Analysis of surface soil moisture patterns in an agricultural landscape utilizing measurements and ecohydrological modeling

Soil moisture and its distribution in space and time plays a decisive role in terrestrial water and energy cycles. It controls the partitioning of precipitation into infiltration and runoff as well as the partitioning of solar radiation into latent and sensible heat flux. Therefore it has a strong impact on numerous processes, e.g., controlling floods, crop yield, erosion, and climate processes. Soil moisture, and surface soil moisture in particular, is highly variable in space and time and its spatial and temporal patterns in an agricultural landscape are affected by multiple natural (precipitation, soil, etc.) and agricultural (soil management, fertilization etc.) parameters. Against this background, the current study investigates the spatial and temporal patterns of surface soil moisture in an agricultural landscape, to determine the dominant parameters and the underlying processes controlling these patterns. The study was conducted on different spatial scales, from the field scale to the whole catchment scale of the river Rur (2364 km2) in Western Germany, because observed patterns are intrinsically connected to the scale on which they are observed. For the investigation three different approaches were used: Analysis based on A) Field measurements, B) Radar remote sensing, and C) Ecohydrological modeling. Extensive field measurements were carried out in a small arable land and grassland test site, measuring surface soil moisture, plant parameters, meteorological parameters, and soil parameters. These measurements were used to analyze the small scale (field scale) patterns of surface soil moisture and for the validation of the two other methods. Since large scale investigations based on field measurements are generally not feasible, surface soil moisture maps from radar remote sensing and ecohydrological modeling were used to analyze large scale patterns of surface soil moisture and their scaling properties. Precipitation, vegetation patterns, topography and soil properties were found to be the dominant parameters for soil moisture patterns in an agriculturally used landscape. Precipitation can be assumed to be homogeneous on the small scale, but can be very heterogeneous on the large scale at the same time. Evapotranspiration causes high small scale variability, especially during the growing season. If analyzed on coarser resolutions, this small scale pattern is smoothed out. Topography is a source of small scale patterns only on wet surface soil moisture states, because of the lateral redistribution of water during or shortly after precipitation events. Soils have a major influence on the variability of surface soil moisture on all scales, due to the large heterogeneity of soil properties within a given soil type (small scale) and between different soil types (large scale). Altogether, the variability of surface soil moisture increases with an increasing size of the investigation area and with an increasing resolution within the investigation area. During the course of the year surface soil moisture variability and its scaling properties are being influenced by different parameters with temporally varying intensities. During the growing season, at the time of high small scale variability of evapotranspiration, the variability of surface soil moisture is high and decreases much stronger with decreasing spatial resolution of the investigation, than during times outside the growing season. In the beginning and towards the end of the year (outside the growing season, when the soil is wet) the patterns and their scaling properties are mainly determined by soil properties. Precipitation events generally superimpose their large scale patterns for a short period of time and diminish the small scale variability induced by evapotranspiration. This thesis improves the knowledge about surface soil moisture patterns in agriculturally used areas and their underlying processes. The results of the scaling analysis indicate the potential to use vegetation and precipitation parameters for downscaling purposes. Understanding the subscale soil moisture heterogeneity is, for example, particularly relevant to better utilize coarse scale soil moisture data derived from SMOS (Soil Moisture and Ocean Salinity) or the upcoming SMAP (Soil Moisture Active Passive) satellite measurements

A comprehensive dataset of vegetation states, fluxes of matter and energy, weather, agricultural management, and soil properties from intensively monitored crop sites in western Germany

Data description paperThe development and validation of hydroecological land-surface models to simulate agricultural areas require extensive data on weather, soil properties, agricultural management, and vegetation states and fluxes. However, these comprehensive data are rarely available since measurement, quality control, documentation, and compilation of the different data types are costly in terms of time and money. Here, we present a comprehensive dataset, which was collected at four agricultural sites within the Rur catchment in western Germany in the framework of the Transregional Collaborative Research Centre 32 (TR32) "Patterns in Soil-Vegetation-Atmosphere Systems: Monitoring, Modeling and Data Assimilation". Vegetation-related data comprise fresh and dry biomass (green and brown, predominantly per organ), plant height, green and brown leaf area index, phenological development state, nitrogen and carbon content (overall > 17 000 entries), and masses of harvest residues and regrowth of vegetation after harvest or before planting of the main crop (> 250 entries). Vegetation data including LAI were collected in frequencies of 1 to 3 weeks in the years 2015 until 2017, mostly during overflights of the Sentinel 1 and Radarsat 2 satellites. In addition, fluxes of carbon, energy, and water (> 180 000 half-hourly records) measured using the eddy covariance technique are included. Three flux time series have simultaneous data from two different heights. Data on agricultural management include sowing and harvest dates as well as information on cultivation, fertilization, and agrochemicals (27 management periods). The dataset also includes gap-filled weather data (> 200 000 hourly records) and soil parameters (particle size distributions, carbon and nitrogen content; > 800 records). These data can also be useful for development and validation of remote-sensing products. The dataset is hosted at the TR32 database (https://www.tr32db.uni-koeln.de/data.php?dataID=1889, last access: 29 September 2020) and has the DOI https://doi.org/10.5880/TR32DB.39 (Reichenau et al., 2020).Peer reviewe

