scholarly journals Irrigation Scheduling Using Remote Sensing Data Assimilation Approach

2013 ◽  
Vol 02 (03) ◽  
pp. 258-268 ◽  
Author(s):  
Baburao Kamble ◽  
Ayse Irmak ◽  
Kenneth Hubbard ◽  
Prasanna Gowda
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Remote Sensing Data ◽  
Irrigation Scheduling ◽  
Sensing Data

scholarly journals Data assimilation of in situ and satellite remote sensing data to 3D hydrodynamic lake models: a case study using Delft3D-FLOW v4.03 and OpenDA v2.4

2020 ◽  
Vol 13 (3) ◽  
pp. 1267-1284 ◽  
Author(s):  
Theo Baracchini ◽  
Philip Y. Chu ◽  
Jonas Šukys ◽  
Gian Lieberherr ◽  
Stefan Wunderle ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Satellite Remote Sensing ◽  
Model Performance ◽  
Remote Sensing Data ◽  
Lake Ecosystem ◽  
Sensing Data ◽  
Localization Scheme ◽  
Spatiotemporal Scales

Abstract. The understanding of physical dynamics is crucial to provide scientifically credible information on lake ecosystem management. We show how the combination of in situ observations, remote sensing data, and three-dimensional hydrodynamic (3D) numerical simulations is capable of resolving various spatiotemporal scales involved in lake dynamics. This combination is achieved through data assimilation (DA) and uncertainty quantification. In this study, we develop a flexible framework by incorporating DA into 3D hydrodynamic lake models. Using an ensemble Kalman filter, our approach accounts for model and observational uncertainties. We demonstrate the framework by assimilating in situ and satellite remote sensing temperature data into a 3D hydrodynamic model of Lake Geneva. Results show that DA effectively improves model performance over a broad range of spatiotemporal scales and physical processes. Overall, temperature errors have been reduced by 54 %. With a localization scheme, an ensemble size of 20 members is found to be sufficient to derive covariance matrices leading to satisfactory results. The entire framework has been developed with the goal of near-real-time operational systems (e.g., integration into meteolakes.ch).

Combining the FAO56 agrohydrological model and remote sensing data to assess water demand in a Sicilian irrigation district

2020 ◽  
Author(s):  
Matteo Ippolito ◽  
Mario Minacapilli ◽  
Giuseppe Provenzano
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Water Use ◽  
Irrigation Water ◽  
Remote Sensing Data ◽  
Irrigation Scheduling ◽  
Irrigation District ◽  
Sensing Data ◽  
Spatiotemporal Distributions ◽  
Crop Parameters

<p>Agricultural water use in irrigated areas plays a key role in the Mediterranean regions characterized by semi-arid climate and water shortage. In the face of optimizing irrigation water use, farmers must revise their irrigation practices to increase the drought resilience of agricultural systems and to avoid severe damages in agro-ecosystems. In this direction, during the last decades, the research has been focused on mathematical models to simulate the process of driving mass transport and energy exchanges in the Soil-Plant-Atmosphere system.</p><p>The objective of the paper was to test the suitability of the combination of FAO56 agro-hydrological model with remote sensing data retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform, to assess the spatiotemporal distributions of crop water requirement and to schedule irrigation in an irrigation district of the south-west of Sicily, Italy.</p><p>The proposed approach allowed obtaining the spatiotemporal distributions of soil and crop parameters used in the FAO56 model implemented in a GIS environment to simulate the water balance, as well as to assess the actual irrigation strategy. The GIS database was organized to include soil and crop parameters, as well as the irrigation volumes actually delivered to each farmer; the latter data can be used not only as input for water balance to evaluate the efficiency of the actual irrigation strategies but also to identify different irrigation scheduling scenario obtained by the FAO56 procedure.</p><p>The first application was carried out for the period 2014-2017, to identify a combination of irrigation scheduling parameters to be implemented in the model aimed at reproducing the ordinary strategy adopted by the farmers, based on the spatiotemporal variability of soil and climate forcings. When the model outputs were aggregated for single crop types, a fairly good agreement was found between simulated and actual seasonal irrigation volumes delivered either at the level of district and secondary units. Alternative scenarios of irrigation water distribution were then identified and analyzed, to provide irrigation technicians and policymakers a decision support tool to improve the efficiency of irrigation systems and to optimize the distribution based on the availability of water resources.</p>

scholarly journals Remote sensing data assimilation for a prognostic phenology model

2008 ◽  
Vol 113 (G4) ◽  
Author(s):  
R. Stöckli ◽  
T. Rutishauser ◽  
D. Dragoni ◽  
J. O'Keefe ◽  
P. E. Thornton ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Phenology Model

scholarly journals Remote sensing data assimilation in modeling urban dynamics: Objectives and methodology

