradation, which may differ from the actual spatial distribution of degradation. The use of remote sensing data (RSD) for soil degradation detection is very widespread. Most often, vegetation indices (indicative botany) have been used for this purpose. In this paper, we propose a method for constructing soil maps based on a multi-temporal analysis of the bare soil surface (BSS). It is an alternative method to the use of vegetation indices. The detection of the bare soil surface was carried out using the spectral neighborhood of the soil line (SNSL) technology. For the automatic recognition of BSS on each RSD image, computer vision based on deep machine learning (neural networks) was used. A dataset of 244 BSS distribution masks on 244 Landsat 4, 5, 7, and 8 scenes over 37 years was developed. Half of the dataset was used as a training sample (Landsat path/row 173/028). The other half was used as a test sample (Landsat path/row 174/027). Binary masks were sufficient for recognition. For each RSD pixel, value “1” was set when determining the BSS. In the absence of BSS, value “0” was set. The accuracy of the machine prediction of the presence of BSS was 75%. The detection of degradation was based on the average long-term spectral characteristics of the RED and NIR bands. The coefficient Cmean, which is the distance of the point with the average long-term values of RED and NIR from the origin of the spectral plane RED/NIR, was calculated as an integral characteristic of the mean long-term values. Higher long-term average values of spectral brightness served as indicators of the spread of soil degradation. To test the method of constructing soil degradation maps based on deep machine learning, an acceptance sample of 133 Landsat scenes of path/row 173/026 was used. On the territory of the acceptance sample, ground verifications of the maps of the coefficient Cmean were carried out. Ground verification showed that the values of this coefficient make it possible to estimate the content of organic matter in the plow horizon (R2 = 0.841) and the thickness of the humus horizon (R2 = 0.8599). In total, 80 soil pits were analyzed on an area of 649 ha on eight agricultural fields. Type I error (false positive) of degradation detection was 17.5%, and type II error (false negative) was 2.5%. During the determination of the presence of degradation by ground methods, 90% of the ground data coincided with the detection of degradation from RSD. Thus, the quality of machine learning for BSS recognition is sufficient for the construction of soil degradation maps. The SNSL technology allows us to create maps of soil degradation based on the long-term average spectral characteristics of the BSS.

NDBSI: A normalized difference bare soil index for remote sensing to improve bare soil mapping accuracy in urban and rural areas

As a component of the Earth’s land surface, bare soil is an important indicator of urbanization and therefore plays an irreplaceable role in regional ecosystems. Given that the spectral signatures of bare soil are complicated and easily confused with impervious surfaces, few soil spectral indices have been purposed and widely used. In this study, we developed a soil index, the normalized difference bare soil index (NDBSI), by analyzing the spectral characteristics of multiple soil types. Using Landsat 8 OLI images from six urban and rural areas in China, we found that NDBSI was more effective in mapping bare soil than currently used indices (i.e. bare soil index (BI), product index for dark soil (PIDS), and biophysical composition index (BCI)), especially in distinguishing between bare soil and impervious surface. Moreover, NDBSI showed good performance in identifying bare soil from red brick, which is helpful for soil mapping in industrial urban areas, where red brick is widely used as roofing materials. Our study may be the first attempt to develop a soil index considering the spectral characteristics of various soil types and verify its effectiveness in multiple regions. More validation of NDBSI with remote sensing images with different spatial resolution will facilitate its applications, and ultimately promote more accurate soil mapping in the future.

Brightness temperature and emissivity retrieved from a portable L-band radiometer

ABSTRACT L-band microwave radiometry is one of the globally accepted techniques to estimate surface soil moisture by measuring upwelling brightness temperature (TB) from the surface. L-band radiometers have been historically used for ground truth validation of space-borne missions. Existing devices are distinguished by using huge and bulky antennas to conduct precise multi-angular measurements at the cost of portability. The current work demonstrates that a portable ground-based L-band microwave radiometer can be used to measure high-resolution TB, soil temperature and emissivity in a case study area in India as an alternative to a traditional huge ground-based radiometer. To show the influence of surface soil moisture (SM) on TB, a controlled experiment was conducted to investigate the temporal variation of TB over a bare soil surface. Spatial analysis of the measured data shows that the compact radiometer can efficiently capture the linear relationship between emissivity and TB, while temporal analysis of TB over a small area shows the variation in TB dynamics with SM.

