scholarly journals Satellite-based assessment of grassland yields

2015 ◽  
Vol XL-7/W3 ◽  
pp. 15-18 ◽  
Author(s):  
K. Grant ◽  
R. Siegmund ◽  
M. Wagner ◽  
S. Hartmann
Keyword(s):  
Land Surface ◽  
Radar Data ◽  
Soil Conditions ◽  
Yield Model ◽  
Sample Number ◽  
Detection Techniques ◽  
Radar Images ◽  
Before And After ◽  
Cost Efficient

Cutting date and frequency are important parameters determining grassland yields in addition to the effects of weather, soil conditions, plant composition and fertilisation. Because accurate and area-wide data of grassland yields are currently not available, cutting frequency can be used to estimate yields. In this project, a method to detect cutting dates via surface changes in radar images is developed. The combination of this method with a grassland yield model will result in more reliable and regional-wide numbers of grassland yields. For the test-phase of the monitoring project, a study area situated southeast of Munich, Germany, was chosen due to its high density of managed grassland. For determining grassland cutting robust amplitude change detection techniques are used evaluating radar amplitude or backscatter statistics before and after the cutting event. CosmoSkyMed and Sentinel-1A data were analysed. All detected cuts were verified according to in-situ measurements recorded in a GIS database. Although the SAR systems had various acquisition geometries, the amount of detected grassland cut was quite similar. Of 154 tested grassland plots, covering in total 436 ha, 116 and 111 cuts were detected using CosmoSkyMed and Sentinel-1A radar data, respectively. Further improvement of radar data processes as well as additional analyses with higher sample number and wider land surface coverage will follow for optimisation of the method and for validation and generalisation of the results of this feasibility study. The automation of this method will than allow for an area-wide and cost efficient cutting date detection service improving grassland yield models.

scholarly journals A New Land Surface Hydrology within the Noah-WRF Land-Atmosphere Mesoscale Model Applied to Semiarid Environment: Evaluation over the Dantiandou Kori (Niger)

2009 ◽  
Vol 2009 ◽  
pp. 1-13 ◽  
Author(s):  
B. Decharme ◽  
C. Ottlé ◽  
S. Saux-Picart ◽  
N. Boulain ◽  
B. Cappelaere ◽  
...  
Keyword(s):  
Land Surface ◽  
Mesoscale Model ◽  
West African Monsoon ◽  
Radar Data ◽  
Rain Gauge ◽  
Surface Energy Budget ◽  
Surface Hydrology ◽  
Land Surface Hydrology ◽  
Rain Gauge Network

Land-atmosphere feedbacks, which are particularly important over the Sahel during the West African Monsoon (WAM), partly depend on a large range of processes linked to the land surface hydrology and the vegetation heterogeneities. This study focuses on the evaluation of a new land surface hydrology within the Noah-WRF land-atmosphere-coupled mesoscale model over the Sahel. This new hydrology explicitly takes account for the Dunne runoff using topographic information, the Horton runoff using a Green-Ampt approximation, and land surface heterogeneities. The previous and new versions of Noah-WRF are compared against a unique observation dataset located over the Dantiandou Kori (Niger). This dataset includes dense rain gauge network, surfaces temperatures estimated from MSG/SEVIRI data, surface soil moisture mapping based on ASAR/ENVISAT C-band radar data and in situ observations of surface atmospheric and land surface energy budget variables. Generally, the WAM is reasonably reproduced by Noah-WRF even if some limitations appear throughout the comparison between simulations and observations. An appreciable improvement of the model results is also found when the new hydrology is used. This fact seems to emphasize the relative importance of the representation of the land surface hydrological processes on the WAM simulated by Noah-WRF over the Sahel.

scholarly journals Mapping Damage-Affected Areas after Natural Hazard Events Using Sentinel-1 Coherence Time Series

2018 ◽  
Vol 10 (8) ◽  
pp. 1272 ◽  
Author(s):  
Stephanie Olen ◽  
Bodo Bookhagen
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Natural Hazard ◽  
Low Cost ◽  
Region Of Interest ◽  
Radar Data ◽  
Coherence Time ◽  
Rapid Identification ◽  
Before And After ◽  
Event Distribution