2011 ◽  
Vol 7 ◽  
pp. 140-145 ◽  
Author(s):  
Johannes van der Kwast ◽  
Frank Canters ◽  
Derek Karssenberg ◽  
Guy Engelen ◽  
Tim Van de Voorde ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Remote Sensing Data ◽  
Urban Dynamics ◽  
Sensing Data

scholarly journals Data assimilation in sea-ice monitoring

2000 ◽  
Vol 31 ◽  
pp. 327-332 ◽  
Author(s):  
Ronald L. S. Weaver ◽  
Konrad Steffen ◽  
John Heinrichs ◽  
James A. Maslanik ◽  
Gregory M. Flato
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Sea Ice ◽  
Global Climate ◽  
Remote Sensing Data ◽  
The Arctic ◽  
Physical Processes ◽  
Error Statistics ◽  
Sensing Data ◽  
Ice Model

AbstractThe detection of small changes in concentration or thickness in the Arctic or Antarctic ice cover is an important topic in the current global-climate-change debate. Change detection using satellite data alone requires rigorous error analysis for their derived ice products, including inter-satellite validation for long time series. All models of physical processes are only approximations, and the best models of complicated physical processes have errors and uncertainties. A promising approach is data assimilation, combining model, in situ data and satellite remote-sensing data. Sea-ice monitoring from satellite, ice-model estimates, and the potential benefit of combining the two are discussed in some detail. In a case-study we demonstrate how the sea-ice backscatter for the Beaufort Sea region was derived using a backscattering model in combination with an ice model. We conclude that, for data assimilation, the first steps include the use of simple models, moving, with success at this level, to progressively more complex models. We also recommend reconfiguring the current remote-sensing data to include precise time tags with each pixel. For example, the current Special Sensor Microwave Imager data might be reissued in a time-tagged orbital (or gridded) format as opposed to the currently available daily averaged gridded data. Finally, error statistics and quality-control information also need to be readily available in a form useful for assimilation. The effectiveness of data-assimilation techniques is directly linked to the availability of data error statistics.

scholarly journals Assessment of Irrigation Water Use Patterns Using Remote Sensing Data in Mexico’s Northeast

10.29007/qz1w ◽  
2018 ◽  
Author(s):  
Saul Arciniega ◽  
Jose A. Breña-Naranjo ◽  
Adrián Pedrozo-Acuña ◽  
Antonio Hernández-Espriú
Keyword(s):  
Remote Sensing ◽  
Water Use ◽  
Irrigation Water ◽  
Land Surface ◽  
Irrigation System ◽  
Functional Model ◽  
Remote Sensing Data ◽  
Irrigation Scheduling ◽  
Sensing Data ◽  
Irrigation Water Use

Irrigation water use (IWU) or withdrawal is a key component for the water management of a region since it tends to exceed the crops consumptive water use, especially in water-stressed regions where groundwater is the main source of water. Nevertheless, temporal IWU information is missing in many irrigation areas. Remote sensing (RS) data is commonly used for crop water requirements estimations in areas with lack of data, however, IWU is more complex to approach since it also depends on water use efficiency, irrigation system type, irrigation scheduling, and water availability, among others. This work explores the use of remote sensing data (TRMM, MODIS) and land surface hydrological products (GLDAS 2 and MERRA 2) to obtain insights about the space-time annual IWU patterns across croplands located within Mexico’s northeast region. Reported IWU in three irrigation districts (Don Martín, Región Lagunera and Bajo Río Bravo) was used to obtain a functional model using satellite data derived. Results suggest strong relationship between reported IWU with soil moisture content from GLDAS and the maximum annual EVI from MODIS, where a potential regression shown statistical correlations of 0.83 and 0.77, respectively.

Remote sensing data assimilation

2020 ◽  
pp. 1-33
Author(s):  
Akhilesh S. Nair ◽  
Rohit Mangla ◽  
Thiruvengadam P ◽  
J. Indu
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Assimilation of LAI Derived from UAV Multispectral Data into the SAFY Model to Estimate Maize Yield

2021 ◽  
Vol 13 (6) ◽  
pp. 1094
Author(s):  
Xingshuo Peng ◽  
Wenting Han ◽  
Jianyi Ao ◽  
Yi Wang
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Crop Yield ◽  
Spatial Resolution ◽  
Remote Sensing Data ◽  
Maize Yield ◽  
Estimation Accuracy ◽  
Corn Yield ◽  
Sensing Data ◽  
Water Treatments

In this study, we develop a method to estimate corn yield based on remote sensing data and ground monitoring data under different water treatments. Spatially explicit information on crop yields is essential for farmers and agricultural agencies to make well-informed decisions. One approach to estimate crop yield with remote sensing is data assimilation, which integrates sequential observations of canopy development from remote sensing into model simulations of crop growth processes. We found that leaf area index (LAI) inversion based on unmanned aerial vehicle (UAV) vegetation index has a high accuracy, with R2 and root mean square error (RMSE) values of 0.877 and 0.609, respectively. Maize yield estimation based on UAV remote sensing data and simple algorithm for yield (SAFY) crop model data assimilation has different yield estimation accuracy under different water treatments. This method can be used to estimate corn yield, where R2 is 0.855 and RMSE is 692.8kg/ha. Generally, the higher the water stress, the lower the estimation accuracy. Furthermore, we perform the yield estimate mapping at 2 m spatial resolution, which has a higher spatial resolution and accuracy than satellite remote sensing. The great potential of incorporating UAV observations with crop data to monitor crop yield, and improve agricultural management is therefore indicated.

Sign in / Sign up

Export Citation Format

Share Document