Surface Soil Moisture Content Research Articles

Influence of soil moisture content and soil and water conservation measures on time to runoff initiation under different rainfall intensities

Time to runoff initiation (TRI) is an important factor related to water infiltration, runoff generation, and hillslope erosion. This study assesses TRI values for six different plots and investigates the effects of rainfall intensity, surface soil moisture content at depths of 0–5 cm, soil and water conservation measures (longitudinal ridges, cross ridges, grass, forest, and flat field under fruit trees), and vegetation on TRI for bare soil, longitudinal ridges under maize, cross ridges under peanut crops, grass, forest, and flat field under fruit trees. Results showed that bare soil exhibited the shortest TRI, followed by the longitudinal ridges under maize and cross ridges under peanut crops. The remaining three plots did not generate runoff. The first principal component affecting TRI was soil moisture content, which contributed 69.87%, 64.88%, and 85.73% to the TRI of bare soil, longitudinal ridges under maize, and cross ridges under peanut crops, respectively. The second major component of these three plots were combined rainfall intensity, comprehensive rainfall and vegetation factors, and vegetation factors, which contributed 27.98%, 33.73%, and 14.27%, respectively. Maximum load factor was generated during runoff with maximum rainfall intensity (0.969), stalk diameter (0.985), and maize height (0.864). In conclusion, soil moisture content are the main factors influencing TRI, and soil and water conservation measures provided key benefits for improving the time to runoff initiation and reduction of runoff erosion in the farmlands.

Soil Moisture Retrieval Using Quad-Polarized SAR Data from Radar Imaging Satellite 1 (RISAT1) Through Artificial Intelligence-Based Soft Computing Techniques

The present study aims to explore and compare the potential of different Artificial Intelligence-based Soft Computing (AISC) techniques to prepare surface Soil Moisture Content (SMC) map using fine-resolution (~ 5 m), quad-polarized Synthetic Aperture Radar (SAR) data obtained from Radar Imaging Satellite 1 (RISAT1). Potential of three different AISC techniques, i.e. Support Vector Machine (SVM), Random Forest (RF) and Genetic Programming (GP), is explored. The estimated surface SMC is validated with the field soil moisture values in both bare and vegetated lands (< 30 cm height). Different techniques have their own merits and demerits; however, we recommend GP to be most useful due to its other features. For example, GP provides the mathematical relationship, importance and sensitivity of each individual input to the surface SMC. This helps us to quantify the contribution of quad-polarized backscattering coefficients and soil texture information. It is noticed that the use of only SAR data without soil texture information may be acceptable with reasonable accuracy with an enormous benefit of its applicability to the locations without soil texture information. Using this, an exemplary fine-resolution (~ 5 m) SMC map is developed. Such high-resolution maps for large spatial extent are expected to be highly useful in many applications.

Moisture Distribution in Sloping Black Soil Farmland during the Freeze–Thaw Period in Northeastern China

This paper outlines dynamics of near-surface hydrothermal processes and analyzes the characteristics of moisture distribution during the freeze–thaw period in a typical black soil zone around Harbin, Northeastern China, a region with a moderate depth of seasonally frozen ground and one of the most important granaries in China. At Field Site 1, we analyzed the soil temperature and soil moisture content data from November 2011 to April 2012 from soil depths of 1, 5, 10, and 15 cm in sunny slope, and from depths of 1, 5, and 10 cm in shady slope black soil farmland. At Field Site 2, soil samples were collected from a 168 m long sloping black soil field at locations 10, 50, 100, and 150 m from the bottom of the slope at different depths of 0–1 cm, 1–5 cm, and 5–10 cm at the same location. Analysis of the monitored Site 1 soil temperature and soil moisture content data showed that the soil moisture content and soil temperature fit line is consistent with a Gaussian distribution rather than a linear distribution during the freeze–thaw period. The soil moisture content and time with temperature fit line is in accordance with a Gaussian distribution during the freeze–thaw period. Site 2 soil samples were analyzed, and the soil moisture contents of the sloping black soil farmland were obtained during six different freeze–thaw periods. It was verified that the soil moisture content and time with temperature fit line is in accordance with a Gaussian distribution during the six different freeze–thaw periods. The maximum surface soil moisture content was reached during the early freeze–thaw period, which is consistent with the natural phenomenon of early spring peak soil moisture content under temperature rise and snow melt. The soil moisture contents gradually increased from the top to the bottom in sloping black soil farmland during the freeze–thaw period. Since the soil moisture content is related to soil temperature during the freeze–thaw cycle, we validated the correlation between soil temperature spatiotemporal China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model–Soil Temperature (CMADS-ST) data and monitored data. The practicality of CMADS-ST in black soil slope farmland in the seasonal frozen ground zone of the study area is very good. This research has important significance for decision-making for protecting water and soil environments in black soil slope farmland.

