Validation of spaceborne and modelled surface soil moisture products with cosmic-ray neutron probes

The scale difference between point in situ soil moisture measurements and low resolution satellite products limits the quality of any validation efforts in heterogeneous regions. Cosmic Ray Neutron Probes (CRNP) could be an option to fill the scale gap between both systems, as they provide area-average soil moisture within a 150–250 m radius footprint. In this study, we evaluate differences and similarities between CRNP observations, and surface soil moisture products from the Advanced Microwave Scanning Radiometer 2 (AMSR2), the METOP-A/B Advanced Scatterometer (ASCAT), the Soil Moisture Active and Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), as well as simulations from the Global Land Data Assimilation System Version 2 (GLDAS2). Six CRNPs located on five continents have been selected as test sites: the Rur catchment in Germany, the COSMOS sites in Arizona and California (USA), and Kenya, one CosmOz site in New South Wales (Australia), and a site in Karnataka (India). Standard validation scores as well as the Triple Collocation (TC) method identified SMAP to provide a high accuracy soil moisture product with low noise or uncertainties as compared to CRNPs. The potential of CRNPs for satellite soil moisture validation has been proven; however, biomass correction methods should be implemented to improve its application in regions with large vegetation dynamics

Remote Sensing of Complex Permittivity and Penetration Depth of Soils Using P-Band SAR Polarimetry

A P-band SAR moisture estimation method is introduced for complex soil permittivity and penetration depth estimation using fully polarimetric P-band SAR signals. This method combines eigen- and model-based decomposition techniques for separation of the total backscattering signal into three scattering components (soil, dihedral, and volume). The incorporation of a soil scattering model allows for the first time the estimation of complex soil permittivity and permittivity-based penetration depth. The proposed method needs no prior assumptions on land cover characteristics and is applicable to a variety of vegetation types. The technique is demonstrated for airborne P-band SAR measurements acquired during the AirMOSS campaign (2012–2015). The estimated complex permittivity agrees well with climate and soil conditions at different monitoring sites. Based on frequency and permittivity, P-band penetration depths vary from 5 cm to 35 cm. This value range is in accordance with previous studies in the literature. Comparison of the results is challenging due to the sparsity of vertical soil in situ sampling. It was found that the disagreement between in situ measurements and SAR-based estimates originates from the discrepancy between the in situ measuring depth of the top-soil layer (0–5 cm) and the median penetration depth of the P-band waves (24.5–27 cm)

Relationship between vegetation microwave optical depth and cross-polarized backscatter from multiyear Aquarius observations

Soil moisture retrieval algorithms based on passive microwave remote sensing observations need to account for vegetation attenuation and emission, which is generally parameterized as vegetation optical depth (VOD). This multisensor study tests a new method to retrieve VOD from cross-polarized radar backscattering coefficients. Three years of Aquarius/SAC-D data were used to establish a relationship between the cross-polarized backscattering coefficient σHV and VOD derived from a multitemporal passive dual-channel algorithm (VODMT). The dependence of the correspondence is analyzed for different land use classes. There are no systematic differences in the slope for woody versus nonwoody vegetation, resulting in a strong correlation (80% explained-variance) and a global linear relationship when all classes are combined. The relationship is stable over the years of observations. The comparison of the Aquarius-derived VODMT to Soil Moisture and Ocean Salinity's multi-angular VOD estimates shows similar spatial patterns and temporal behavior, evident in high correlations. However, VODMT has considerably higher mean values, but lower dynamic range globally. Most of the differences can be attributed to differences in instrument sampling. The main result of this study, a relationship between backscatter and VOD, will permit high-resolution mapping of VOD with synthetic aperture radar measurements. These maps allow future studies of scaling and heterogeneity effects of vegetation on soil moisture retrieval at the coarser scales of land microwave radiometry. The study shows that VOD based on passive measurements and predicted by active measurements are comparable globally and that the breakdown by land cover classification does not affect the relationship appreciably

Machine Learning with UAS LiDAR for Winter Wheat Biomass Estimations

peer reviewedAbstract. Biomass is an important indicator in the ecological and management process that can now be estimated at higher temporal and spatial resolutions because of unmanned aircraft systems (UAS). LiDAR sensor technology has advanced enabling more compact sizes that can be integrated with UAS platforms. Its signals are capable of penetrating through vegetation canopies enabling the capture of more information along the plant structure. Separate studies have used LiDAR for crop height, rate of canopy penetrations as related to leaf area index (LAI), and signal intensity as an indicator of plant chlorophyll status or green area index (GAI). These LiDAR products are combined within a machine learning method such as an artificial neural network (ANN) to assess the potential in making accurate biomass estimations for winter wheat

