In situ changes in gross N transformations in bare soil after addition of straw

1998 ◽  
Vol 31 (1) ◽  
pp. 119-133 ◽  
Author(s):  
Sylvie Recous ◽  
Celso Aita ◽  
Bruno Mary
Keyword(s):  
Bare Soil ◽  
N Transformations ◽  
Gross N Transformations

EFFECTS OF GROWING SEASON SOIL TEMPERATURE, MOISTURE, AND NH4-N ON SOIL NITROGEN

1974 ◽  
Vol 54 (4) ◽  
pp. 403-412 ◽  
Author(s):  
C. A. CAMPBELL ◽  
D. W. STEWART ◽  
W. NICHOLAICHUK ◽  
V. O. BIEDERBECK
Keyword(s):  
Quantitative Relationship ◽  
Growing Season ◽  
Stepwise Multiple Regression ◽  
Soil N ◽  
N Transformations ◽  
Temperature Changes ◽  
Diurnal Patterns ◽  
Multiple Regression Technique ◽  
Time Required

Wood Mountain loam was wetted with water or (NH4)2SO4 solution to provide a factorial combination among three moisture and three NH4-N levels. Samples in polyethylene bags were incubated at 2.5-cm depths in fallow, and in an incubator that simulated the diurnal patterns of temperature fluctuation recorded in the field. During the growing season, treatments were sampled regularly for moisture, NO3− and exchangeable NH4-N. Similar determinations were made on in situ samples taken in fallow Wood Mountain loam. The incubator simulated the effects of growing season temperatures on soil N transformations satisfactorily. Pronounced increases or decreases in temperature led to flushes in N mineralization. However, in the 1972 growing season, temperature was suboptimal and temperature changes were generally small. Consequently, when a stepwise multiple regression technique was used to analyze the data, neither ammonification nor nitrification showed a quantitative relationship to temperature. Comparison of the nitrification occurring in laboratory-incubated soils with that occurring in situ led to the conclusion that 70 to 90% of the NO3-N produced in surface soil resulted from wetting and drying. Estimates of potentially ammonifiable soil N(No) and its rate of mineralization (k) were derived from cumulative ammonification by assuming that the laws of first-order kinetics were applicable. In the 10, 15, and 20% moisture treatments the average No was 27, 41, and 82 ppm, respectively. Under the conditions of this study, the time required to mineralize half of No was about 7 wk.

scholarly journals Comparison of Hyperspectral Versus Traditional Field Measurements of Fractional Ground Cover in the Australian Arid Zone

2019 ◽  
Vol 11 (23) ◽  
pp. 2825 ◽  
Author(s):  
Claire Fisk ◽  
Kenneth Clarke ◽  
Megan Lewis
Keyword(s):  
Spatial Scale ◽  
Arid Zone ◽  
Ground Cover ◽  
Field Measurements ◽  
Bare Soil ◽  
Point Sampling ◽  
Calibration And Validation ◽  
Fractional Cover ◽  
In Situ Methods

The collection of high-quality field measurements of ground cover is critical for calibration and validation of fractional ground cover maps derived from satellite imagery. Field-based hyperspectral ground cover sampling is a potential alternative to traditional in situ techniques. This study aimed to develop an effective sampling design for spectral ground cover surveys in order to estimate fractional ground cover in the Australian arid zone. To meet this aim, we addressed two key objectives: (1) Determining how spectral surveys and traditional step-point sampling compare when conducted at the same spatial scale and (2) comparing these two methods to current Australian satellite-derived fractional cover products. Across seven arid, sparsely vegetated survey sites, six 500-m transects were established. Ground cover reflectance was recorded taking continuous hyperspectral readings along each transect while step-point surveys were conducted along the same transects. Both measures of ground cover were converted into proportions of photosynthetic vegetation, non-photosynthetic vegetation, and bare soil for each site. Comparisons were made of the proportions of photosynthetic vegetation, non-photosynthetic vegetation, and bare soil derived from both in situ methods as well as MODIS and Landsat fractional cover products. We found strong correlations between fractional cover derived from hyperspectral and step-point sampling conducted at the same spatial scale at our survey sites. Comparison of the in situ measurements and image-derived fractional cover products showed that overall, the Landsat product was strongly related to both in situ methods for non-photosynthetic vegetation and bare soil whereas the MODIS product was strongly correlated with both in situ methods for photosynthetic vegetation. This study demonstrates the potential of the spectral transect method, both in its ability to produce results comparable to the traditional transect measures, but also in its improved objectivity and relative logistic ease. Future efforts should be made to include spectral ground cover sampling as part of Australia’s plan to produce calibration and validation datasets for remotely sensed products.

