Estimation of volumetric surface soil moisture content using microwave-calibrated soil evaporative efficiency information

ABSTRACT

Soil moisture (SM) is a critical variable in energy and water partitioning at the interface between the land surface and atmosphere. In this study, we provided a robust method to retrieve soil moisture using optimal remotely sensed soil evaporative efficiency (SEE) information. Specifically, SEE was deduced from the triangle space constituted by remotely sensed land surface temperature (LST) and fractional vegetation cover (Fc). Theoretical solutions of the dry and wet boundaries were derived by annual-scale optimization and microwave SM calibration. The two limits of SM were obtained by linear fit function between SEE and microwave-based SM. The proposed method was validated at the Liaoning Province of China in the year 2011 by using MODerate Resolution Imaging Spectroradiometer (MODIS) and Soil Moisture and Ocean Salinity (SMOS) satellite images as input. Results indicated that the new method has not only bypassed the complex parametric scheme in the calculation of boundaries within the LST-Fc feature space but also performed superior in the estimation of soil moisture status at all-sky days. Besides, the optimal method has reproduced the spatial and temporal patterns of soil moisture reasonably well, with a root mean square error of 0.07 m³ m^?3. Therefore, the proposed method can be regarded as a suitable tool to provide accurate and continuous monitoring of soil moisture.     

Classification of crop fields in northeast Thailand based on hydrological characteristics detected by L-band SAR backscatter data

Synthetic aperture radar (SAR) backscatter amplitude image data have proven useful in estimating soil moisture levels and in approximating areas of water inundation over large regions. Based on the pattern of seasonal change in the backscatter coefficient at each image pixel, this study classified a variety of crop fields in Northeast Thailand according to their hydrological characteristics. L-band horizontal-transmit horizontal-receive (HH) polarization images from advanced land observing satellite phased array type L-band synthetic aperture radar (ALOS-PALSAR) at six dates from January to December over the rainy season (May to November) in 2007 were used. Fifteen clusters of pixels were generated using the k-means method, with five variables obtained by taking the difference between the backscatter coefficient for the dry season (January) and the other five dates, effectively removing effects of soil surface roughness. As a result, a detailed spatial distribution of hydrological characteristics that accurately reflected topographical features and hydrological conditions was obtained.     

A model for the rapid monitoring of soil salinization in the Yellow River Delta using Landsat 8 OLI imagery based on VI-SI feature space

Traditional monitoring methods often ignore the vegetation information, which has significantly indirect influence on the process of soil salinization. In this study, the vegetation indices-salinity indices (VI-SI) feature space was utilized to improve the inversion accuracy of soil salinity, while considering the bare soil and vegetation information. By fully considering the surface vegetation landscape in the Yellow River Delta, twelve VI-SI feature spaces were constructed, and two categories of soil salinization monitoring index were established. The experiment results showed that remote sensing monitoring index based on MSAVI-SI₁ had the highest inversion accuracy (coefficient of determination (R²) = 0.912), while that based on the ENDVI-SI₄ feature space had the lowest (R² = 0.664). Therefore, the remote sensing monitoring index derived from MSAVI-SI can greatly improve the dynamic and periodical monitoring of soil salinity in the Yellow River Delta.     

Estimation of soil thermal inertia from geostationary Meteosat Second Generation (MSG) data

The soil thermal inertia is an important parameter in remote sensing monitoring of soil heat flux and soil moisture, while its quantitative estimation from remote sensing data is still a great challenge. Most past methods use data at specific time points, which are limited to the local times of satellite passes and sensitive to single moment observation error. Moreover, most of these methods need field measurements as extra parameters. In this paper, to overcome these deficiencies, the least square adjustment real thermal inertia analytical (LSA-TI) model is proposed to estimate real thermal inertia by making full use of the high-frequency land surface temperature (LST) measurements from geostationary satellites (i.e. Meteosat Second Generation, MSG). The thermal inertia values in North African region are calculated using this method as MSG sequences of LST measurements are adopted as the only inputs.     