Study on Soil Water and Heat Transport Characteristic Responses to Land Use Change in Sanjiang Plain

Water and heat transport is the focus of hydrological research in seasonal frost areas. Considering Sanjiang Plain as the study area, this study explored the influence of land use change on soil water and heat transport and the future trend of surface water and heat transport. The effects of land use change on soil water and heat transport were revealed using observation data from the Sanjiang Mire Wetland Experimental Station of the Chinese Academy of Sciences from the period of May 2016 to May 2017. The analysis found evident changes in the water and heat status of different land use types. During conversion from uplands to paddy fields, the surface soil moisture content increased, evapotranspiration increased by approximately 20%, surface infiltration decreased by about 50%, and surface heat flux increased. In a future climate change scenario, the change trend of soil water and heat condition was roughly consistent with the present situation, and the proportion of evapotranspiration of upland and paddy fields in precipitation decreased to 40% and 55%, respectively. These results can provide a theoretical basis for the rational utilization of land and water resources in Sanjiang Plain.

Detection of soil moisture anomalies based on Sentinel-1

Active and passive microwave remote sensing has evolved into a well-accepted and widely used method for the spatially continuous, coarse to medium resolution mapping of the surface soil moisture content (SMC). Presently, the exploitation of high-resolution data is less mature. Sentinel-1 is a high-resolution Synthetic Aperture Radar (∼5 by 20 m), which acts as the basis for mapping of SMC in this study. There aren't any other comparable SAR missions currently available whose data can be accessed and used for free.One of the applications for SMC measurements is connected to the relationship between SMC anomalies and natural hazards such as droughts, flooding, or land-slides. A requirement for the detection and quantification of an anomaly is a long time-series (often 10–30 years) to derive a reference value. Herein lies one of the issues of Sentinel-1 based SMC mapping – at the time of writing, the Sentinel-1 time-series spanned a period of approximately 3.5 years.We introduce an approach to overcome this problem and enable the Sentinel-1 based SMC anomaly detection. The method is based on a cross-calibration between Sentinel-1 SMC estimations and coarse resolution (∼30 km) modelled SMC from the Global Land Data Assimilation System (GLDAS), which covers the time-span from 1948 to today. As a result, we can derive the long-term averages for each Sentinel-1 pixel.Results show that the proposed method allows a very accurate reproduction of the average SMC (for any given pixel) – if computed for the S1 time-span, the RMSE between estimated (GLDAS based) and true average S1 SMC is 0.7 %-Vol. Furthermore, the comparison with an in-situ time-series shows the correct detection of negative and positive anomalies, respectively. The method presented here may allow the integration of S1 data into, e.g., drought monitoring or flood forecasting applications.

