Combining FAO-56 model and ground-based remote sensing to estimate water consumptions of wheat crops in a semi-arid region

Er‐Raki, Salah; Chehbouni, Abdelghani; Guemouria, N.; Duchemin, B.; Ezzahar, Jamal; Hadria, Rachid

doi:10.1016/j.agwat.2006.02.004

Cited by 239 publications

(168 citation statements)

References 24 publications

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“…Many attempts have been made in this direction, with new algorithms proposed to address these other factors. Recently, many studies were conducted to obtain ET and Kc values from remote sensing [39] using the normalized difference vegetation index NDVI [40,41] and its relationship with LAI [42]. Using the improved temporal (five days with Sentinel 2A-B) and spatial (10 m in the visible and infrared region) resolution, ET estimates have the potential to provide crop water requirements over vast areas, as discussed in another contribution to this Special Issue [43].…”

Section: Need For Et Data and Simple Et Modelling Approaches In Irrigmentioning

confidence: 99%

Stress Coefficients for Soil Water Balance Combined with Water Stress Indicators for Irrigation Scheduling of Woody Crops

Ferreira

2017

Horticulturae

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There are several causes for the failure of empirical models to estimate soil water depletion and to calculate irrigation depths, and the problem is particularly critical in tall, uneven, deficit irrigated (DI) crops in Mediterranean climates. Locally measured indicators that quantify water status are useful for addressing those causes and providing feed-back information for improving the adequacy of simple models. Because of their high aerodynamic resistance, the canopy conductance of woody crops is an important factor in determining evapotranspiration (ET), and accurate stress coefficient (Ks) values are needed to quantify the impact of stomatal closure on ET. A brief overview of basic general principles for irrigation scheduling is presented with emphasis on DI applications that require Ks modelling. The limitations of existing technology related to scheduling of woody crops are discussed, including the shortcomings of plant-based approaches. In relation to soil water deficit and/or predawn leaf water potential, several woody crop Ks functions are presented in a secondary analysis. Whenever the total and readily available water data were available, a simple Ks model was tested. The ultimate aim of this discussion is to illustrate the central concept: that a combination of simple ET models and water stress indicators is required for scheduling irrigation of deep-rooted woody crops.

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Section: Need For Et Data and Simple Et Modelling Approaches In Irrigmentioning

confidence: 99%

Stress Coefficients for Soil Water Balance Combined with Water Stress Indicators for Irrigation Scheduling of Woody Crops

Ferreira

2017

Horticulturae

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“…Some examples of this approach consider a linear relationship between Kcb and NDVI [13,20,25,27,28], and between Kcb and SAVI [13,25,26] but an exponential relationship between NDVI and Kc was also considered [29]. A few studies report a Kcb estimation based on fc or LAI obtained through VI, including for field and tree crops [7,30,31]. However, in most studies, the relationship between Kcb and VI was established for non-stressed conditions and for conditions of dry soil surface, which often do not represent the actual conditions of crop management.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Actual Crop Coefficients Using Remotely Sensed Vegetation Indices and Soil Water Balance Modelled Data

et al. 2015

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Abstract:A new procedure is proposed for estimating actual basal crop coefficients from vegetation indices (Kcb VI) considering a density coefficient (Kd) and a crop coefficient for bare soil. Kd is computed using the fraction of ground cover by vegetation (fc VI), which is also estimated from vegetation indices derived from remote sensing. A combined approach for estimating actual crop coefficients from vegetation indices (Kc VI) is also proposed by integrating the Kcb VI with the soil evaporation coefficient (Ke) derived from the soil water balance model SIMDualKc. Results for maize, barley and an olive orchard have shown that the approaches for estimating both fc VI and Kcb VI compared well with results obtained using the SIMDualKc model after calibration with ground observation data. For the crops studied, the correlation coefficients relative to comparing the actual Kcb VI and Kc VI with actual Kcb and Kc obtained with SIMDualKc were larger than 0.73 and 0.71, respectively. The corresponding regression coefficients were close to 1.0. The methodology herein presented and discussed allowed for obtaining information for the whole crop season, including periods when vegetation cover is incomplete, as the initial and development stages. Results show that the proposed methods are adequate for supporting irrigation management. OPEN ACCESSRemote Sens. 2015, 7 2374

