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United States Department of Agriculture

Agricultural Research Service

Research Project: IMPROVING WATER PRODUCTIVITY AND NEW WATER MANAGEMENT TECHNOLOGIES TO SUSTAIN RURAL ECONOMIES

Location: Soil and Water Management Research

Title: Utility of multi temporal satellite images for crop water requirements estimation and irrigation management in the Jordan Valley

Authors
item Jitan, Mohammed -
item Ziadat, Feras -
item Evett, Steven

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: August 2, 2012
Publication Date: N/A

Technical Abstract: Identifying the spatial and temporal distribution of crop water requirements is a key for successful management of water resources in the dry areas. Climatic data were obtained from three automated weather stations to estimate reference evapotranspiration (ETO) in the Jordan Valley according to the FAO Penman Montieth equation. Also, environmental data were obtained from the Ministry of Water and Irrigation in Jordan, and compiled within a GIS data format. Two high-resolution images - SPOT 4 (10 m, B2 band of 0.61-0.68) and SPOT 5 (2.5 m, panchromatic) were processed to determine the extent of irrigated areas and to establish a map of the major crops. Land cover mapping with satellite images provided up-to-date essential information for water allocation plans and for crude estimates of irrigation needs, which are helpful in balancing crop water requirements (demand) with available water amounts (supply). Visual classification of these images was ideal to provide the extent of irrigated and non irrigated areas as well as the number and location of greenhouses. The accuracy of visual interpretation was assessed using a confusion matrix and ground check points and the records obtained from MWI-JVA. The overall accuracy was 75.5 % (Kappa Coefficient was 0.70). Although visual interpretation is tedious and time consuming compared to supervised classification, still the use of such an approach is more convenient than the traditional method, which depends on field observations. The classification accuracy came to 100% for green houses, since it was obvious, and the classification was straightforward. Accuracy was low, however, for alfalfa crops (59%), since alfalfa presents a similar pattern to trees and vegetables. However it was easily possible to estimate the fallow area (92% accuracy), which represented the cultivated area, but not planted (irrigated) at the image date. Results showed that the dynamic variation of the NDVI values (a satellite derived "greenness" factor) during the growing season allow ETC estimation as well as the identification of land cover classes. Criteria related to crop phenology, such as the minimum and the maximum values of NDVI, were used to correlate to crop stages and to values of crop coefficient (KC), and then used for both ETC estimation and land cover characterization. Values of KC obtained from FAO were correlated with NDVI values for four ASTER images acquired during one growing season. The results indicated that the proposed approach could provide valuable information about irrigation water requirements. Such information is helpful in dry years to recommend a cropping pattern that considers the supply-demand balance. Furthermore, these results are important for the operation, maintenance and design of the hydraulic system. The results are also helpful for future planning to build dams and reservoirs to provide adequate irrigation water and bridge the gap between demand and supply. The approach followed in this study could be out-scaled to other irrigated areas.

Last Modified: 11/28/2014
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