Influence of short-term surface temperature dynamics on tree orchards energy balance fluxes

Land surface temperature (LST) plays an essential role in developing and applying precision agriculture protocols, especially for calculating crop evapotranspiration (ETc) by surface energy balance (SEB) approaches; and for determining crop water status. However, LST is quite dependent on the meteorological conditions, which can rapidly vary. This variability, together with the limited meterological data acquisition frequency in most weather stations, can lead to the miscalculation of the SEB components, especially relevant when used for irrigation purposes.The present study assessed the temporal dynamic of LST in a very short period of time (20-minutes) through the acquisition of multiple thermal imagery. Additionally, a combination of SEB approach with Eddy Covariance technique was performed for quantifying the effect that LST variations have on the sensible (H) and latent (LE) heat fluxes.Even under steady meteorological conditions, temporal variations in LST of 3.5 and 4.0 K were observed for tree canopy and sunny bare soil surfaces, respectively. These LST oscillations reached values of about 7.8 and 17.9 K for tree canopies and bare soil when heterogeneous meteorological conditions were observed (i.e. cloud presence). Such LST differences translated into H and LE differences of about 26 and 19%, respectively; with variations up to 5 (for H) and 2.7 times (for LE) under fast-varying meteorological conditions.The obtained results suggest the necessity of acquiring thermal imagery when steady meteorological conditions exist or, otherwise, ensuring the collection of instantaneous meteorological data for applying post-processing corrections. This is of importance when incorporating the obtained ETc maps into precision irrigation protocols.

THE CONTRIBUTION OF RADAR REMOTE SENSING VIA SENTINEL-1 DATA AND PHOTO-INTERPRETATION BY GOOGLE EARTH IMAGES FOR WETLAND MAPPING

Abstract. Wetlands are considered as sensitive ecosystems exposed and threatened by climate change and the urbanization of natural environments. In the purpose of managing these sensitive areas and conservatizing their biodiversity, remote sensing is an efficient way to track environmental variables over large areas as wetlands. However, when it comes to the study of hydrologic dynamics, high temporal and spatial resolutions are essential. Since the access to optical satellite imagery is restrictive because of the large cloud cover that masks the ground, radar sensors that are working in the microwave field, are particularly suited to the characterization of hydrological dynamics due to the sensitivity of their measurements in the presence of water, regardless of the vegetation in place. Recently, radar remote sensing has experienced a real revolution with the launch of the Sentinel-1A satellite in 2014, followed by its twin Sentinel-1B two years later by the European Space Agency as part of the Copernicus program. These sensors acquire C-band data (λ = 5.6 cm) with a temporal resolution of 12 days by satellite and their distribution is open and free. This article aims to assess the potential of Sentinel A1 SAR data for wetland mapping in the city of Benslimane (Central Morocco). The first part is explaining the methodology for mapping water surfaces. We identified a confusion of the C-band radar response of water surfaces and that of certain bare soils. We then showed that the VH polarization is the most suitable for the mapping of water surfaces, comparing four methods of detecting areas in water. It. The second part is discussing the use of unsupervised methods without a priori data demonstrating that the methods taking into account the spatial neighborhood give better results. Temporal filtering has been developed and has made it possible to improve detection and to overcome confusion between bare soil and permanent water surfaces. Water surfaces larger than 0.5 ha are at 80% detected. Classification was performed using the SVM (Support Vector Machine) algorithm. This latter information was then implemented into the thematic map derived from SPOT-4 images to obtain the final weltands map.

Soil Moisture Retrieval Over Bare Soil Surface From Single-Polarization SAR Data by Combining Neighborhood Pixels

The objective of this letter is to extend a method proposed by Kweon et al. to retrieve soil moisture (m_v) over bare soil surface by combining neighborhood pixels of single-polarization synthetic aperture radar (SAR) data. This letter uses single-polarization (HH, VV) SAR data to simultaneously retrieve the root-mean-square (rms) height (h_rms) and the real part of the relative dielectric constant (ε_s) which can be converted to soil moisture content. For the copolarization SAR data, the letter first uses the Integral Equation Model (IEM) and the semiempirical calibration of the correlation length (L) to obtain the probability distribution curve of rms height and the real part of the relative dielectric constant for each neighborhood pixel. Then, these probability distribution curves are placed on the ε_s-h_rms plane, and the juxtaposition model is applied to obtain the average value of estimations of neighborhood pixels. The average soil moisture estimations of neighborhood pixels in farmlands are compared with the in-situ measurements with the RMSE equal to 0.036 cm³/cm³ and the correlation coefficient equal to 0.84 at VV polarization in the L band, which demonstrates that the proposed method is suitable to invert soil moisture with acceptable accuracy and high resolution. However, volume scattering contribution from crops can decrease the performance of the proposed method.