Assessment of spectral indices for cover estimation of senescent vegetation

Quantification of dry plant matter (crop residue, senesced foliage, non-photosynthetic vegetation, or plant litter) surface cover (f _R) is important for assessing agricultural tillage practices, carbon sequestration, rangeland health, or brush fire hazards. The Cellulose Absorption Index (CAI) and the Shortwave Infrared Normalized Difference Residue Index (SINDRI) are two spectral indices that can remotely estimate f _R. CAI and SINDRI utilize three and two spectral bands, respectively, so SINDRI is expected to be less expensive to implement in future satellite sensors. We assessed the contrast of CAI and SINDRI with respect to soil reflectance spectra. Estimating f _R with CAI is possible for all soils. However, a number of soil samples had positive SINDRI values due to various soil minerals, such as gibbsite and antigorite, which would be interpreted as high f _R, and could limit its usefulness in some areas. Therefore, SINDRI is less applicable for estimating f _R, even with reduced implementation costs.     

Land surface evapotranspiration estimation combining soil texture information and global reanalysis datasets in Google Earth Engine

ABSTRACT

Global accurate evapotranspiration (ET) maps are crucial to monitor the water balance on the Earth’s surface. For this purpose, Walker et al. (2019) developed a methodology to estimate ET (ET_WV), considering that the soil properties and moisture (SM) are important limiting factors. In this work ET_WV was assessed in different land covers, land uses and soil properties using FLUXNET data and climate reanalysis datasets available in Google Earth Engine (GEE). GEE application programming interface (API), a powerful programming tool for non-experienced user, was used here to derived global ET_WV maps. ET_WV, computed with in situ FLUXNET data resulted in an overall ubRMSE of about 1.58 mm d^?1. The ET_WV estimates with GEE datasets yielded RMSE at least 56% smaller than those published by the available weather datasets. So, ET_WV methodology showed to be suitable for deriving global ET maps from different sources of data.     

Validation of the generalized single-channel algorithm using Landsat 8 imagery and SURFRAD ground measurements

Land surface temperature (LST) is a key parameter in land surface physics and depends on energy and water exchange processes with the atmosphere. The generalized single-channel (GSC) algorithm is an operational approach for retrieving LST from satellite sensors with only one thermal infrared channel. Until now, validations of the GSC algorithm using in situ data were limited. In this study, Landsat 8 imagery and SURFace RADiation budget network (SURFRAD) ground observations were employed to test the accuracy of the GSC algorithm. The validation results show that the GSC algorithm can obtain high LST retrieval accuracy when the total atmospheric water vapour content is between 0.5 and 3 g cm^?2, with coefficient of determination (R²) between the retrieved LST and the ground LST greater than 0.97 and mean absolute error (MAE) and root mean square error (RMSE) values of 1.57 K and 1.96 K, respectively.     

Multi-dimensional histogram-based information capacity analysis of urban heat island effect using Landsat 8 data

The vegetation and impervious surface area (ISA) are the most key indicators for the urban heat islands (UHI). This study used the normalized difference vegetation index (NDVI) as the indicator of vegetation abundance, the modified normalized difference impervious surface index (MNDISI) as the indicator of impervious surface fraction to estimate the land surface temperature (LST)–vegetation relationship and LST–ISA relationship. The land surface cover types were obtained by classification and regression tree (CART). Results demonstrate and verify that LST possessed strong negative correlations with NDVI and positive correlations with MNDISI at various spatial resolutions (30–960 m). Both correlation coefficients reached their strongest points at 30 m resolution, which is believed to be the operational scale of LST, NDVI and MNDISI. Further, information capacity (IC), as a spatial index, is used to characterize the spatial pattern of UHI. Results show that the IC of LST (LST_IC) possessed strong positive correlations with the IC of NDVI (NDVI_IC) and the IC of MNDISI (MNDISI_IC). It is suggested that the spatial pattern of UHI has a direct correspondence with the vegetation and ISA.     

An observation-driven optimization method for continuous estimation of soil moisture over large heterogeneous areas

ABSTRACT

In most remote sensing-based soil moisture (SM) retrieval methods, in-situ SM measurements are commonly used for validation purposes. Few studies have investigated whether such measurements can be used for calibration. In this paper, an observation-driven optimization method was proposed to estimate SM from remote sensing observations. Specifically, the optimization method was developed within the surface temperature-vegetation index (TVX) framework for the definition of objective function and constraints. In-situ SM measurements were used to optimize the theoretical boundaries of the TVX feature space. We demonstrated the applicability of the new method with Moderate Resolution Imaging Spectroradiometer (MODIS) products over the Southern Great Plains (SGP) of the United States of America. Results indicate that the accuracy produced using only one site for calibration has reached a level comparable with those produced by traditional methods. Moreover, the method has not only bypassed the complex parameterization of aerodynamic and surface resistance but also achieved continuous monitoring of SM. That is just the capacity that the traditional TVX method does not possess. Therefore, although our optimization method requires the ancillary of in-situ observations, its simplicity proves that it is a useful tool for a quick and continuous monitoring of SM over large heterogeneous areas.     

