scholarly journals Crop Nitrogen Status Assessment Tools in a Decision Support System for Nitrogen Fertilization Management of Potato Crops

2011 ◽  
Vol 21 (3) ◽  
pp. 282-286 ◽  
Author(s):  
Jean-Pierre Goffart ◽  
Marguerite Olivier ◽  
Marc Frankinet
Keyword(s):  
Remote Sensing ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Agricultural Research ◽  
Chlorophyll Concentration ◽  
Balance Sheet ◽  
Assessment Tools ◽  
Field Scale ◽  
Total N

A decision support system (DSS) was based on the splitting of total nitrogen (N) fertilizer application combined with in-season assessments of crop N requirements aimed to matching, at field scale, potato (Solanum tuberosum) total crop N requirements and mineral N supply from soil and fertilizers. After the preplanting establishment of the total N recommendation based on the predictive balance-sheet method at a specific field scale, 70% of the recommended amount was applied to the crop at planting. Subsequently, at 20–50 days after emergence (DAE) the need for supplemental N was assessed through noninvasive measurements of leaf chlorophyll concentration directly in the field. A simple conditional relationship was established to support potato growers’ decisions on the usefulness of applying the remaining 30% N. This required a crop N status (CNS) assessment in the fertilized field and within a small, untreated area (zero-N for reference). The strategy developed is economically feasible, easy to operate, and validated for several potato varieties. It also gives the grower the possibility of improving N use efficiency (NUE). Several tools to assess CNS have been investigated, or are currently being investigated, at the Walloon Agricultural Research Center in Gembloux, Belgium (CRA-W) for integration into this strategy. All the tools are evaluated for four main characteristics: measurement accuracy and precision, sensitivity to N, specificity to N, and feasibility. There are invasive or noninvasive tools. The use of a chlorophyll meter (CM) has been currently developed in the DSS. Current CRA-W research is investigating the potential of crop light reflectance as an indicator of CNS (ground-based radiometers for near remote sensing and satellite multispectral imagery for spatial remote sensing).

scholarly journals An Efficient Decision Support System for Flood Inundation Management Using Intermittent Remote-Sensing Data

2021 ◽  
Vol 13 (14) ◽  
pp. 2818
Author(s):  
Hai Sun ◽  
Xiaoyi Dai ◽  
Wenchi Shou ◽  
Jun Wang ◽  
Xuejing Ruan
Keyword(s):  
Remote Sensing ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Water Body ◽  
Remote Sensing Data ◽  
Flood Inundation ◽  
Remote Sensing Images ◽  
Sensing Data ◽  
Tracking Model

Timely acquisition of spatial flood distribution is an essential basis for flood-disaster monitoring and management. Remote-sensing data have been widely used in water-body surveys. However, due to the cloudy weather and complex geomorphic environment, the inability to receive remote-sensing images throughout the day has resulted in some data being missing and unable to provide dynamic and continuous flood inundation process data. To fully and effectively use remote-sensing data, we developed a new decision support system for integrated flood inundation management based on limited and intermittent remote-sensing data. Firstly, we established a new multi-scale water-extraction convolutional neural network named DEU-Net to extract water from remote-sensing images automatically. A specific datasets training method was created for typical region types to separate the water body from the confusing surface features more accurately. Secondly, we built a waterfront contour active tracking model to implicitly describe the flood movement interface. In this way, the flooding process was converted into the numerical solution of the partial differential equation of the boundary function. Space upwind difference format and the time Euler difference format were used to perform the numerical solution. Finally, we established seven indicators that considered regional characteristics and flood-inundation attributes to evaluate flood-disaster losses. The cloud model using the entropy weight method was introduced to account for uncertainties in various parameters. In the end, a decision support system realizing the flood losses risk visualization was developed by using the ArcGIS application programming interface (API). To verify the effectiveness of the model constructed in this paper, we conducted numerical experiments on the model's performance through comparative experiments based on a laboratory scale and actual scale, respectively. The results were as follows: (1) The DEU-Net method had a better capability to accurately extract various water bodies, such as urban water bodies, open-air ponds, plateau lakes etc., than the other comparison methods. (2) The simulation results of the active tracking model had good temporal and spatial consistency with the image extraction results and actual statistical data compared with the synthetic observation data. (3) The application results showed that the system has high computational efficiency and noticeable visualization effects. The research results may provide a scientific basis for the emergency-response decision-making of flood disasters, especially in data-sparse regions.

scholarly journals A novel decision support system for the interpretation of remote sensing big data

2017 ◽  
Vol 11 (1) ◽  
pp. 31-45 ◽  
Author(s):  
Wadii Boulila ◽  
Imed Riadh Farah ◽  
Amir Hussain
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Decision Support ◽  
Decision Support System ◽  
Support System

scholarly journals A Field-Scale Decision Support System for Assessment and Management of Soil Functions

2019 ◽  
Vol 7 ◽  
Author(s):  
Marko Debeljak ◽  
Aneta Trajanov ◽  
Vladimir Kuzmanovski ◽  
Jaap Schröder ◽  
Taru Sandén ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Soil Functions ◽  
Field Scale
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