Pattern of distribution of vegetation and functionality of the ecosystem after a soil conservation action229

This work analyzes whether the soil conservation and reforestation actions carried out three years ago in the study area are reflectedin a better functionality of the landscape. The functionality of the landscape will be evaluated partially using the method known asLandscape Function Analysis (LFA) under the precept that a landscape is functional when it is able to retain its resources (soil,water, nutrients) regardless of internal redistributions. The “Las Barretas” ejido, where this research was carried out, is located 12km east of the city of Linares, Nuevo León. One hundred and thirty transects were established to obtain the percentage of totalcoverage and relative frequency of each type of coverage. Likewise, the attributes that contribute to interrupting, diverting orabsorbing surface runoff and transported materials were evaluated. To achieve this, the length and width of the vegetated patchesand the length of the interpatches without vegetation were measured. More bare soil (61%) and more rock presence (27%) wererecorded in the control area than in the area with soil conservation works. Herbaceous and pasture covers were slightly higher in thearea with conservation actions than in the control. The average length and width of the patches are significantly greater in the controlarea, however the average length of the interpatches is also greater, which means a greater surface of bare soil exposed to risk oferosion. The area with soil conservation actions has smaller but more continuous patches, the foregoing is evidenced by the higherBare Soil Index that resulted in the area without conservation works. The coverage at ground level (herbaceous, litter, pasture)seems to be increasing in the areas where soil conservation actions were carried out.

Dual-Channel Convolutional Neural Network for Bare Surface Soil Moisture Inversion Based on Polarimetric Scattering Models

The polarimetric synthetic aperture radar (PolSAR) can be used to obtain soil moisture by inverting scattering models at high resolution. The convolutional neural network (CNN) has been recently introduced to estimate soil roughness for PolSAR data, which need to be driven by a large amount of data. In this paper, a dual-channel CNN based on polarimetric models is proposed for soil moisture inversion, and it aims to further expand the applicable range of roughness in the X-Bragg model by integration with the integral equation model (IEM). Meanwhile, it fully utilizes the spatial information of PolSAR images to relax the number of required training samples when real data on the surface are difficult to obtain. Besides, we designed a framework based on this network. Coarse-grained inversion and fine-grained inversion of soil moisture are carried out through the qualitative classification network and the quantitative regression network, respectively. Experiments on simulated and airborne E-SAR data show that the proposed network can accurately fit the nonlinear relationship between polarization parameters and soil moisture, so as to improve the inversion accuracy with a small number of samples. In our experiments, the average inversion accuracy reached 95.39%, and the root mean square error (RMSE) of the regression network was 0.98%. This method can be applied to a wide range of soil moisture monitoring applications.

Modeling Transient Evaporation From Porous Media as a Succession of Steady‐State Steps

AbstractEvaporation from bare soil surfaces is a natural flow process of global importance. During drying, significant temporal changes in flow rates occur at the soil surface, accompanied by temporal and spatial variations in the partitioning of liquid and vapor water distributions below it. We present a novel approach to modeling the transient process of evaporation from porous media as a succession of steady‐state solutions. The governing mechanisms during this dynamic process are captured by accounting for the hydraulic properties of the drying medium, the characteristic features of the medium that control water flow, the atmospheric forcing, and the partitioning between the liquid and vapor phases of the water within the drying profile. We validate this simplified approach using data from a numerical model and from evaporation experiments in different soil types, under various ambient conditions. Using the model, we demonstrate the effect of soil hydraulic properties on the evaporation process. Furthermore, we use the flexibility of the model for evaluating evaporation under different initial and boundary conditions. The predictive capabilities of the proposed analytical model indicate that it is a reliable tool for representing the evaporation process.