The emergence of the Sentinel-1A and 1B satellites now offers freely available and widely accessible Synthetic Aperture Radar (SAR) data. Near-global coverage and rapid repeat time (6–12 days) gives Sentinel-1 data the potential to be widely used for monitoring the Earth’s surface. Subtle land-cover and land surface changes can affect the phase and amplitude of the C-band SAR signal, and thus the coherence between two images collected before and after such changes. Analysis of SAR coherence therefore serves as a rapidly deployable and powerful tool to track both seasonal changes and rapid surface disturbances following natural disasters. An advantage of using Sentinel-1 C-band radar data is the ability to easily construct time series of coherence for a region of interest at low cost. In this paper, we propose a new method for Potentially Affected Area (PAA) detection following a natural hazard event. Based on the coherence time series, the proposed method (1) determines the natural variability of coherence within each pixel in the region of interest, accounting for factors such as seasonality and the inherent noise of variable surfaces; and (2) compares pixel-by-pixel syn-event coherence to temporal coherence distributions to determine where statistically significant coherence loss has occurred. The user can determine to what degree the syn-event coherence value (e.g., 1st, 5th percentile of pre-event distribution) constitutes a PAA, and integrate pertinent regional data, such as population density, to rank and prioritise PAAs. We apply the method to two case studies, Sarpol-e, Iran following the 2017 Iran-Iraq earthquake, and a landslide-prone region of NW Argentina, to demonstrate how rapid identification and interpretation of potentially affected areas can be performed shortly following a natural hazard event.

INSAR Monitoring of the Alaska Pipeline Following the November, 2002 Earthquake

2004 ◽  
Author(s):  
Bernhard Rabus ◽  
Adrian McCardle ◽  
Elden Johnson
Keyword(s):  
Land Surface ◽  
Surface Deformation ◽  
Single Point ◽  
Ground Truth ◽  
Cost Effective ◽  
Maximum Displacement ◽  
Detection Techniques ◽  
Before And After ◽  
Radar Satellite ◽  
Pipeline Crossing

Repeat pass interferometry using synthetic aperture radar satellites (INSAR) has been used to measure small movements of the earth’s surface associated with both natural and man-made activities. On November 3, 2002, an earthquake measuring 7.9 on the Richter scale struck Alaska along the Denali fault in the vicinity of the Trans-Alaska pipeline crossing. Images from the RADARSAT 1 satellite were acquired both before and after the earthquake and have been analyzed using a variety of techniques, including INSAR, to: • Illustrate various change detection techniques relevant to pipeline structures and natural features of interest (e.g. slopes and glaciers). • Detect and measure subtle earth movement and evaluate the accuracy and operational usefulness by comparing with ground control. The results show increasing land surface deformation near the pipeline crossing at the fault line, and indicate a maximum displacement of 19 feet. Independent ground truth surveys have confirmed the accuracy of these measured displacements. These results demonstrate that radar satellite observations can successfully be used to detect and assess potential threats to the integrity of pipelines. The benefits include: • Accurate measurements of deformation down to the millimeter level. • Remotely monitoring features without extensive groundwork. • Large spatial extent of two-dimensional InSAR monitoring rather than single point monitoring with GPS. • Cost-effective method to observe potential hazards along pipeline right of way. • Ability to “go back in time” for baseline information through the use of archived satellite images.

scholarly journals Direct impact of solar farm deployment on surface longwave radiation

2021 ◽  
Author(s):  
Chongxing Fan ◽  
Xianglei Huang
Keyword(s):  
Land Surface ◽  
Climate Models ◽  
Longwave Radiation ◽  
In Situ Measurements ◽  
Solar Panels ◽  
Land Type ◽  
Before And After ◽  
Moderate Resolution ◽  
Split Window Algorithm