Climate change simulation and trend analysis of extreme precipitation and floods in the mesoscale Rur catchment in western Germany until 2099 using Statistical Downscaling Model (SDSM) and the Soil & Water Assessment Tool (SWAT model)

Due to climate change and global warming, speed and intensity of the hydrological cycle will accelerate. In order to carry out regional risk assessment, integrated water resources management and flood protection, far reaching predictions and future scenarios of climate change effects on extreme precipitation and flooding are of particular relevance. In this study, trends in frequencies of extreme precipitation and floods until 2099 are analysed for the German Rur catchment, which is half located in highlands and half in lowlands and therefore has a high topographical and climatological contrast. To predict future trends, coupled modeling is performed based on NCEP reanalysis data and a General Circulation Model (GCM). Assuming HadCM3 future emission scenarios A2a and B2a, an empirical Statistical Downscaling Model (SDSM) is developed and daily precipitation amounts are projected until 2099 by a stochastic weather generator. The generated precipitation data are used as an input for the ecohydrological Soil & Water Assessment Tool (SWAT model) to simulate daily water discharge until 2099. Statistical trend analyses are implemented based on three annual extreme precipitation indices (EPIs) and the magnitudes of ten flood return periods derived with GEV and Gumbel extreme value distributions for 109 30-year moving periods using regression analyses and Mann-Kendall tendency tests to check for significant trends in the frequencies until 2099. As a result, it could be demonstrated for all EPIs that the frequency of extreme precipitation in the upper Rur catchment will significantly increase by +33% to +51% until 2099 compared to the base period 1961-1990, whereas mostly non-significant negative trends of extreme precipitation can be projected in the lowlands. For runoff, it was found that the magnitudes of the ten flood return periods will significantly increase by +31% for B2a to +36% for A2a until 2099 compared to the base period

Variability of surface soil moisture observed from multitemporal C-band synthetic aperture radar and field data

The study aimed to analyze the spatial variability of surface soil moisture at different spatial scales based on field measurements and remote sensing estimates. Multitemporal Envisat satellite Advanced Synthetic Aperture Radar (ASAR) data were used to derive the surface soil moisture utilizing an empirical C-band retrieval algorithm. Eight wide-swath (WS) images with a spatial resolution of 150 m acquired between February and October 2008 were used to determine the surface soil moisture contents. The accuracy of the surface soil moisture retrievals was evaluated by comparison with in situ measurements. This comparison yielded a root mean square error of 5% (v/v). Based on our in situ measurements as well as remote sensing results, the relationship of the coefficient of variation of the spatial soil moisture patterns and the mean soil moisture was analyzed at different spatial scales ranging from the catchment scale to the field scale. Our results show that the coefficient of variation decreases at all scales with increasing soil moisture. The gain of this relationship decreases with scale, however, indicating that at a given soil moisture state, the spatial variation at the large scale of whole catchments is larger than at the field scale. Knowledge of the spatial variability of the surface soil moisture is important to better understand energy exchange processes and water fluxes at the land surface as well as their scaling properties

Soil moisture index from ERS-SAR and its application to the analysis of spatial patterns in agricultural areas

Soil moisture is an important factor influencing hydrological and meteorological exchange processes at the land surface. Synthetic aperture radar (SAR) backscatter is strongly affected by the volumetric soil moisture content, thus providing the potential to derive spatially distributed soil moisture information. Archives of historic SAR data exist, in which the use is limited by the lack of corresponding ground truth measurements. This study analyzes the potential of using a soil moisture index (SMI) with high spatial resolution to assess the soil moisture status in the absence of ground truth data. The index method is applied to agricultural areas in the catchment of the river Rur in Germany. The SMI was evaluated using antecedent precipitation and the wetting and drying behavior. The spatial patterns of the SMI were analyzed using semi-variograms. This study confirms the applicability of a high resolution soil moisture index for monitoring near-surface soil moisture changes, to analyze soil moisture patterns, and indicates the possibility to complement antecedent precipitation as an input to hydrological models. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License

Crop height variability detection in a single field by multi-temporal terrestrial laser scanning

