Crop Water Requirement and Irrigation by Remote Sensing

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Water Use and Irrigation".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 1476

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Guest Editor
Agricultural Research Institute Nicosia, P.O.Box 22016, Nicosia 1516, Cyprus
Interests: water resources manageme; remote sensing in agriculture and environment
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Special Issue Information

Dear colleagues,

Water resources are essential to production in primary, secondary and tertiary sectors, as well as to household consumption. Throughout the years, overexploitation of water resources has led to their degradation and depletion globally.

Remote sensing for hydrological purposes is used to improve farm-level irrigation management and fight water scarcity. The possibility for monitoring irrigation demand from space is catalytic for policy makers. The increased accuracy can lead to a reduction in water for irrigation and improve water reservoir management. In addition, at the micro-economic and producers’ levels, improved irrigation management can have a positive effect on the economics of farms.

The use of remotely sensed data is very useful for the deployment of water strategies because it can offer a huge amount of information in a short time, compared to conventional methods. Besides the convenience and reduction in time, remotely sensed data lessen the costs for data acquisition, especially when the area is extended. The potential of remote sensing techniques in irrigation and water resource management has been widely acknowledged. Multispectral images from many different sensors are used to infer crop water requirements, which are the main input for water balance methods and models.

This Special Issue titled “Crop Water Requirement and Irrigation by Remote Sensing’’ has the clear purpose of providing, in a scientific way, real cases of how remote sensing techniques are used to infer crop water requirements and their application to the irrigation of different plants worldwide.

Dr. George Papadavid
Guest Editor

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Keywords

  • remote sensing
  • irrigation management
  • crop water requirements
  • evapotranspiration
  • algorithms

Published Papers (1 paper)

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Research

16 pages, 2618 KiB  
Article
Effect of Dynamic PET Scaling with LAI and Aspect on the Spatial Performance of a Distributed Hydrologic Model
by Utku Demirci and Mehmet Cüneyd Demirel
Agronomy 2023, 13(2), 534; https://doi.org/10.3390/agronomy13020534 - 13 Feb 2023
Viewed by 1174
Abstract
The spatial heterogeneity in hydrologic simulations is a key difference between lumped and distributed models. Not all distributed models benefit from pedo-transfer functions based on the soil properties and crop-vegetation dynamics. Mostly coarse-scale meteorological forcing is used to estimate only the water balance [...] Read more.
The spatial heterogeneity in hydrologic simulations is a key difference between lumped and distributed models. Not all distributed models benefit from pedo-transfer functions based on the soil properties and crop-vegetation dynamics. Mostly coarse-scale meteorological forcing is used to estimate only the water balance at the catchment outlet. The mesoscale Hydrologic Model (mHM) is one of the rare models that incorporate remote sensing data, i.e., leaf area index (LAI) and aspect, to improve the actual evapotranspiration (AET) simulations and water balance together. The user can select either LAI or aspect to scale PET. However, herein we introduce a new weight parameter, “alphax”, that allows the user to incorporate both LAI and aspect together for potential evapotranspiration (PET) scaling. With the mHM code enhancement, the modeler also has the option of using raw PET with no scaling. In this study, streamflow and AET are simulated using the mHM in The Main Basin (Germany) for the period of 2002–2014. The additional value of PET scaling with LAI and aspect for model performance is investigated using Moderate Resolution Imaging Spectroradiometer (MODIS) AET and LAI products. From 69 mHM parameters, 26 parameters are selected for calibration using the Optimization Software Toolkit (OSTRICH). For calibration and evaluation, the KGE metric is used for water balance, and the SPAEF metric is used for evaluating spatial patterns of AET. Our results show that the AET performance of the mHM is highest when using both LAI and aspect indicating that LAI and aspect contain valuable spatial heterogeneity information from topography and canopy (e.g., forests, grasslands, and croplands) that should be preserved during modeling. This is key for agronomic studies like crop yield estimations and irrigation water use. The additional “alphax” parameter makes the model physically more flexible and robust as the model can decide the weights according to the study domain. Full article
(This article belongs to the Special Issue Crop Water Requirement and Irrigation by Remote Sensing)
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