ADVANCES IN SOIL MOISTURE RETRIEVAL FROM NEAR-SURFACE MEASUREMENTS USING SATELLITE REMOTE SENSING

Abstract. Soil moisture influences numerous environmental processes occurring over large spatial and temporal scales. It profoundly influences the hydrological and meteorological activity together with climate predictions and hazard analysis. Space-borne sensors are capable of retrieving the surface soil moisture over a region on a regular basis. Latent heat measurements of soil, reflectance based methods, microwave measurements and synergistic approaches are some of the techniques used since long for providing soil moisture estimates over regional and global scales. Due to the dynamic interaction of soil with crops, retrieval of surface soil moisture is always challenging. This paper gives a brief overview of advance in soil moisture retrieval techniques, and an attempt to generate surface soil moisture from fine-resolution satellite remote sensing data. The optical remote sensing explores the linear relationship between land surface reflectance and soil moisture content, and through development of empirical spectral vegetation indices. Another way to estimate soil moisture emerged by measuring amplitude of diurnal temperature, which is closely related to thermal conductivity and heat capacity of soil. Emergence of radiometric satellite measurements at fine resolution has reached at a higher level of technology these days. Microwave remote sensing techniques have a long legacy of providing surface soil moisture estimates with reasonable accuracy. The SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Passive and Active) missions launched in 2009 and 2015 respectively, are completely dedicated for providing soil moisture at global scale with a spatial resolution of 35 km &amp;amp; 3–40 km. These soil moisture products, however, provides data at highly coarser spatial resolution. The launch of Sentinels gave insight by providing active radar and optical data at higher resolution (∼10 m). Sentinel-1 is the first SAR (Synthetic Aperture Radar) constellation having 6-day revisit time providing data in C-band with dual polarisations. However, no algorithm or methodology is available to generate surface soil moisture product at a finer resolution from dual polarisations. Sentinel-1 data has been used to generate regional surface soil moisture image through modelling. The same has been also used for generating surface soil moisture map of IARI farm at New Delhi. Dubois, a bare surface model, was tested for its suitability for surface soil moisture retrieval of the farm. In addition, radar- based Soil moisture (SM) proxy method was used over Sentinel-1 data for the month of July 2018, and validated through actual surface soil moisture (gravimetric) measurements. Results were satisfactory for a range of 4–16 m3 m−3 of soil moisture, with coefficient of determination (R2) as 0.45, RMSE of 2.35 and a p-value of 0.005. However, over a higher range of soil moisture (21–33 m3 m−3), which occurred after the rainfall, the R2 value reduced to 0.22 with larger RMSE. Results suggested that SM-proxy approach might work well for a limited range (drier part) of soil moisture content, and not for the wet soil.

MARMIT: A multilayer radiative transfer model of soil reflectance to estimate surface soil moisture content in the solar domain (400–2500 nm)

Surface soil moisture content (SMC) is known to impact soil reflectance at all wavelengths of the solar spectrum. As a consequence, many semi-empirical methods aim at inferring SMC from soil reflectance, but very few rely on physically-based models. This article presents a multilayer radiative transfer model of soil reflectance called MARMIT (multilayer radiative transfer model of soil reflectance) as a function of SMC given on a mass basis and a method called MARMITforSMC to estimate it from soil reflectance spectra. This model depicts a wet soil as a dry soil covered with a thin film of water. It is used to assess SMC over seven independent laboratory datasets gathered from the literature. A learning phase is required to link the thickness of the water film with the SMC. For that purpose, a sigmoid function, the parameters of which are related to soil physical and chemical properties such as porosity, grain size and mineralogy composition, is fitted. SMC can be inferred with good accuracy (RMSE ≈ 3%) if the learning step is applied soil by soil. The link between SMC and water thickness actually depends on soil texture and chemical composition. If the soils are divided into classes and if the learning phase is applied to a class, the RMSE slightly increases up to 5%. Finally, MARMITforSMC provides lower RMSE than any other existing semi-empirical or physically-based method.

Spatial and temporal variability of soil moisture in relation with topographic and meteorological factors in south of Ardabil Province, Iran

This research has been tried to evaluate the spatial and temporal variability of surface soil moisture (SM) in a semi-arid and cold region of Ardabil Province in Iran with an area of about 10,000km2. The used SM data is the SMAP Enhanced L2 Radiometer Half-Orbit 9km Soil Moisture, provided by NASA. The study area was subdivided into 120 locations consisting 10 × 12 grids, matching with the pixels of the SMAP images. In order to evaluate the spatial variations of SM, the relation of mean SM with coefficient of variation and standard deviation has been evaluated and, then, the representative location for mean SM of the area has been identified using the index of temporal stability. Moreover, the effect of topographic factors (elevation, slope, and aspect) on spatial variations of SM, and the effect of meteorological factors (rainfall, sunshine hours, temperature, relative humidity, wind speed, and number of dry days) on temporal variations of SM have been investigated. The relation of mean SM with the coefficient of variation and standard deviation represented an exponentially negative and upper convex shape, respectively. The SM content of the representative location had a correlation with the mean SM of the area with the coefficient of determination value of 0.91. Of the three topographic factors, only the slope factor, and of the meteorological factors all of them except the wind speed have showed a significant relationship with SM spatial and temporal variations respectively.