Adsorption of CO over the Heusler alloy CrCoIrGa(001) surface: first-principles insights

In this study, the adsorption of CO molecule over (001) surface of the Heusler alloy CrCoIrGa, has been investigated using DFT+U calculations. It is demonstrated that, after relaxation, the (001) surface retains the bulk atomic positions, exhibiting no apparent surface reconstruction. Owing to the emergence of unsaturated bonds at the surface, the surface layer atoms are found to carry more spin-polarization (SP) and atomic moments than that of inner layer atoms. The ground state total SP (magnetic moment) is found to be 27% (42.256 μ_B). To explore the CO adsorption over the surface, five different adsorption configurations (sites) are considered and the strength of CO to surface interaction is estimated from the computed density of states (DOS), adsorption energy (E_a), change in magnetic moment (ΔM), vertical height between molecule and surface (h), charge transfer (ΔQ), and charge density difference (CDD) plots. For all configurations, the E_a lies in the range of −2.15 to −2.34 eV, with CO molecule adsorbed on the top of Ir atom as the most favorable adsorption configuration. The observed E_a, ΔQ, h, and ΔM values, collectively predict that the (001) surface has strong interaction (chemisorption) with CO gas molecule, thus, might be useful in gas sensing applications.

Charge density difference (CDD) plots for CO adsorbed at various sites of the CrCoIrGa(001) surface. The yellow (cyan) color represents the charge accumulation (depletion) region.     

Using reflected signal power from the BeiDou geostationary satellites to estimate soil moisture

Satellites-based microwave sensors are sensitive to soil moisture at the surface of the Earth, but their performance is limited by their continuously varying footprints due to the repeat cycle of satellites. In recent years, Global Navigation Satellite System (GNSS) microwave signals have been used to estimate soil moisture and the BeiDou system (BDS) provides new signal sources. Unlike Global Positioning System, BDS includes five geostationary earth orbit (GEO) satellites. Because their orbits are geosynchronous, the footprints of the GEO satellites remain nearly unchanged for fixed receivers; this property is beneficial for continuous long-term observation of reflection studies in the fixed area. We conducted a ground-based field experiment and collected data that included signals from GEO satellites and in situ soil moisture observations during this experiment to investigate the soil moisture estimation using the power of the reflected BDS signals. In this letter, we first designed a filter to separate the effect of GEO slight motion on raw signal. We then calculated and calibrated reflection coefficient. Finally, we estimate the continuous soil moisture in the fixed area per half hour using the reflection coefficient. The results show that the estimated soil moisture changes are largely consistent with the in situ soil moisture data except rainy days in which soil moisture changes rapidly and the mean absolute error of the estimation is less than 2.37%. This experiment demonstrates that the BDS GEO satellites represent an important source of data for use in GNSS reflectometry.     

Relating AEROCam-derived NDVI to apparent soil electrical conductivity (ECa) for corn fields in Wyoming,USA

High-spatial-resolution aerial images are necessary to capture variations in crop growth in small fields. The objective of this study was to analyse the relationship between the normalized difference vegetation index (NDVI), derived from Airborne Environmental Research and Observation Camera (AEROCam; 1 m colour infrared) images, and the apparent soil electrical conductivity (EC_a) values measured in furrow- and sprinkler-irrigated fields planted with corn (Zea mays L.) in Lingle, Wyoming. NDVI, calculated using 2005, 2006 and 2007 AEROCam images, and EC_a were selected, as both variables are used as a measure of potential crop yield. NDVI values exhibited statistically significant exponential relationships with the EC_a values for both fields. For the furrow-irrigated field, the adjusted R ² values ranged between 0.66 and 0.79, and for the sprinkler-irrigated field the adjusted R ² values ranged between 0.64 and 0.91. Results obtained in this study indicate that AEROCam images can be used for monitoring crop growth in small fields and can provide valuable insights about crop growth patterns when EC_a measurements are not available.     