Artificial biocrust establishment on materials of potash tailings piles along a salinity gradient

Biocrust communities provide a pallet of ecosystem services, such as soil stabilization, altering of hydrological cycles and primary production, and often are the first colonizers of unvegetated surfaces during succession. Therefore, artificially establishing biocrusts can improve soil properties, for example, by stabilizing bare soil surfaces against erosion or by accumulating nutrients. In this study, the establishment of artificial biocrusts was tested for the restoration of potash tailings piles that result from potash fertilizer production and mostly consist of NaCl. A biocrust cover as primary vegetation could decrease the saline seepage waters by trapping rainwaters, thereby reducing the environmental pollution. In a laboratory experiment, we created a salt gradient by mixing the tailings materials with non-saline dune sand. Surface material of the abandoned potash tailings pile Neuhof-Ellers (NE) and material of the Infiltration Hampering Stratum (IHS) were tested, along with a treatment with bone charplus (BCplus) and sodium alginate. A mixture of 50% (w/w) IHS and dune sand was most successful for the establishment of green biocrust microalgae, based on increased biomass and photosynthetic performance. The chlorophyll a content was negatively correlated with the electrical conductivity (EC), and was significantly increased in the BCplus and sodium alginate treatment, while biocrusts failed to establish on pure tailings piles substrates. The limit of the substrates EC for biocrust establishment was 35 mS cm−1. This limit provides a baseline for future studies that should use BCplus and sodium alginate to increase the success of biocrust establishment on potash tailings piles.

Livestock grazing effect on the hydraulic properties of gypseous soils in a Mediterranean region

Although is well known that grazing has a significant effect on the soil hydraulic properties and through them on the pasture productivity in semi-arid regions, there is still little data about the effect of this management on soils with high gypsum content. This work compares the effect of three increasing grazing intensities (low, medium, and high) on the hydraulic properties of the upper soil layers, measured on two areas with increasing gypsum content (56 and 73%, respectively) located in a semi-arid region of the Middle Ebro Valley (NE, Spain). 90 soil samplings were taken on bare soil surface areas. The measured soil parameters included the saturated sorptivity (Sc) and hydraulic conductivity (Kc) of the soil surface layer and the soil bulk density (ρb), saturated hydraulic conductivity (Ks), saturated volumetric water content, θs, and α and n parameters of the water retention curve of the 1–8 cm depth soil layer. Increasing gypsum content increased the soil water infiltration but did not affect the water retention capacity and total porosity. All studied gypseous soils presented low water retention. High grazing intensities decreased the soil water infiltration. While soils with high gypsum content tended to attenuate the effect of the livestock trampling on ρb, a significant influence of grazing on ρb was observed in the fields with moderate soil gypsum content. No effect of livestock trampling on the soil water retention characteristics was observed. Moderate grazing, which did not show negative effects on the soil hydro-physical properties, could be an acceptable management for soil conservation in these semiarid regions of soils with high gypsum content.

The intensity of slope and fluvial processes after a catastrophic windthrow event in small catchments in the Tatra Mountains

Strong wind events frequently result in creating large areas of windthrow, which causes abrupt environmental changes. Bare soil surfaces within pits and root plates potentially expose soil to erosion. Absence of forest may alter the dynamics of water circulation. In this study we attempt to answer the question of whether extensive windthrows influence the magnitude of geomorphic processes in 6 small second- to third-order catchments with area ranging from 0.09 km2 to 0.8 km2. Three of the catchments were significantly affected by a windthrow which occurred in December 2013 in the Polish part of the Tatra Mountains, and the other three catchments were mostly forested and served as control catchments. We mapped the pits created by the windthrow and the linear scars created by salvage logging operations in search of any signs of erosion within them. We also mapped all post-windthrow landslides created in the windthrow-affected catchments. The impact of the windthrow on the fluvial system was investigated by measuring a set of channel characteristics and determining bedload transport intensity using painted tracers in all the windthrow-affected and control catchments. Both pits and linear scars created by harvesting tend to become overgrown by vegetation in the first several years after the windthrow. The only signs of erosion were observed in 10% of the pits located on convergent slopes. During the period from the windthrow event in 2013 until 2019, 5 very small (total area <100 m2) shallow landslides were created. The mean distance of bedload transport was similar (t-test, p=0.05) in most of the windthrow-affected and control catchments. The mapping of channels revealed many cases of root plates fallen into a channel and pits created near a channel. A significant amount of woody debris delivered into the channels influenced the activity of fluvial processes by creating alternating zones of erosion and accumulation.