"Off-Season" - CO2_{2}-Austausch landwirtschaftlicher Flächen

Durch zunehmende CO2-Konzentrationen und Erwärmung hat sich sowohl die Senken- (Photosynthese) als auch die Quellenfunktion (Respiration) der terrestrischen Biosphäre intensiviert. Der Nettoeffekt entspricht derzeit einer Senke, die etwa ein Drittel der CO2-Emissionen aus fossilen Brennstoffen aufgenommen hat. Allerdings stellt zugleich der Landnutzungswandel eine Nettoquelle von etwa 14% dar (5. IPCC-Sachstandsbericht, WG I, Kap. 6, S. 471, 2013).Die Klimawirksamkeit der Landwirtschaft wird von allen drei Faktoren beeinflusst – einem steigenden Senkenpotential durch den CO2-Düngeeffekt, einer steigenden (Boden)respiration durch Erwärmung, und Landnutzungsentscheidungen. Die Wechselwirkung zwischen ihnen wird im Folgenden am Beispiel der zunehmenden Klimarelevanz von Entscheidungen über die Zwischennutzung landwirtschaftlicher Flächen demonstriert.In den letzten 50 Jahren haben sich die Aussaattermine für Winterweizen in Deutschland etwa um eine, die Erntetermine um zwei Wochen nach vorne verschoben, ähnliches gilt für vergleichbare Kulturen. Die nicht für den produktiven Anbau genutzte Zeit wird sowohl länger als auch wärmer – einerseits wegen ihrer zunehmenden Verschiebung in Richtung Sommer, andererseits wegen steigender Jahresmitteltemperaturen. Die Entscheidung über die Verwendung dieser Phasen wird somit klimarelevanter: Bei vegetationsfreiem Boden ist eine stärkere respirationsbedingten Quellenfunktion, bei einer Nutzung für den produktiven Anbau oder einer Einsaat von Zwischenfrüchten eine stärkere Senkenfunktion möglich.Durch die EU-Gesetzgebung unter dem Stichwort „Greening“ erscheint eine sprunghafte Zunahme von Zwischensaaten wie z.B. Ölrettich und Gelbsenf im Winter 2015/16 wahrscheinlich. Dies bietet eine gute Gelegenheit zur Quantifizierung des möglichen Einflusses von Landnutzungsentscheidungen auf diese „Off-Season“-Klimawirksamkeit. Auf dem Poster stellen wir Ergebnisse von CO2-Austauschmessungen in Zwischensaatbeständen und Pläne zur satellitengestützten Quantifizierung ihrer Anbaufläche vor. Die Messungen des CO2-Austauschs, der Verdunstung und der Bodenrespiration mit Hilfe von Eddy-Kovarianz-Stationen und zwei verschiedenen Haubensystemen sind ein Teil des BMBF-geförderten Projektes „IDAS-GHG“, dessen Gesamtkonzept im Vorjahr vorgestellt wurde

UAS Lidar Derived Metrics for Winter Wheat Biomass Estimations using Multiple Linear Regression

peer reviewedUnmanned Aircraft Systems (UAS) are being used more often in agriculture to provide estimations of important metrics such as biomass because of the potential for improved temporal and spatial resolutions. More recently LiDAR sensor technology has advanced enabling more compact sizes that can be integrated with UAS platforms. Being an active sensor, LiDAR signals are capable of penetrating through the vegetation canopy providing more information on plant structure. Commonly, LiDAR data is used to derive only height information. However, newer studies have shown the retrieval of additional information from the spatial distribution and intensity of LiDAR signals. This study takes a unique look at combining these types of informative products, that are particular to LiDAR, for making biomass estimation with winter wheat

Soil moisture and soil properties estimation in the Community Land Model with synthetic brightness temperature observations

The Community Land Model (CLM) includes a large variety of parameterizations, also for flow in the unsaturated zone and soil properties. Soil properties introduce uncertainties into land surface model predictions. In this paper, soil moisture and soil properties are updated for the coupled CLM and Community Microwave Emission Model (CMEM) by the Local Ensemble Transform Kalman Filter (LETKF) and the state augmentation method. Soil properties are estimated through the update of soil textural properties and soil organic matter density. These variables are used in CLM for predicting the soil moisture retention characteristic and the unsaturated hydraulic conductivity, and the soil texture is used in CMEM to calculate the soil dielectric constant. The following scenarios were evaluated for the joint state and parameter estimation with help of synthetic L-band brightness temperature data assimilation: (i) the impact of joint state and parameter estimation; (ii) updating of soil properties in CLM alone, CMEM alone or both CLM and CMEM; (iii) updating of soil properties without soil moisture update; (iv) the observation localization of LETKF. The results show that the characterization of soil properties through the update of textural properties and soil organic matter density can strongly improve with assimilation of brightness temperature data. The optimized soil properties also improve the characterization of soil moisture, soil temperature, actual evapotranspiration, sensible heat flux, and soil heat flux. The best results are obtained if the soil properties are updated only. The coupled CLM and CMEM model is helpful for the parameter estimation. If soil properties are biased, assimilation of soil moisture data with only state updates increases the root mean square error for evapotranspiration, sensible heat flux, and soil heat flux

Development and analysis of the Soil Water Infiltration Global database

In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements ( ∼ 76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76% of the experimental sites with agricultural land use as the dominant type ( ∼ 40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it

Fully polarimetric L-band brightness temperature signatures of azimuthal permittivity patterns - Measurements and model simulations

peer reviewedL-Band microwave radiometry over land mainly focuses on observations of horizontally (H) and vertically (V) polarized brightness temperatures. However, it has been demonstrated that measurements of the full Stokes vector [1] are sensitive to additional environmental properties, e.g. azimuthal plant row orientation. Furthermore, model simulations show that also a smooth surface with a periodic permittivity pattern can cause azimuthal dependencies of the Stokes parameters. The objective of this paper is to present fully polarimetric L-band measurement results from observations of a striped wood and styrodur target, when rotated 360° in small steps. Measurement results of a striped soil and open water target are also reported. Finally, measurements are compared to model simulations, with very good agreement within the validity range of the model (stripe thickness < ?/2)