Information on crop height, crop growth and biomass distribution is important for crop management and environmental modelling. For the determination of these parameters, terrestrial laser scanning in combination with real-time kinematic GPS (RTK-GPS) measurements was conducted in a multi-temporal approach in two consecutive years within a single field. Therefore, a time-of-flight laser scanner was mounted on a tripod. For georeferencing of the point clouds, all eight to nine positions of the laser scanner and several reflective targets were measured by RTK-GPS. The surveys were carried out three to four times during the growing periods of 2008 (sugar-beet) and 2009 (mainly winter barley). Crop surface models were established for every survey date with a horizontal resolution of 1 m, which can be used to derive maps of plant height and plant growth. The detected crop heights were consistent with observations from panoramic images and manual measurements (R-2 = 0.53, RMSE = 0.1 m). Topographic and soil parameters were used for statistical analysis of the detected variability of crop height and significant correlations were found. Regression analysis (R-2 < 0.31) emphasized the uncertainty of basic relations between the selected parameters and crop height variability within one field. Likewise, these patterns compared with the normalized difference vegetation index (NDVI) derived from satellite imagery show only minor significant correlations (r < 0.44)

Regional mapping of Fluorescence in a heterogeneous agriculture landscape

Regional mapping of Fluorescence in a heterogeneous agriculture landscapeMaria Matveeva1, Patrick Rademske1, Alexander Damm2, Cosimo Brogi3, Wolfgang Korres4, and Uwe Rascher11 Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, Leo-Brandt-Str., 52425 Jülich, Germany2 Remote Sensing Laboratories, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland3 Institute of Bio- and Geosciences, IBG-3: Agrosphere, Forschungszentrum Jülich GmbH, Leo-Brandt-Str., 52425 Jülich, Germany4 Institute of Geography, University of Cologne, Albertus-Magnus-Platz , D-50923 Cologne, GermanyThe increased interest of the scientific community to the remote sensing of sun-induced chlorophyll fluorescence (F) leads to a large number of fruitful and interesting experiments on the field scale. On the other side, satellite data became available, from which fluorescence on the global scale can be derived. However, it is still an open question, how representative the results of field experiments are for a larger (regional) scale. Fluorescence of the same crop strongly varies depending on the season, soil moisture, nutrient availability, etc.To evaluate the heterogeneity of fluorescence (F) and vegetation indices (VI) within and between fields and for a better understanding of the link between F and biophysical parameters, the agriculture area in Nordrhein-Westfalen (Germany) was chosen for measurements. Data were collected using the high performance imaging spectrometer HyPlant, which is a dedicated fluorescence spectrometer and allows measuring radiance in the wavelength range between 400 nm and 2500 nm, and between 670 nm and 780 nm with a high spectral resolution of 0.26 nm allowing the measurement of both fluorescence peaks. Data were recorded with a spatial resolution of 3 meter per pixel for the whole region (ca. 14×14 km) and with 1 m resolution for the Selhausen area (ca. 1.5×5 km). That area was better characterized in terms of land use classification, soil moisture, geophysical measurements, leaf area index (LAI), defined soil properties and the presence of an Eddy Covariance tower.In this work, we investigate the within and between species variability of red, far-red, integrated fluorescence and vegetation indices, from which such biophysical parameters as LAI, chlorophyll content, fractional cover etc. can be calculated. Considering the land use classification it is possible to choose the fields with the same crop type in the whole investigated area and find a distribution of F emission of main regional crops such as winter wheat, winter barley, sugar beet and corn

Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA)

The ratio of leaf area to ground area (leaf area index, LAI) is an important state variable in ecosystem studies since it influences fluxes of matter and energy between the land surface and the atmosphere. As a basis for generating temporally continuous and spatially distributed datasets of LAI, the current study contributes an analysis of its spatial variability and spatial structure. Soil-vegetation-atmosphere fluxes of water, carbon and energy are nonlinearly related to LAI. Therefore, its spatial heterogeneity, i.e., the combination of spatial variability and structure, has an effect on simulations of these fluxes. To assess LAI spatial heterogeneity, we apply a Comprehensive Data Analysis Approach that combines data from remote sensing (5 m resolution) and simulation (150 m resolution) with field measurements and a detailed land use map. Test area is the arable land in the fertile loess plain of the Rur catchment on the Germany-Belgium-Netherlands border. LAI from remote sensing and simulation compares well with field measurements. Based on the simulation results, we describe characteristic crop-specific temporal patterns of LAI spatial variability. By means of these patterns, we explain the complex multimodal frequency distributions of LAI in the remote sensing data. In the test area, variability between agricultural fields is higher than within fields. Therefore, spatial resolutions less than the 5 m of the remote sensing scenes are sufficient to infer LAI spatial variability. Frequency distributions from the simulation agree better with the multimodal distributions from remote sensing than normal distributions do. The spatial structure of LAI in the test area is dominated by a short distance referring to field sizes. Longer distances that refer to soil and weather can only be derived from remote sensing data. Therefore, simulations alone are not sufficient to characterize LAI spatial structure. It can be concluded that a comprehensive picture of LAI spatial heterogeneity and its temporal course can contribute to the development of an approach to create spatially distributed and temporally continuous datasets of LAI