Using Generalized Regression Neural Network to Retrieve Bare Surface Soil Moisture From Radarsat-2 Backscatter Observations, Regardless of Roughness Effect

The combined influence of surface soil moisture and roughness on radar backscatters has been limiting SAR’s application in soil moisture retrieval. In the past research, multi-temporal analysis and artificial neural network (ANN) inversion of physically based forward models were regarded as promising methods to decouple that combined influence. However, the former does not consider soil roughness change over a relatively longer period and the latter makes it hard to thoroughly eliminate the effect of soil roughness. This study proposes to use generalized regression neural network (GRNN) to derive bare surface soil moisture (BSSM) from radar backscatter observations regardless of the effect of soil roughness (GRNN inversion of backscatter observations). This method not only can derive BSSM from radar backscatters, provided soil roughness is unknown in any long period, but also can train models based on small-size sample data so as to reduce the manual error of training data created by simulation of physically based models. The comparison of validations between BSSM-backscatter models and BSSM-roughness-backscatter models both analyzed by GRNN shows that the incorporation of soil roughness cannot raise the prediction accuracy of models and, instead, even reduce it, indicating that the combined influence is thoroughly decoupled when being analyzed by GRNN. Moreover, BSSM-backscatter models by GRNN are recommended due to their good prediction, even compared to those related models in past publications.

Towards moss biocrust effects on surface soil water holding capacity: Soil water retention curve analysis and modeling

As ecosystem engineers in drylands, biocrusts possibly generate great influences on surface soil water holding capacity and availability, but these effects have not yet been sufficiently investigated, especially through analyzing and modeling their soil water retention curve (SWRC). On the Loess Plateau of China, the naturally developed moss biocrusts and surface bare soil were sampled in five replicates on loess soil and aeolian sand respectively, and their SWRCs from saturation (soil water potential = 0 hPa) to dry (soil water potential = −15,000 hPa) were measured with a sandbox (low water potential range) in combination with a pressure plate (high water potential range). The measured SWRCs were compared to the van Genuchten (VG), Brooks and Corey (BC), and Log-Normal Distribution (LND) models, and the model with best performance was adopted to further determine the effects of biocrusts on surface soil water holding capacity and water availability through comparing the differences in soil water content (θ) at specific water potentials between the biocrusts and uncrusted soil. Our results showed that (i) the biocrusts always had higher θ as compared with the bare soil across the whole water potential range, and the increasing effects of the biocrusts was much stronger on aeolian sand (165%) in comparison to that on loess soil (15%). (ii) As compared with the bare soil, the saturated water content, field capacity, wilting point, and available water content of the aeolian sand increased by 52%, 256% (0.192 vs. 0.054 cm3 cm−3), 65%, and 1000% by the biocrusts, respectively; while that of the loess soil increased by 28%, 9% (0.240 vs.0.220 cm3 cm−3), 27%, and 8% by the biocrusts, respectively. (iii) The increased water holding capacity of the biocrusts was possibly attributed to their higher percentage (18% vs. 16%) of microscopic pores (0.3–5 μm) on the loess soil and their increasing (16% vs. 1%) microscopic pores and decreasing (18% vs. 50%) medium pores (30–75 μm) on the aeolian sand. (iv) The VG and LND models performed better (R2 > 0.98, RMSE ≤ 0.019 cm3 cm−3, RE ≤ 7.4%) than the BC model in simulating the SWRCs of the biocrusts and bare soil, and their fitting errors were mostly distributed in the high (−500 to −1000 hPa) and low (<−5000 hPa) water potential ranges. As indicated by SWRCs, we conclude that biocrusts greatly increase surface soil water holding capacity and water availability, leading in turn to a positive feedback mechanism of the biocrusted layer, which may affect surface hydropedological process (e.g., infiltration and evaporation) in drylands.

UAVSAR Observations of InSAR Polarimetric Phase Diversity: Implications for NISAR Ionospheric Phase Estimation

AbstractThe NASA‐ISRO Synthetic Aperture Radar (NISAR) is a repeat‐pass radar mission that will acquire fully polarimetric SAR data in an innovative acquisition mode known as quasi‐quad pol (QQP). It consists of simultaneously operating a HH/HV and a VH/VV dual‐pol modes in the lowest and upper parts of the transmitted frequency spectrum. Compared to more traditional acquisition modes, the ionospheric phase estimation algorithms for QQP repeat‐pass data will likely exploit the difference of two sub‐band SAR interferograms acquired not only at distinct center frequencies but also at different polarizations (e.g., HH/VV), making the ionospheric phase estimation vulnerable to possible bias caused by polarimetric interferometric scattering phase components. Using UAVSAR airborne data, we show that temporal variations of the interferometric scattering phase may introduce spurious polarimetric‐dependent phase terms which may bias QQP ionospheric phase estimates. The magnitude of this bias depends on the type of observed land cover. For bare soil and forested areas, we detect HH‐VV interferometric phase discrepancies up to 30° over 12 days, corresponding to a 10 cm bias at L‐band. Over comparable time intervals, changes in vegetation vitality introduce HH‐VV interferometric phase inconsistencies beyond 90° for vertically oriented agricultural fields. We simulate the QQP ionospheric phase screen over the San Andreas Fault, USA, a region characterized by a mixture of vegetated and bare soil surfaces. Based on the results from the UAVSAR data analysis, we recommend using the same polarization on the main and side‐bands of the NISAR operational science modes (e.g., single‐pol or dual‐pol) to avoid potential biases in the ionospheric phase estimates.

