Monitoring wheat phenology and irrigation in Central Morocco: On the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices

Duchemin, B.; Hadria, Rachid; Er-Raki, Salah; Boulet, G.; Maisongrande, P.; Chehbouni, Abdelghani; Escadafal, Richard; Ezzahar, Jamal; Hoedjes, J.C.B.; Kharrou, Mohamed Hakim; Khabba, Saïd; Mougenot, Bernard; Olioso, Albert; Rodríguez, Julio; Simonneaux, Vincent

doi:10.1016/j.agwat.2005.02.013

Cited by 382 publications

(260 citation statements)

References 57 publications

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“…The NDVI-GLA relationship was established between in situ GLA data derived from hemi-views and NDVI averaged over 3 × 3 pixels centered on the 20 × 20 m 2 sampling units where the photography was taken. The NDVI shows a logarithmic response to GLA (Equation (1) and Figure 3), in agreement with the results obtained in previous studies (e.g., [68][69][70]). The empirical relationship between NDVI and LAI is associated with acceptable statistical indexes, a determination coefficient R (25% in relative value).…”

Section: Vegetation Growthsupporting

confidence: 81%

Impact of Sowing Date on Yield and Water Use Efficiency of Wheat Analyzed through Spatial Modeling and FORMOSAT-2 Images

et al. 2015

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Regional analysis of water use efficiency (WUE) is a relevant method for diagnosing the performance of irrigation systems in water-limited environments. In this study, we investigated the potential of FORMOSAT-2 images to provide spatial estimates of WUE over irrigated wheat crops cultivated within the semi-arid Yaqui Valley, in the northwest of Mexico. FORMOSAT-2 provided us with a unique dataset of 36 images at a high resolution (8 m) encompassing the wheat growing season from November 2007 to May 2008. Time series of green leaf area index were derived from these satellite images and used to calibrate a simple crop/water balance model. The method was applied over an 8 × 8 km 2 irrigated area on up to 530 wheat fields. It allowed us to accurately reproduce the time courses of Leaf Area Index and dry aboveground biomass, as well as evapotranspiration and soil moisture. In a second step, we analyzed the variations of WUE as the ratio of accumulated dry aboveground biomass to seasonal evapotranspiration. Despite the study OPEN ACCESSRemote Sens. 2015, 7 5952 area being rather small and homogeneous (soil, climate), we observed a large range in wheat biomass production, from 5 to 15 t·ha −1 , which was primarily related to the timing of plant emergence. In contrast, the seasonal evapotranspiration only varied from 350 to 450 mm, with no evident link with sowing practices. A significant gain in crop water productivity was found for the fields sown the earliest (maximal WUE around 3.5 kg·m −3 ) compared to those sown the latest (minimal WUE around 1.5 kg·m −3). These results demonstrated the value of the FORMOSAT-2 images to provide spatial estimates of crop production and water consumption. The detailed information provided by such high space and time resolution imaging systems is highly valuable to identify agricultural practices that could enlarge crop water productivity.

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Section: Vegetation Growthsupporting

confidence: 81%

Impact of Sowing Date on Yield and Water Use Efficiency of Wheat Analyzed through Spatial Modeling and FORMOSAT-2 Images

et al. 2015

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“…The SAVI index is considered less sensitive to soil background reflectance variability than other VI, thus with higher potential for application in tree crops with discontinuous ground cover. NDVImax was set to 0.75-0.85 for the three crops, and the value 0.10 was set for NDVImin, both based on reference values for, respectively, an irrigated agricultural field with full cover and a bare soil [48][49][50]. When SAVI was considered, the SAVImin was set to 0.09 while the SAVImax was adjusted to 0.75 according to Mateos et al [7].…”

Section: Basal Crop Coefficients Derived From Reflectance Vegetation mentioning

confidence: 99%

“…Both NDVI and SAVI may be used, with the latter producing better results for incomplete cover crops, e.g., olive orchards. NDVImax and NDVImin were set according to references in the literature [48][49][50] (Table 4). For the super high density olive orchard, SAVImax and SAVImin were considered following values proposed by Mateos et al [7] (Table 4).…”

Section: Basal Crop Coefficients Derived From Reflectance Vegetation mentioning

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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“…The approach is tested using a disaggregated soil moisture dataset and in situ measurement time series collected during the 2010-2013 period at six locations in the Haouz plain, central Morocco [35][36][37][38]. The downscaled dataset is obtained from the disaggregation of 40-km resolution SMOS soil moisture at 1-km resolution using MODIS data and DISPATCH (disaggregation based on physical and theoretical scale change; [17,18]) methodology.…”

Section: Reference Rmsd R B S Lr Space Time Spaceandtimementioning

confidence: 99%

“…The typical field size is about 3-4 ha. The number of irrigation rounds (0-6) and the global amount of water per round (generally more than 60 mm in equivalent-water depth) depend on the dam water levels [35].…”

Section: Study Areamentioning

confidence: 99%

Performance Metrics for Soil Moisture Downscaling Methods: Application to DISPATCH Data in Central Morocco

et al. 2015

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Data disaggregation (or downscaling) is becoming a recognized modeling framework to improve the spatial resolution of available surface soil moisture satellite products. However, depending on the quality of the scale change modeling and on the uncertainty in its input data, disaggregation may improve or degrade soil moisture information at high resolution. Hence, defining a relevant metric for evaluating such methodologies is crucial before disaggregated data can be eventually used in fine-scale studies. In this paper, a new metric, named G DOWN , is proposed to assess the potential gain provided by disaggregation relative to the non-disaggregation case. The performance metric is tested during a four-year period by comparing 1-km resolution disaggregation based on physical and theoretical scale change (DISPATCH) data with the soil moisture measurements collected by six stations in central Morocco. DISPATCH data are obtained every 2-3 days from 40-km resolution SMOS (Soil Moisture Ocean Salinity) and 1-km resolution optical MODIS (Moderate Resolution Imaging Spectroradiometer) data. The correlation coefficient between G DOWN and the disaggregation gain in time series correlation, mean bias and bias in the slope of the linear fit ranges from 0.5 to 0.8. The new metric is found to be a good Remote Sens. 2015, 7 3784 indicator of the overall performance of DISPATCH. Especially, the sign of G DOWN (positive in the case of effective disaggregation and negative in the opposite case) is independent of the uncertainties in SMOS data and of the representativeness of localized in situ measurements at the downscaling (1 km) resolution. In contrast, the traditional root mean square difference between disaggregation output and in situ measurements is poorly correlated (correlation coefficient of about 0.0) with the disaggregation gain in terms of both time series correlation and bias in the slope of the linear fit. The G DOWN approach is generic and thus could help test a range of downscaling methods dedicated to soil moisture and to other geophysical variables.

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Monitoring wheat phenology and irrigation in Central Morocco: On the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices

Cited by 382 publications

References 57 publications

Impact of Sowing Date on Yield and Water Use Efficiency of Wheat Analyzed through Spatial Modeling and FORMOSAT-2 Images

Impact of Sowing Date on Yield and Water Use Efficiency of Wheat Analyzed through Spatial Modeling and FORMOSAT-2 Images

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

Performance Metrics for Soil Moisture Downscaling Methods: Application to DISPATCH Data in Central Morocco

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