Abstract Motivated by a previous study of using the Moderate Resolution Imaging Spectroradiometers (MODIS) observations to quantify changes in surface shortwave spectral reflectances caused by six solar farms in the southwest United States, here we used a similar method to study the longwave effects of the same six solar farms, with emphases on surface emissivities and land surface temperature (LST). Two MODIS surface products were examined: one relying on generalized split-window algorithm while assuming emissivities from land cover classifications (MYD11A2), the other based on Temperature Emissivity Separation algorithm capable of dynamically retrieving emissivities (MYD21A2). Both products suggest that, compared to adjacent regions without changes before and after solar farm constructions, the solar farm sites have reduced outgoing radiances in three MODIS infrared window channels. Such reduction in upward longwave radiation is consistent with previous in-situ measurements. The MYD11A2 results show constant emissivities before and after solar farm constructions because its land type classification algorithm is not aware of the presence of solar farms. The estimated daytime and nighttime LST reduction due to solar farm deployment are ~1-4K and ~0.2-0.9K, respectively. The MYD21A2 results indicate a decrease in Band 31 (10.78-11.28 µm) emissivity up to −0.01 and little change in Band 32 (11.77-12.27 µm) emissivity. The LST decreases in the MYD21A2 is slightly smaller than its counterpart in the MYD11A2. Laboratory and in-situ measurements indicate the longwave emissivity of solar panels can be as low as 0.83, considerably smaller than MODIS retrieved surface emissivity over the solar farm sites. The contribution of exposed and shaded ground within the solar farm to the upward longwave radiation needs to be considered to fully explain the results. A synthesis of MODIS observations and published in-situ measurements is presented. Implication for parameterizing such solar farm longwave effect in the climate models is also discussed.

scholarly journals Urban Land Surface Temperature Monitoring and Surface Thermal Runoff Pollution Evaluation Using UAV Thermal Remote Sensing Technology

2021 ◽  
Vol 13 (20) ◽  
pp. 11203
Author(s):  
Shanshan Xu ◽  
Kun Yang ◽  
Yuanting Xu ◽  
Yanhui Zhu ◽  
Yi Luo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Surface Temperature ◽  
Water Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Surface ◽  
Surface Water Temperature ◽  
Before And After

With the continuous advancement of urbanization, the impervious surface expands. Urbanization has changed the structure of the natural land surface and led to the intensification of the urban heat island (UHI) effect. This will affect the surface runoff temperature, which, in turn, will affect the surface water temperature of urban lakes. This study will use UAS TIR (un-manned aerial system thermal infrared radiance) remote sensing and in situ observation technology to monitor the urban space surface temperature and thermal runoff in Kunming, Yunnan, in summer; explore the feasibility of UAS TIR remote sensing to continuously observe urban surface temperature during day and night; and analyze thermal runoff pollution. The results of the study show that the difference between UAS TIR LSTs and in situ LSTs (in situ air temperature 10 cm above the ground.) varies with the type of land covers. Urban surface thermal runoff has varying degrees of impact on water bodies. Based on the influence of physical factors such as vegetation and buildings and meteorological factors such as solar radiation, the RMSE between UAS LSTs and in situ LSTs varies from 1 to 5 °C. Land cover types such as pervious bricks, asphalt, and cement usually show higher RMSE values. Before and after rainfall, the in situ data of the lake surface water temperature (LSWT) showed a phenomenon of first falling and then rising. The linear regression analysis results show that the R2 of the daytime model is 0.92, which has high consistency; the average R2 at night is 0.38; the averages R2 before and after rainfall are 0.50 and 0.83, respectively; and the average RMSE is 1.94 °C. Observational data shows that thermal runoff quickly reaches thermal equilibrium with the land surface temperature about 30 min after rainfall. The thermal runoff around the lake has a certain warming effect on LSWT.

scholarly journals Trends in Land Evapotranspiration over Canada for the Period 1960–2000 Based on In Situ Climate Observations and a Land Surface Model

2007 ◽  
Vol 8 (5) ◽  
pp. 1016-1030 ◽  
Author(s):  
Richard Fernandes ◽  
Vladimir Korolevych ◽  
Shusen Wang
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Surface Radiation ◽  
Soil Conditions ◽  
Surface Model ◽  
Eastern Canada ◽  
Area Index ◽  
Increasing Temperature ◽  
Increasing Trends

