Combining Satellite Remote Sensing Data with the FAO-56 Dual Approach for Water Use Mapping In Irrigated Wheat Fields of a Semi-Arid Region

Er‐Raki, Salah; Chehbouni, Abdelghani; Duchemin, B.

doi:10.3390/rs2010375

Cited by 82 publications

(52 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Crop evapotranspiration for middle season growth stage was higher than the other growth stage which agreed with [ER--RAKI et al 2010] remote sensing estimates of ETc that compare very satisfactorily with ground measurements, since the soil evaporation and plant water stress are negligible, and wheat water requirement was higher in the vegetative and mid-season stage and shows decreasing trend toward the maturity stage [GAURAV et al 2010]. Crop coefficient value is a function of irrigation frequency and the evaporative power of atmosphere (ETo).…”

Section: Discussionsupporting

confidence: 72%

Wheat water use and yield under different salinity of irrigation water

Abedinpour

2017

Journal of Water and Land Development

View full text Add to dashboard Cite

A field experiment was conducted for determination of crop coefficient (KC) and water stress coefficient (Ks) for wheat crop under different salinity levels, during 2015-2016. Complete randomized block design of five treatments were considered, i.e., 0.51 dS·m -1 (fresh water, FW) as a control treatment and other four saline water treatments (4, 6, 8 and 10 dS·m -1 ), for S 1 , S 2 , S 3 and S 4 with three replications. The results revealed that the water consumed by plants during the different crop growth stages follows the order of FW > S 1 > S 2 > S 3 > S 4 salinity levels. According to the obtained results, the calculated values of KC significantly differed from values released by FAO paper No 56 for the crops. The Ks values clearly differ from one stage to another because the salt accumulation in the root zone causes to reduction of total soil water potential (Ψ t ), therefore, the average values of water stress coefficient (Ks) follows this order; FW(1.0) = S 1 (1.0) > S 2 (1.0) > S 3 (0.93) > S 4 (0.82). Precise data of crop coefficient, which is required for regional scale irrigation management is lacking in developing countries. Thus, the estimated values of crop coefficient under different variables are essential to achieve the best management practice (BMP) in agriculture.

show abstract

Section: Discussionsupporting

confidence: 72%

Wheat water use and yield under different salinity of irrigation water

Abedinpour

2017

Journal of Water and Land Development

View full text Add to dashboard Cite

show abstract

“…This index is sensitive to the presence of green vegetation [18]. It has been used for several regional and global applications, in studies concerning the distribution and potential photosynthetic activity of vegetation [19][20][21][22][23][24]. Due to its formulation, it robustly describes green vegetation in spite of varying atmospheric conditions in the red and NIR bands [25,26].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Vegetation Behavior in a North African Semi-Arid Region, Using SPOT-VEGETATION NDVI Data

et al. 2011

Self Cite

View full text Add to dashboard Cite

Abstract:The analysis of vegetation dynamics is essential in semi-arid regions, in particular because of the frequent occurrence of long periods of drought. In this paper, multi-temporal series of the Normalized Difference of Vegetation Index (NDVI), derived from SPOT-VEGETATION satellite data between September 1998 and June 2010, were used to analyze the vegetation dynamics over the semi-arid central region of Tunisia. A study of the persistence of three types of vegetation (pastures, annual agriculture and olive trees) is proposed using fractal analysis, in order to gain insight into the stability/instability of vegetation dynamics. In order to estimate the state of vegetation cover stress, we propose evaluating the properties of an index referred to as the Vegetation Anomaly Index (VAI). A positive VAI indicates high vegetation dynamics, whereas a negative VAI indicates the presence of vegetation stress. The VAI is tested for the above three types of vegetation, during the study period from 1998 to 2010, and is compared with other drought indices. The VAI is found to be strongly correlated with precipitation.

show abstract

“…Fractional green canopy cover (Fc) is a readily measured property that is a good indicator of light interception. As such, accurate and efficient estimation of actual Fc might allow improved scheduling and allocation of irrigation water [5][6][7][8]. Several studies have related Fc, or the closely related metric, fractional ground shaded area, to specialty crop water use [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Satellite NDVI Assisted Monitoring of Vegetable Crop Evapotranspiration in California’s San Joaquin Valley

2012

View full text Add to dashboard Cite

Abstract:Reflective bands of Landsat-5 Thematic Mapper satellite imagery were used to facilitate the estimation of basal crop evapotranspiration (ETcb), or potential crop water use, in San Joaquin Valley fields during 2008. A ground-based digital camera measured green fractional cover (Fc) of 49 commercial fields planted to 18 different crop types (row crops, grains, orchard, vineyard) of varying maturity over 11 Landsat overpass dates. Landsat L1T terrain-corrected images were transformed to surface reflectance and converted to normalized difference vegetation index (NDVI). A strong linear relationship between NDVI and Fc was observed (r 2 = 0.96, RMSE = 0.062). The resulting regression equation was used to estimate Fc for crop cycles of broccoli, bellpepper, head lettuce, and garlic on nominal 7-9 day intervals for several study fields. Prior relationships developed by weighing lysimeter were used to transform Fc to fraction of reference evapotranspiration, also known as basal crop coefficient (Kcb). Measurements of grass reference evapotranspiration from the California Irrigation Management Information System were then used to calculate ETcb for each overpass date. Temporal profiles of Fc, Kcb, and ETcb were thus developed for the study fields, along with estimates of seasonal water use. Daily ETcb retrieval uncertainty resulting from error in satellite-based Fc estimation was <0.5 mm/d, with seasonal uncertainty of 6-10%. Results were compared with FAO-56 irrigation guidelines and prior lysimeter observations for reference. OPEN ACCESSRemote Sens. 2012, 4 440

show abstract

Combining Satellite Remote Sensing Data with the FAO-56 Dual Approach for Water Use Mapping In Irrigated Wheat Fields of a Semi-Arid Region

Cited by 82 publications

References 22 publications

Wheat water use and yield under different salinity of irrigation water

Wheat water use and yield under different salinity of irrigation water

Analysis of Vegetation Behavior in a North African Semi-Arid Region, Using SPOT-VEGETATION NDVI Data

Satellite NDVI Assisted Monitoring of Vegetable Crop Evapotranspiration in California’s San Joaquin Valley

Contact Info

Product

Resources

About