Towards an operational method for land surface temperature retrieval from Landsat 8 data

Land surface temperature (LST) is a key parameter in the physics of land surfaces through the processes of energy and water exchange with the atmosphere. For Landsat data with only one thermal infrared channel (Landsat 4 to Landsat 7), LST cannot actually be retrieved, and external data sources, such as meteorological observations or Moderate Resolution Imaging Spectroradiometer (MODIS) data, are needed to obtain the water vapour content parameter (an important input parameter for the LST retrieval algorithm); this results in limitations on deriving LST. However, the band designations of the Landsat 8 sensors enable the derivation of LST from the Landsat 8 data. This article demonstrates an LST retrieval methodology that makes use of only Landsat 8 image data. In this methodology, the split-window covariance-variance ratio (SWCVR) technique is introduced to derive water vapour content from Landsat 8. A comparison between the retrieved LST and the in situ LST measurements shows good accuracy, with a root mean squared error (RMSE) of 0.83 K. The fact that the proposed LST estimation method utilizing solely Landsat 8 image data does not rely on any external data is a significant advantage for the development of an operational Landsat 8 LST product generating system.     

Evaluation of simulated soil moisture from China Land Data Assimilation System (CLDAS) land surface models

ABSTRACT

This study evaluated the land surface model-simulated soil moisture (SM) product from the China Land Data Assimilation System (CLDAS). This was achieved using three densely instrumented in situ observation networks in China that have different environmental conditions, i.e., the Hebi, Naqu and Heihe sites. The European Space Agency Climate Change Initiative multi-satellite-retrieved SM product (CCI-SM) was also included for inter-comparison purposes. Standard validation scores indicated that the CLDAS-SM product has high correlation and low uncertainty with both surface and root-zone soil moisture observations. The target accuracy (0.04 m³ m^?3) was achieved over all three sites. Compared with the CCI-SM product, the CLDAS-SM product showed higher accuracy for the Hebi and Heihe sites but slightly lower accuracy for the Naqu site located at the centre of the Tibetan Plateau. Regionally, the unbiased root mean square difference between the CLDAS-SM and CCI-SM products was noticeably smaller within China than in neighbouring countries. Given that the performance of the CCI-SM product should be unaffected by country boundaries, the better performance of the CLDAS-SM product in China can be attributed primarily to the high-quality meteorological forcing data.     

An open-source method of constructing cloud-free composites of forest understory temperature using MODIS

Surface air temperature (T_air) is a critical driver of ecosystem processes and phenological dynamics, and can be estimated in near-real time with satellite remote sensing. However, persistent cloud cover often creates large spatial and temporal gaps in our observation records. Previous studies have successfully mapped T_air; however, the challenges of mapping forest understory temperatures (T_ust) are relatively unexplored. This study describes a methodology for constructing cloud-free composites of T_ust at 250 m spatial resolution. We used generalized linear models to correlate daily average T_ust with ground-surveyed forest structural characteristics and land surface temperature (LST) obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). Models were applied to all four daily MODIS overpasses and combined in to a single image to maximize cloud-free spatial coverage. Pixel temperatures within the remaining cloud gaps were estimated using a temporal averaging algorithm that incorporated a novel approach for factoring the relative cloudiness between days. Models predicted T_ust to within 1.5°C (R² ~ 0.87), with an overall final map accuracy having a mean absolute error of 2.2°C. Maps were produced for two growing seasons using in situ observation data from forested sites throughout the Rocky Mountains of Alberta, Canada. By avoiding complex physical models, our procedure is computationally efficient and capable of processing large volumes of data using open-source programming languages and desktop computers.     

Predicting soil depth using simple ground-based measurements of stem shape and taper in the butt swell section of individual Pinus radiata trees

Predicting soil depth using simple ground-based measurements of the tree stem has multiple benefits for precision (site-specific) forest management and estimating carbon stocks of plantation forests. Current methods of mapping soil depth rely on collecting a sufficient density of direct soil measurements, which is expensive and typically not feasible over extensive forest areas. The availability of detailed soil depth information under forest plantations is consequently sparse and this presents a significant impediment to precision forest management and the ability to estimate forest soil carbon stocks. In this study, we propose that the relationship between stem shape and taper in the butt swell of individual Pinus radiata trees and soil depth can be described in a simple empirical model. We demonstrate that shape and taper of the butt-swell section of the tree stem are as robust predictors of soil depth as individual tree height, and also have the advantage of being easy to measure from the ground. This finding has potential benefits for reducing the cost of soil data collection and improving fine-scale forest soil mapping.     

