Your search found 3 records
1 de Santa Olalla, F. M.; Calera, A.; DomÃnguez, A. 2003. Monitoring irrigation water use by combining irrigation advisory service, and remotely sensed data with a geographic information system. Agricultural Water Management, 61(2):111-124.
GIS ; Remote sensing ; Monitoring ; Evapotranspiration ; Water requirements ; Groundwater ; Aquifers ; Irrigated farming
(Location: IWMI-HQ Call no: PER Record No: H032159)
2 Pocas, I.; Calera, A.; Campos, I.; Cunha, M. 2020. Remote sensing for estimating and mapping single and basal crop coefficientes: a review on spectral vegetation indices approaches. Agricultural Water Management, 233:106081. [doi: https://doi.org/10.1016/j.agwat.2020.106081]
Remote sensing ; Crops ; Water requirements ; Evapotranspiration ; Vegetation index ; Irrigation management ; Soil water balance ; Soil moisture ; Earth observation satellites ; Landsat ; Geographical information systems ; Monitoring ; Water stress ; Mapping ; Models
(Location: IWMI HQ Call no: e-copy only Record No: H049654)
(0.77 MB)
The advances achieved during the last 30 years demonstrate the aptitude of the remote sensing-based vegetation indices (VI) for the assessment of crop evapotranspiration (ETc) and irrigation requirements in a simple, robust and operative manner. The foundation of these methodologies is the well-established relationship between the VIs and the basal crop coefficient (Kcb), resulting from the ability of VIs to measure the radiation absorbed by the vegetation, as the main driver of the evapotranspiration process. In addition, VIs have been related with single crop coefficient (Kc), assuming constant rates of soil evaporation. The direct relationship between VIs and ET is conceptually incorrect due to the effect of the atmospheric demand on this relationship. The rising number of Earth Observation Satellites potentiates a data increase to feed the VI-based methodologies for estimating and mapping either the Kc or Kcb, with improved temporal coverage and spatial resolution. The development of operative platforms, including satellite constellations like Sentinels and drones, usable for the assessment of Kcb through VIs, opens new possibilities and challenges. This work analyzes some of the questions that remain inconclusive at scientific and operational level, including: (i) the diversity of the Kcb-VI relationships defined for different crops, (ii) the integration of Kcb-VI relationships in more complex models such as soil water balance, and (iii) the operational application of Kcb-VI relationships using virtual constellations of space and aerial platforms that allow combining data from two or more sensors.
3 Garrido-Rubio, J.; Gonzalez-Piqueras, J.; Campos, I.; Osann, A.; Gonzalez-Gomez, L.; Calera, A.. 2020. Remote sensing-based soil water balance for irrigation water accounting at plot and water user association management scale. Agricultural Water Management, 238:106236. (Online first). [doi: https://doi.org/10.1016/j.agwat.2020.106236]
Irrigation water ; Water accounting ; Remote sensing ; Soil water balance ; Water user associations ; Water management ; Irrigated farming ; Wheat ; Maize ; Barley ; Evapotranspiration ; Irrigated sites ; Satellite imagery ; Monitoring ; Models / Spain
(Location: IWMI HQ Call no: e-copy only Record No: H049697)
(3.35 MB)
Irrigation water accounting (IWA) plays a key role in irrigation management in arid or semi-arid environments. Currently, water managers perform IWA through indirect or direct measurements such as statistical methods or flow meters. However, they have a high maintenance cost and great efforts must be done when large irrigated areas must be covered. The presented framework based on the dual crop coefficient FAO56 methodology introduces an operative application of a Remote Sensing-based Soil Water Balance (RS-SWB) to obtain a Remote Sensing-based Irrigation Water Accounting (RS-IWA). A basic input of the model is the time series of basal crop coefficient and fractional vegetation cover. It has been implemented in a large water user association (100,000 ha) along three years (2010-2012). The results are analysed from the perspective of two water management scales: the plot and the water user association. At plot scale, the RS-IWA of maize and wheat, as primary crops irrigated on demand, show a root square mean error (RMSE) of about 12 % compared with the records from local farmers. At water user association management scale, the results from RS-IWA show an RMSE of about 15 % for a comprehensive range of irrigated crops group such as spring crops, summer crops, double harvest, alfalfa, and vineyards. Hence, RS-IWA based on RS-SWB offers reproducible and reliable mapped estimations that can be used for different water managers, as they are being required from actual agro-environmental laws that are pushing these actors to better knowledge in time and space of those water resources applied.
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