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“…Over the growing season results indicated that NDVI had a relationship with K cb for wheat for a semi-arid region in Morocco [Er-Raki et al, 2007]. The…”

Section: Evapotranspiration Estimationmentioning

confidence: 84%

Integrated sensing of soil moisture at the field-scale: Measuring, modeling and sharing for improved agricultural decision support

Phillips

Newlands

Liang

et al. 2014

Computers and Electronics in Agriculture

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Determining the best way to efficiently use limited water resources, for food and energydedicated crops, has become crucial due to the rise in extreme events (floods/droughts) and higher variability in rainfall attributed to global climate change. Changing climate conditions will require new crops to be adapted to a changing agricultural environment. Reliable information on seasonal trends in crop growth and evapotranspiration with associated uncertainty/confidence ranges is crucial to guide the development of new crops and management strategies to cope with future climate. Given that crop growth is strongly coupled to soil moisture, developing reliable growth curves requires a detailed understanding of soil moisture at the field-scale. Typically, it is impractical to collect soil samples to adequately assess soil moisture that represents both spatial distribution at the field-scale and temporal dynamics on the scale of a growing season (e.g. 110 days for cereals). A novel way to address soil moisture monitoring challenges is through an integrated, agro-ecosystems-level approach using an integrated sensing system that can link data from multiple platforms (wireless sensors, satellites, airborne imagery, near real-time climate stations). Assimilated data can then be fed into predictive models to generate reference crop growth curves and predict regionally-specific yield potentials. However, integrated sensing requires interagency cooperation, common data processing standards and long-term, timely access to data. Large databases need to be reusable by various organizations and accessible in the future, with comprehensive metadata. During the 2012 growing season a feasibility study was conducted which involved measuring field-scale soil moisture with sensor network technology. The experiment utilized radially-distributed sensors for tracking in-season soil moisture data was collect using both automated in-situ sensors and hand-held sensors. Box-plots of soil moisture data was collected with hand-held revealed an early season wet soil moisture regime and late season dry soil moisture regime. The data sampled on July 5, 2012 was selected for ii geostatistical analysis. Bayesian kriging models and Bayesian kriging models with polynomial trends using different combinations prior distributions for the range and nugget were tested. Models with first order polynomial trend, a reciprocal 2 prior distribution for the range and a reciprocal prior distribution for the nugget fit tended to predict the sample distributions the best. Soil moisture was predicted at a set of random point using ordinary kriging, universal kriging, the two Bayesian kriging models and the Bayesian kriging models with first and second order polynomial trends. Overall, the universal kriging and Bayesian trend models predicted similar data distributions. OpenGIS-compliant services and standards were utilized to provide long-term access to sensor data and construct corresponding metadata. Sensor Model Language, an inter-operable metadata format, ...

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Combining FAO-56 model and ground-based remote sensing to estimate water consumptions of wheat crops in a semi-arid region

Abstract: Please cite this article in press as: Er-Raki, S. et al., Combining FAO-56 model and ground-based remote sensing to estimate water consumptions of wheat crops in a semi-arid region, Agric. Water Manage. (2006),

Cited by 239 publications

References 24 publications

Stress Coefficients for Soil Water Balance Combined with Water Stress Indicators for Irrigation Scheduling of Woody Crops

Stress Coefficients for Soil Water Balance Combined with Water Stress Indicators for Irrigation Scheduling of Woody Crops

Estimation of Actual Crop Coefficients Using Remotely Sensed Vegetation Indices and Soil Water Balance Modelled Data

Integrated sensing of soil moisture at the field-scale: Measuring, modeling and sharing for improved agricultural decision support

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