Development of Adaptive Threshold and Data Smoothening Algorithm for GPR Imaging

There are many approaches available to separate the background and foreground in image processing applications. Currently, researchers are focusing on wavelet De-noising, curvelet threshold, Edge Histogram Descriptor threshold, Otsu thresholding, recursive thresholding and adaptive progressive thresholding. In fixed and predictable background conditions, above techniques separate background and foreground efficiently. In a common scenario, background reference is blind due to soil surface moisture content and its non-linearity. There are many methodologies proposed from time to time by researchers to solve this blind reference background separation. But challenges still now remain, because there are two major problems in ground penetrating radar imaging such as targets like ground enhances the false alarm and non-metallic target detection, where the threshold decision is a critical task. In this paper, a novel real time blind adaptive threshold algorithm is proposed for ground penetrating radar image processing. The blind threshold was decided to use normal random variable variance and image data variance. Further, the image was smoothened by random variance ratio to image data variance. Experimental results showed satisfactory results for the background separation and smoothening the targeted image data with the proposed algorithm.

Impact of Surface Soil Moisture Variations on Radar Altimetry Echoes at Ku and Ka Bands in Semi-Arid Areas

Radar altimetry provides information on the topography of the Earth surface. It is commonly used for the monitoring not only sea surface height but also ice sheets topography and inland water levels. The radar altimetry backscattering coefficient, which depends on surface roughness and water content, can be related to surface properties such as surface soil moisture content. In this study, the influence of surface soil moisture on the radar altimetry echo and backscattering coefficient is analyzed over semi-arid areas. A semi-empirical model of the soil’s complex dielectric permittivity that takes into account that small-scale roughness and large-scale topography was developed to simulate the radar echoes. It was validated using waveforms acquired at Ku and Ka-bands by ENVISAT RA-2 and SARAL AltiKa respectively over several sites in Mali. Correlation coefficients ranging from 0.66 to 0.94 at Ku-band and from 0.27 to 0.96 at Ka-band were found. The increase in surface soil moisture from 0.02 to 0.4 (i.e., the typical range of variations in semi-arid areas) increase the backscattering from 10 to 15 dB between the core of the dry and the maximum of the rainy seasons.

On the relationship between land surface infrared emissivity and soil moisture

The relationship between surface infrared (IR) emissivity and soil moisture content has been investigated based on satellite measurements. Surface soil moisture content can be estimated by IR remote sensing, namely using the surface parameters of IR emissivity, temperature, vegetation coverage, and soil texture. It is possible to separate IR emissivity from other parameters affecting surface soil moisture estimation. The main objective of this paper is to examine the correlation between land surface IR emissivity and soil moisture. To this end, we have developed a simple yet effective scheme to estimate volumetric soil moisture (VSM) using IR land surface emissivity retrieved from satellite IR spectral radiance measurements, assuming those other parameters impacting the radiative transfer (e.g., temperature, vegetation coverage, and surface roughness) are known for an acceptable time and space reference location. This scheme is applied to a decade of global IR emissivity data retrieved from MetOp-A infrared atmospheric sounding interferometer measurements. The VSM estimated from these IR emissivity data (denoted as IR-VSM) is used to demonstrate its measurement-to-measurement variations. Representative 0.25-deg spatially-gridded monthly-mean IR-VSM global datasets are then assembled to compare with those routinely provided from satellite microwave (MW) multisensor measurements (denoted as MW-VSM), demonstrating VSM spatial variations as well as seasonal-cycles and interannual variability. Initial positive agreement is shown to exist between IR- and MW-VSM (i.e., R(sup 2) = 0.85). IR land surface emissivity contains surface water content information. So, when IR measurements are used to estimate soil moisture, this correlation produces results that correspond with those customarily achievable from MW measurements. A decade-long monthly-gridded emissivity atlas is used to estimate IR-VSM, to demonstrate its seasonal-cycle and interannual variation, which is spatially coherent and consistent with that from MW measurements, and, moreover, to achieve our objective of investigating the relationship between land surface IR emissivity and soil moisture.