scholarly journals Evaluation of Collection-6 MODIS Land Surface Temperature Product Using Multi-Year Ground Measurements in an Arid Area of Northwest China

2018 ◽  
Vol 10 (11) ◽  
pp. 1852 ◽  
Author(s):  
Lei Lu ◽  
Tingjun Zhang ◽  
Tiejun Wang ◽  
Xiaoming Zhou
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Northwest China ◽  
Bare Soil ◽  
Vegetation Monitoring ◽  
Modis Land Surface Temperature ◽  
Global Circulation Models ◽  
In Situ Data

Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products are widely used in ecology, hydrology, vegetation monitoring, and global circulation models. Compared to the collection-5 (C5) LST products, the newly released collection-6 (C6) LST products have been refined over bare soil pixels. This study aims to evaluate the C6 MODIS 1-km LST product using multi-year in situ data covering barren surfaces. Evaluation using all in situ data shows that the MODIS C6 LSTs are underestimated with a root-mean-square error (RMSE) of 2.59 K for the site in the Gobi area, 3.05 K for the site in the sand desert area, and 2.86 K for the site in the desert steppe area at daytime. For nighttime LSTs, the RMSEs are 2.01 K, 2.88 K, and 1.80 K for the three sites, respectively. Both biases and RMSEs also show strong seasonal signals. Compared to the error of C5 1-km LSTs, the RMSE of C6 1-km LST product is smaller, especially for daytime LSTs, with a value of 2.24 K compared to 3.51 K. The large errors in the sand desert region are presumably due to the lack of global representativeness of the magnitude of emissivity adjustment and misclassification for the barren surface causing error in emissivities. It indicates that the accuracy of the MODIS C6 LST product might be further improved through emissivity adjustment with globally representative magnitude and accurate land cover classification. From this study, the MODIS C6 1-km LST product is recommended for applications.

Exploring the use of machine-learning techniques to integrate ground- and remote sensing-based observations for efficient near-surface soil moisture mapping

2020 ◽  
Author(s):  
Sarah Schönbrodt-Stitt ◽  
Paolo Nasta ◽  
Nima Ahmadian ◽  
Markus Kurtenbach ◽  
Christopher Conrad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Spatial Resolution ◽  
Surface Soil ◽  
Bare Soil ◽  
Small Scale ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Campania Region