Abstract An assessment of annual trends in actual evapotranspiration (AET) and associated meteorological inputs is performed at 101 locations across Canada with available long-term hourly surface climate observations to determine if AET in Canada is increasing in relation to observed increases in air temperature. AET was estimated for the dominant land cover class, with representative soil and leaf area index conditions, within a 50 km × 50 km window around each location for the period 1960–2000. The Ecological Assimilation of Land and Climate Observations (EALCO) land surface model, which simulates coupled carbon, energy, and water cycles, was applied to estimate AET on a half-hourly basis at each location using in situ meteorological measurements and ambient atmospheric CO2 concentrations. Increases in annual AET, of up to 0.73% yr−1, were identified at 81 locations, and decreases, of up to 0.25% yr−1, were found at the remaining 20 stations. Statistically significant increasing trends were detected in 35% of the locations with the majority corresponding to Atlantic and Pacific coastal regions. Increasing trends were generally related to increasing temperature and total downwelling surface radiation trends in eastern Canada and increasing temperature, surface radiation, and precipitation trends in western Canada. In sharp contrast to other studies based on simpler AET models, annual AET trends in the prairie climate zone were mixed in terms of increases and decreases with no locations showing statistically significant trends. Future studies focused on scaling AET model estimates to subbasins or basins are required both to account for this spatial variability in soil conditions and to permit water budget closure validation.

Hyperresolution Land Surface Modeling in the Context of SMAP Cal–Val

2015 ◽  
Vol 17 (1) ◽  
pp. 345-352 ◽  
Author(s):  
Camille Garnaud ◽  
Stéphane Bélair ◽  
Aaron Berg ◽  
Tracy Rowlandson
Keyword(s):  
Soil Moisture ◽  
Soil Temperature ◽  
Land Surface ◽  
Large Scale ◽  
Initial Conditions ◽  
Surface Condition ◽  
Soil Conditions ◽  
Small Scale ◽  
In Situ Observations

Abstract This study explores the performance of Environment Canada’s Surface Prediction System (SPS) in comparison to in situ observations from the Brightwater Creek soil moisture observation network with respect to soil moisture and soil temperature. To do so, SPS is run at hyperresolution (100 m) over a small domain in southern Saskatchewan (Canada) during the summer of 2014. It is shown that with initial conditions and surface condition forcings based on observations, SPS can simulate soil moisture and soil temperature evolution over time with high accuracy (mean bias of 0.01 m3 m−3 and −0.52°C, respectively). However, the modeled spatial variability is generally much weaker than observed. This is likely related to the model’s use of uniform soil texture, the lack of small-scale orography, as well as a predefined crop growth cycle in SPS. Nonetheless, the spatial averages of simulated soil conditions over the domain are very similar to those observed, suggesting that both are representative of large-scale conditions. Thus, in the context of the National Aeronautics and Space Administration’s (NASA) Soil Moisture Active Passive (SMAP) project, this study shows that both simulated and in situ observations can be upscaled to allow future comparison with upcoming satellite data.

Updraft Vertical Velocity Observations and Uncertainties in High Plains Supercells Using Radiosondes and Radars

2020 ◽  
Vol 148 (11) ◽  
pp. 4435-4452
Author(s):  
Peter J. Marinescu ◽  
Patrick C. Kennedy ◽  
Michael M. Bell ◽  
Aryeh J. Drager ◽  
Leah D. Grant ◽  
...  
Keyword(s):  
Convective Cloud ◽  
Radar Data ◽  
High Plains ◽  
State University ◽  
Colorado State University ◽  
Uncertainty Estimates ◽  
Before And After ◽  
Storm Dynamics ◽  
In Situ Observations