Fusing Microwave and Optical Satellite Observations to Simultaneously Retrieve Surface Soil Moisture, Vegetation Water Content, and Surface Soil Roughness

Uncertainty in surface soil roughness strongly degrades the performance of surface soil moisture (SSM) and vegetation water content (VWC) retrieval from passive microwave observations. This paper proposes an algorithm to objectively determine the surface soil roughness parameter of the radiative transfer model by fusing microwave and optical satellite observations. It is then demonstrated in a semiarid in situ observation site. The roughness correction of this new algorithm positively impacted the performance of SSM (root-mean-square error reduced from 0.088 to 0.070) and VWC retrieval from the Advanced Microwave Scanning Radiometer 2 and Moderate Resolution Imaging Spectroradiometer. Since this surface soil roughness correction may be transferrable to other microwave satellite retrieval algorithms such as those for the Soil Moisture and Ocean Salinity and Soil Moisture Active Passive satellites, this new algorithm can contribute to many microwave earth surface observation satellite missions.

Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products

Global-scale surface soil moisture (SSM) products retrieved from active and passive microwave remote sensing provide an effective method for monitoring near-real-time SSM content with nearly daily temporal resolution. In the present study, we first inter-compared global-scale error patterns and combined the Soil Moisture Active Passive (SMAP), Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) SSM products using a triple collocation (TC) analysis and the maximized Pearson correlation coefficient (R) method from April 2015 to December 2016. The Global Land Data Assimilation System (GLDAS) and global in situ observations were utilized to investigate and to compare the quality of satellite-based SSM products.The average R-values of SMAP, ASCAT, and AMSR2 were 0.74, 0.64, and 0.65 when they compared with in situ networks, respectively. The ubRMSD values were (0.0411, 0.0625, and 0.0708) m3m−3; and the bias values were (−0.0460, 0.0010, and 0.0418) m3m−3 for SMAP, ASCAT, and AMSR2, respectively. The highest average R-values from SMAP against the in situ results are very encouraging; only SMAP showed higher R-values than GLDAS in several in situ networks with low ubRMSD (0.0438m3m−3). Overall, SMAP showed a dry bias (−0.0460m3m−3) and AMSR2 had a wet bias (0.0418m3m−3); while ASCAT showed the least bias (0.0010m3m−3) among all the products.Each product was evaluated using TC metrics with respect to the different ranges of vegetation optical depth (VOD). Under vegetation scarce conditions (VOD<0.10), such as desert and semi-desert regions, all products have difficulty obtaining SSM information. In regions with moderately vegetated areas (0.10<VOD<0.40), SMAP showed the highest Signal-to-Noise Ratio. Over highly vegetated regions (VOD>0.40) ASCAT showed comparatively better performance than did the other products.Using the maximized R method, SMAP, ASCAT, and AMSR2 products were combined one by one using the GLDAS dataset for reference SSM values. When the satellite products were combined, R-values of the combined products were improved or degraded depending on the VOD ranges produced, when compared with the results from the original products alone.The results of this study provide an overview of SMAP, ASCAT, and AMSR2 reliability and the performance of their combined products on a global scale. This study is the first to show the advantages of the recently available SMAP dataset for effective merging of different satellite products and of their application to various hydro-meteorological problems.

SMOS near-real-time soil moisture product: processor overview and first validation results

Abstract. Measurements of the surface soil moisture (SM) content are important for a wide range of applications. Among them, operational hydrology and numerical weather prediction, for instance, need SM information in near-real-time (NRT), typically not later than 3 h after sensing. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite is the first mission specifically designed to measure SM from space. The ESA Level 2 SM retrieval algorithm is based on a detailed geophysical modelling and cannot provide SM in NRT. This paper presents the new ESA SMOS NRT SM product. It uses a neural network (NN) to provide SM in NRT. The NN inputs are SMOS brightness temperatures for horizontal and vertical polarizations and incidence angles from 30 to 45°. In addition, the NN uses surface soil temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS). The NN was trained on SMOS Level 2 (L2) SM. The swath of the NRT SM retrieval is somewhat narrower (∼ 915 km) than that of the L2 SM dataset (∼ 1150 km), which implies a slightly lower revisit time. The new SMOS NRT SM product was compared to the SMOS Level 2 SM product. The NRT SM data show a standard deviation of the difference with respect to the L2 data of &lt; 0.05 m3 m−3 in most of the Earth and a Pearson correlation coefficient higher than 0.7 in large regions of the globe. The NRT SM dataset does not show a global bias with respect to the L2 dataset but can show local biases of up to 0.05 m3 m−3 in absolute value. The two SMOS SM products were evaluated against in situ measurements of SM from more than 120 sites of the SCAN (Soil Climate Analysis Network) and the USCRN (US Climate Reference Network) networks in North America. The NRT dataset obtains similar but slightly better results than the L2 data. In summary, the NN SMOS NRT SM product exhibits performances similar to those of the Level 2 SM product but it has the advantage of being available in less than 3.5 h after sensing, complying with NRT requirements. The new product is processed at ECMWF and it is distributed by ESA and via the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) multicast service (EUMETCast).