Feasibility of using signal strength indicator data to estimate soil moisture based on GNSS interference signal analysis

The use of multipath signals to estimate soil moisture is an important application of Global Navigation Satellite System (GNSS) reflectometry. In most studies the data used to estimate the soil moisture are raw signal to noise ratio (SNR) data. However, the SNR data are only regarded as auxiliary data used to determine the quality of signal in most of the widely distributed Continuous Operational Reference System (CORS) receivers. So SNR data are generally ignored and unavailable. Fortunately, the GNSS receivers output the standard data format as Rinex, where the Signal Strength Indicator (SSI) is recorded as alternative data to SNR. This study aims to investigate the feasibility of soil moisture estimation based on SSI data. An experiment was conducted to estimate SSI phase and record the in situ soil moisture data for comparison. Then the relationship between the phase and soil moisture is determined by 44 days SSI data processing. Finally, the relationship is used to further estimate soil moisture with 36 days data. Experimental results show the correlation coefficient between the SSI phase and in situ soil moisture is approximately 0.7, and that the root mean square estimation error of soil moisture is lower than 9.9%. Results demonstrate the feasibility of using SSI data to estimate soil moisture.     

Calibration and validation of the Australian fractional cover product for MODIS collection 6

Fractional cover of photosynthetic vegetation (F_PV), non-photosynthetic vegetation (F_NPV) and bare soil (F_BS) is an important input for assessment of the productivity of global pastures and rangelands. Here we describe the updating of this product using the new Moderate Resolution Imaging Spectroradiometer (MODIS) Nadir BRDF-Adjusted Reflectance (NBAR) Collection 6 reflectance product (C6) and a major expansion of the field calibration database. Fractional cover based on the C6 input exhibited reduced bias and root mean square error compared with the Collection 5 (C5) product. The expanded calibration database with more sites in arid areas provided greater separation between reflectance values of end-members for F_NPV and F_BS. Specific site variations in F_NPV and F_BS in arid areas could be traced to small but consistent changes in blue and green band, and occasional changes in short wave infrared reflectance in C6 when compared to C5. The recalibration described here provides an Australian fractional cover product with reduced uncertainty and improves the basis for a prototype global product for use in modelling of rangeland and pasture productivity.     

Field measurements of the backscattering coefficient in a cascading reservoir system: first results from Nova Avanhandava and Barra Bonita Reservoirs (São Paulo,Brazil)

In this study, a data set of total suspended matter (TSM), chlorophyll-a (Chl-a), total backscattering coefficient (b_b) and the remote sensing reflectance (R_rs) were measured in the euphotic zone of two hydroelectric reservoirs at 71 stations during field surveys in the wet and dry seasons. These two reservoirs are located in a cascading system in Tietê River, São Paulo State, Brazil. The limnological and optical data were interpolated using the ordinary kriging technique to map their spatial distribution. The differences in TSM, Chl-a and in b_b in space and time were investigated. The profiling data from b_b were analysed. All these data were used to explain the resulting R_rs spectra in these two reservoirs. For both reservoirs, the inorganic fraction of TSM was responsible for the b_b variability and therefore modulates the R_rs spectra. The seasonally difference in the optical data will help to understand how the inherent optical properties and the apparent optical properties changes in a cascading reservoir system.     

Estimating chlorophyll-a concentration based on a four-band model using field spectral measurements and HJ-1A hyperspectral data of Qiandao Lake,China

Accurate estimation of phytoplankton chlorophyll-a (chl-a) concentration from remote sensing data is challenging due to the complex optical properties of case II waters. Recently, a novel semi-analytical four-band model was developed to estimate chl-a concentration in turbid productive waters. The objective of this study was to evaluate the performance of the four-band model and extend its application to hyperspectral satellite data for estimating chl-a concentration in Qiandao Lake of China. Based on field spectral measurements and in situ water sampling, the four-band model expressed as [R_rs^?1(661.6) – R_rs^?1(706.7)] [R_rs^?1(714.8) – R_rs^?1(682.2)]^?1 was calibrated after band tuning, where R_rs^?1 represents the reciprocal of the remote sensing reflectance. The spectral-based four-band model accounted for more than 88% of variance in chl-a concentration with a root mean square error (RMSE) of 1.47 μg l^?1. To justify the potential of this model with satellite data, comparable wavelengths selected from HJ-1A Hyperspectral Imager (HSI) imagery were utilized to calibrate the four-band model. The HSI-based model explained about 80% of chl-a variation with an RMSE of 1.35 μg l^?1. Experimental results also showed that the four-band model outperformed its three-band counterpart. The results validated the rationale of the four-band model and demonstrated the effectiveness of this model for estimating chl-a concentration from both in situ spectral data and HJ-1A hyperspectral satellite imagery.