<p>Mapping near-surface soil moisture (<em>θ</em>) is of tremendous relevance for a broad range of environment-related disciplines and meteorological, ecological, hydrological and agricultural applications. Globally available products offer the opportunity to address <em>θ</em> in large-scale modelling with coarse spatial resolution such as at the landscape level. However, <em>θ</em> estimation at higher spatial resolution is of vital importance for many small-scale applications. Therefore, we focus our study on a small-scale catchment (MFC2) belonging to the “Alento” hydrological observatory, located in southern Italy (Campania Region). The goal of this study is to develop new machine-learning approaches to estimate high grid-resolution (about 17 m cell size) <em>θ</em> maps from mainly backscatter measurements retrieved from C-band Synthetic Aperture Radar (SAR) based on Sentinel-1 (S1) images and from gridded terrain attributes. Thus, a workflow comprising a total of 48 SAR-based <em>θ</em> patterns estimated for 24 satellite overpass dates (revisit time of 6 days) each with ascendant and descendent orbits will be presented. To enable for the mapping, SAR-based <em>θ</em> data was calibrated with in-situ measurements carried out with a portable device during eight measurement campaigns at time of satellite overpasses (four overpass days in total with each ascendant and descendent satellite overpasses per day in November 2018). After the calibration procedure, data validation was executed from November 10, 2018 till March 28, 2019 by using two stationary sensors monitoring <em>θ</em> at high-temporal (1-min recording time). The specific sensor locations reflected two contrasting field conditions, one bare soil plot (frequently kept clear, without disturbance of vegetation cover) and one non-bare soil plot (real-world condition). Point-scale ground observations of <em>θ</em> were compared to pixel-scale (17 m × 17 m), SAR-based <em>θ</em> estimated for those pixels corresponding to the specific positions of the stationary sensors. Mapping performance was estimated through the root mean squared error (RMSE). For a short-term time series of <em>θ</em> (Nov 2018) integrating 136 in situ, sensor-based <em>θ</em> (<em>θ</em><sub>insitu</sub>) and 74 gravimetric-based <em>θ</em> (<em>θ</em><sub>gravimetric</sub>) measurements during a total of eight S1 overpasses, mapping performance already proved to be satisfactory with RMSE=0.039 m³m<sup>-</sup>³ and R²=0.92, respectively with RMSE=0.041 m³m<sup>-</sup>³ and R²=0.91. First results further reveal that estimated satellite-based <em>θ</em> patterns respond to the evolution of rainfall. With our workflow developed and results, we intend to contribute to improved environmental risk assessment by assimilating the results into hydrological models (e.g., HydroGeoSphere), and to support future studies on combined ground-based and SAR-based <em>θ</em> retrieval for forested land (future missions operating at larger wavelengths e.g. NISARL-band, Biomass P-band sensors).</p>

Bare-soil evaporation determined in situ by infrared thermometry

1984 ◽  
Vol 69 (1-4) ◽  
pp. 325-334 ◽  
Author(s):  
J. Ben-Asher ◽  
A.W. Warrick ◽  
A.D. Matthias
Keyword(s):  
Bare Soil ◽  
Soil Evaporation ◽  
Infrared Thermometry ◽  
Bare Soil Evaporation

Simulated sheep urine causes the formation of acidic subsurface layers in soil under field conditions

Soil Research ◽  
2020 ◽  
Vol 58 (7) ◽  
pp. 662
Author(s):  
Jason R. Condon ◽  
A. Scott Black ◽  
Mark K. Conyers
Keyword(s):  
Subsurface Layer ◽  
Soil Surface ◽  
Bare Soil ◽  
Internal Diameter ◽  
N Transformations ◽  
Ammonium N ◽  
Subsurface Layers ◽  
Sampling Times ◽  
Sheep Urine ◽  
Treatment Application

This study aimed to ascertain whether application of sheep urine led to the development of acidic subsurface layers of a pasture soil. Deionised water or simulated urine solution delivering urea-nitrogen (N) at 44.8 g m–2 and potassium at 25 g m–2 was applied to soil in either winter or spring. Treatments were applied to the soil surface within 10.3 cm internal diameter PVC tubes inserted 20 cm into the soil either under ryegrass or kept bare. Main sampling times corresponded to the completion of various soil N transformations as determined by periodic sampling. Main samplings involved the collection of above ground plant material and soil sampling in 2 cm depth increments in 0–10 cm and 5 cm intervals in 10–20 cm depths. Following treatment application, urea and ammonium-N moved to a depth no greater than 20 cm but the extent of movement was greater in winter than spring due to the influence of initial soil moisture. Following urea hydrolysis, soil pH increased in the 0–15 cm depth. Subsequent nitrification significantly acidified soil under pasture by 0.8–1.0 pH units in the 2–8 and 2–6 cm depths in winter and spring respectively. This created a net acidic subsurface layer of 0.2–0.4 pH units compared with soil at the beginning of the experiment. Subsurface acidification was 0.5–0.7 pH units greater in bare soil compared with the presence of pasture. Transformations of N resulting from application of simulated urine solution formed acidic subsurface layers in the field regardless of the season of application.

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