Four decades of microwave satellite soil moisture observations: Part 1. A review of retrieval algorithms

The satellite based passive (radiometer) and active (radar) microwave sensors enhanced our ability to retrieve soil moisture at global scales. It has been almost four decades since the first passive microwave satellite sensor was launched in 1978. Since then soil moisture has gained considerable attention in hydro-meteorological, climate, and agricultural research resulting in the deployment of two dedicated missions in the last decade, SMOS and SMAP. Microwave retrievals require an algorithm to estimate soil moisture from satellite measurements. In this Part 1 of a two-part review series, we provide a synthesis of four decades of research and development on the passive and active microwave soil moisture retrieval algorithms. The algorithms associated with passive sensors use the radiometer brightness temperatures, while active sensors use the radar backscatter measurements to retrieve soil moisture. The physics of both algorithm classes are based on the fact that the microwave measurements at lower frequencies are influenced by the soil dielectric property, which acts as a proxy for the surface soil moisture content. In this review effort, the emphasis is laid on the physical models of the passive and the active retrieval algorithms. These algorithms facilitate to obtain the individual radiative contributions from soil, vegetation, and atmosphere that reach satellite sensors after mixing (roughness), scattering, and attenuation. In the process, we looked into the current research efforts to improve individual aspects of the algorithms, followed by a description of different retrieval procedures. In Part 2 of this review series, performance evaluation and inter-sensor comparisons of soil moisture of eight passive and two active sensors are carried out using 1058 stations along with model soil moisture data in the Contiguous United States (CONUS) region. (C) 2017 Elsevier Ltd. All rights reserved.

Estimation of the Instantaneous Soil Surface Moisture Content in Cold Seasons by using Optical and Thermal Remote Sensing Data under Clear Sky

Estimation of the Instantaneous Soil Surface Moisture Content in Cold Seasons by using Optical and Thermal Remote Sensing Data under Clear Sky

Landsat images and crop model for evaluating water stress of rainfed soybean

Soil water content is a vital resource that plays a central role in agricultural areas. In Argentina the soybean (Glycine max (L.) Merrill) is the most important crop, considering the economic yield and the sown area. Actually, remote sensing allows continuous monitoring of crops and to evaluate the impact of water stress in their development. The combination of Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI) is an indicator that provides information about the condition of the vegetation and surface soil moisture content. In this study we evaluate relationships between indicators of crop water stress and the Temperature Vegetation Dryness Index (TVDI) determined from Landsat, for sites with rainfed soybean in the agricultural central zone of Córdoba (Argentina).Field data were acquired continuously throughout the whole growing season. For each sample date and plot, data of percent green vegetation cover, soil moisture content and phenology were registered. The use of a simulation crop model allowed obtaining indicators of crop water stress indices: (1) soil water deficit, (2) crop water use vs. reference crop evapotranspiration, and (3) fraction of the available water capacity that is readily available.The NDVI/LST spaces presented a trapezoidal form, which indicated that TVDI will have similar sensitivity for the full range of NDVI and showed temporal changes of wet and dry edges.When soybean cover exceeds 60%, the combination of TVDI with (2) and (3) can enhance the ability of detecting crop water stress conditions (R2=0.62 and 0.82, respectively). However, the relationship between TVDI and (1) showed low correlation values (R2=0.21).