The response of photosynthetic capacity and yield of cotton to various mulching practices under drip irrigation in Northwest China

Mulched drip irrigation is regarded as an effective water-saving irrigation technique that is adopted widely in dryland regions. However, due to the lack of efficient mulch film recovery, residual plastic film fragments accumulating in soil nowadays pose a risk to agricultural production sustainability. To mitigate the pollution result from polyethylene (PE) film mulching and demonstrate the relationship between soil microenvironment, photosynthetic capacity, yield, and water use efficiency of cotton (Gossypium hirsutum L.) in various mulching practices, a two-year field experiment was carried out to test five mulching practices (sprayable degradable film mulching; biodegradable transparent film with an 80-day induction period mulching; biodegradable transparent film with a 100-day induction period mulching; conventional transparent polyethylene film mulching; no film mulching) in Xinjiang, Northwest China. The results showed that mulching significantly increased soil water content and temperature by 23.83% and 15.01% (average on two years) in topsoil compared to the bare soil surface, associated with a higher plant height, leaf area index, photosynthetic rate and aboveground biomass accumulation. Soil moisture, temperature, and crop height all significantly influenced cotton leaf photosynthetic rate, and soil moisture had the most significant impact (the direct path coefficient was 0.450). The highest cotton yield and WUE were observed in PE film mulching (5480.59 kg ha−1 and 1.35 kg m−3, respectively, average on two years) and the biodegradable transparent film with a 100-day induction period was comparable with PE film in yield increased and reduction of water loss (yield and WUE was 5357.51 kg ha−1 and 1.29 kg m−3, respectively, average on two years). The sprayable biodegradable film also had good wetting and warming effects on soil, but notably, sprayable film performances were still weaker than that in solid films. Compared to solid degradable films mulching, soil moisture, soil temperature, photosynthetic rate, yield, and WUE in sprayable film mulching was decreased by 5.55%, 7.19%, 31.41%, 5.31%, and 8.58% on average, respectively. In general, biodegradable transparent film with a 100-day induction period could significantly improve soil water-heat status, enhance cotton development, reduce water evaporation, increase cotton yield and WUE, which provides a cotton-cultivation option to control residual PE fragments pollution and maintained cotton yield.

Integration of multispectral and hyperspectral data to map magnetic susceptibility and soil attributes at depth: A novel framework

The understanding of attributes and magnetic susceptibility (χ) at soil surface, mainly subsurface, is crucial due to their role to identify climate changes, soil degradation, soil classification systems, soil fertility, and pedogenesis. The integration of proximal sensing (PS) and remote sensing (RS) data sources could increase the efficiency of Digital Soil Mapping. Nevertheless, products of this integration need to be evaluated in hybrid, stochastic, and deterministic models to predict soil attributes and χ at surface and subsurface. This study investigates the PS and RS integration by applying four deterministic (e.g. Bayesian Regularised Neural Network, Generalised Linear Model, Random Forest and Cubist) and hybrid models (e.g. Regression Kriging of residuals of the best-fitted model) to create a new environmental variable, the Best Synthetic Soil Image (BSSI), at three soil depths (e.g. 0 – 20, 40 – 60 and 80 – 100 cm) that quantitatively represent the soil spectral signature. We also used the BSSI in a comparison with bare soil surface (e.g. SYSI - Synthetic Soil Image) to predict soil attributes and χ by performing the deterministic and hybrid models. We hypothesize that the BSSI, which integrates PS and RS data, enhances soil modelling predictions at subsurface by selecting the best model approach. The BSSI demonstrated original and valuable contribution to increase the predictive model power at deeper layers, while SYSI was effective at upper layers. The PS and RS integration helped to identify the main soil patterns horizontally and vertically, which traditional soil surveys have not been capable of representing.