AbstractObservations of the air vertical velocities (wair) in supercell updrafts are presented, including uncertainty estimates, from radiosonde GPS measurements in two supercells. These in situ observations were collected during the Colorado State University Convective Cloud Outflows and Updrafts Experiment (C3LOUD-Ex) in moderately unstable environments in Colorado and Wyoming. Based on the radiosonde accelerations, instances when the radiosonde balloon likely bursts within the updraft are determined, and adjustments are made to account for the subsequent reduction in radiosonde buoyancy. Before and after these adjustments, the maximum estimated wair values are 36.2 and 49.9 m s−1, respectively. Radar data are used to contextualize the in situ observations and suggest that most of the radiosonde observations were located several kilometers away from the most intense vertical motions. Therefore, the radiosonde-based wair values presented likely underestimate the maximum values within these storms due to these sampling biases, as well as the impacts from hydrometeors, which are not accounted for. When possible, radiosonde-based wair values were compared to estimates from dual-Doppler methods and from parcel theory. When the radiosondes observed their highest wair values, dual-Doppler methods generally produced 15–20 m s−1 lower wair for the same location, which could be related to the differences in the observing systems’ resolutions. In situ observations within supercell updrafts, which have been limited in recent decades, can be used to improve our understanding and modeling of storm dynamics. This study provides new in situ observations, as well as methods and lessons that could be applied to future field campaigns.

scholarly journals Landslide detection using ALOS optical and radar data. A case study from the Ilia prefecture.

2016 ◽  
Vol 47 (3) ◽  
pp. 1489 ◽  
Author(s):  
K. G. Nikolakopoulos ◽  
Ch. Choussiafis ◽  
V. Karathanassi
Keyword(s):  
Near Infrared ◽  
Radar Data ◽  
Landslide Monitoring ◽  
Data Sets ◽  
Differential Gps ◽  
Radar Images ◽  
The City

In this study the usefulness of the ALOS optical and radar data for landslide monitoring  is  examined.  ALOS  contains  three  sensors,  commonly  referred  to  as  the “three eyes” of ALOS. These sensors are: the Panchromatic Remote-Sensing Instrument for Stereo Mapping (PRISM), the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2), and the Phased Array type L-band Synthetic Aperture Radar (PALSAR). The area of study is located in a small village named Sykies near to the city of Andritsena in Western Peloponnese. The area suffered during the last years from enormous fires. As a result many landslides have been recorded. One of the latest Landslides has been recorded on January 2009 as a consequence of heavy rains. That landslide was mapped in situ using differential GPS. The possibility of detecting and mapping the specific landslide using ALOS data is examined in this study and the results are presented. Thirty ALOS radar images within a period of three years, two ALOS Prism data sets and two ALOS AVNIR collected over the same area within a year were used.

scholarly journals SIGNIFICANT WAVE HEIGHT DETERMINED FROM SEQUENCE OF X-BAND RADAR IMAGES USING TEAGER-HUANG TRANSFORM

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1265-1268 ◽  
Author(s):  
M. R. Mortazavi ◽  
C. J. Huang ◽  
L. C. Wu
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Energy Operator ◽  
Radar Data ◽  
Significant Wave ◽  
Radar Images ◽  
In Situ Data ◽  
Mode Decomposition ◽  
X Band

This work introduces a nonlinear and data-dependant method for extracting the significant wave height from a sequence of X-band radar images, which is based on the Teager-Huang Transform (THH). The THH comprises two parts, which are empirical mode decomposition (EMD) and application of the Teager-Kaiser energy operator (TKEO). EMD is applied to decompose the images into various decompositions, which are narrow-banded and have mono-components; TKEO separates the aforementioned narrow-banded components into their amplitude and frequency. The standard deviation of the separated amplitude is related to <i>Hs</i> , and, the relation is obtained by calibrating radar data with in situ data (buoy). The separated frequencies reveal the orientation and intensity of data, which are directly related to the direction of the waves. For validation, the method was applied to sequences of radar images that were obtained from the west coast of Taiwan. The results obtained using the method indicate that THH can be used specifically to estimate <i>Hs</i> with a root mean square error (RMSE) of 0.34 m. Furthermore, the developed method can efficiently measure the direction of waves at each specific point in an image.

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