A Time-Series Approach to Estimating Soil Moisture From Vegetated Surfaces Using L-Band Radar Backscatter

Many previous studies have shown the sensitivity of radar backscatter to surface soil moisture content, particularly at L-band. Moreover, the estimation of soil moisture from radar for bare soil surfaces is well-documented, but estimation underneath a vegetation canopy remains unsolved. Vegetation significantly increases the complexity of modeling the electromagnetic scattering in the observed scene, and can even obstruct the contributions from the underlying soil surface. Existing approaches to estimating soil moisture under vegetation using radar typically rely on a forward model to describe the backscattered signal and often require that the vegetation characteristics of the observed scene be provided by an ancillary data source. However, such information may not be reliable or available during the radar overpass of the observed scene (e.g., due to cloud coverage if derived from an optical sensor). Thus, the approach described herein is an extension of a change-detection method for soil moisture estimation, which does not require ancillary vegetation information, nor does it make use of a complicated forward scattering model. Novel modifications to the original algorithm include extension to multiple polarizations and a new technique for bounding the radar-derived soil moisture product using radiometer-based soil moisture estimates. Soil moisture estimates are generated using data from the Soil Moisture Active/Passive (SMAP) satellite-borne radar and radiometer data, and are compared with up-scaled data from a selection of in situ networks used in SMAP validation activities. These results show that the new algorithm can consistently achieve rms errors less than 0.07 m3/m3 over a variety land cover types.

Wheat residue mulch and anti-transpirants improve productivity and quality of rainfed soybean in semi-arid north-Indian plains

Soybean (Glycine max L. Merr.) is largely grown in moisture-stressed conditions resulting in low yield. A 3-year field experiment was conducted during rainy seasons of 2012–2014 to determine: (i) the effects of straw mulches and anti-transpirants on soil moisture retention, rainwater-use efficiency (RWUE), photosynthesis, growth, yield, and quality of soybean (cv. PS 1347), and (ii) the influence of climate (year) on responses of soybean to straw-mulch and anti-transpirant application. The treatments included two mulching treatments, no-mulch and wheat residue mulch (at 5Mgha−1), and the use of anti-transpirants, MgCO3 (5%), glycerol (5%), Na2CO3 (2%), KNO3 (1%) and no-anti-transpirant (control) sprayed 15days after flower initiation. Wheat straw mulching enhanced surface soil (0–30cm) moisture content by 20% over plots under no-mulch, improved leaf SPAD (soil plant analysis development) values, net-photosynthetic rates and boosted soybean grain yield by ∼16% over no-mulching. Plots with wheat straw mulching also had ∼18, 18 and 17% higher protein yield, oil yield and RWUE compared with no-mulch plots. Spraying MgCO3 and KNO3 along with wheat residue mulching (5Mgha−1) caused significant enhancement in grain-and protein-yield, and RWUE over control. However, Na2CO3 was not a suitable anti-transpirant as it reduced soybean growth, yield and quality. These results suggest that wheat residue mulch and anti-transpirants, MgCO3 and KNO3, can significantly improve the yield and quality of soybean in moisture-stressed rainfed environments.

Analysis of Radio Frequency Interference in Metop ASCAT Backscatter Measurements

The advanced scatterometer (ASCAT) is a radar system carried on board the ESA/EUMETSAT METOP series of satellites. It is designed for the purpose of retrieving wind field over oceans. It also provides information on surface soil moisture content and sea ice. Although ASCAT uses a linear frequency modulated pulse with a center frequency of 5.255 GHz (C-band), it is subject to radio frequency interference (RFI). This paper analyses seven years of ASCAT data and shows an increase of the number of noise outliers and an increase of the noise background level over specific land areas. This suggests that the outliers are not a natural occurrence, but are due to RFI from ground-based equipments. As regards the observed increase of the noise background level, it is not straightforward to associate possible RFI sources which could have caused it. However, since the ASCAT has a dynamic range of about 30 dB, the worse measured increase of 1 dB in the noise floor has almost no impact on performance, in particular, on soil moisture retrieval. In addition, the effect of the noise outliers on the estimate of the ASCAT receiver filter shape function used in the processing is also examined and is found to introduce errors of up to 0.4 dB. However, the occurrence of the noise outliers is generally very low, typically two out of 60 000 noise measurements per day, so the impact on the operational use of ASCAT data for wind vector retrieval is limited.