Award Date


Degree Type


Degree Name

Master of Science in Engineering (MSE)


Civil and Environmental Engineering and Construction

First Committee Member

Haroon Stephen

Second Committee Member

Sajjad Ahmad

Third Committee Member

David James

Fourth Committee Member

Ashok Singh

Number of Pages



The Imperial Valley (IV) in the US is an extensively irrigated agricultural region, which includes multiple crops changing on an annual and semiannual basis. The valley is facing grave concerns about water management due to its semi-arid environment, water intensive crops, and limited water supply. A simple, inexpensive, and repeatable method to detect changes in cropping patterns may assist irrigation managers to understand crop diversification and associated consumptive use. In addition, a spatial assessment of existing water irrigation system performance and productivity is crucial to benchmark and improve current water management strategies. This thesis estimates the spatial pattern of change in crop distributions from 2018 to 2019 across the IV, using remotely sensed data with high resolution and a machine learning algorithm. Furthermore, it also quantifies the irrigation performance indicators based on the equity, adequacy, and water productivity of water intensive crops utilizing remote sensing, Vegetation indices, and county level crop production statistics.

First, we addressed the spatial analysis of cropland change in an agricultural field of the IV over 2018 and 2019. Optical images from the Sentinel-2 platform were used to develop an annual cropland map using a random forest algorithm in R version 4.0.2. The reflectance from the Sentinel images and Normalized Difference Vegetation Index (NDVI) served as a predictor variable. A cropland data layer was utilized to identify the field’s crop type for ground truthing. We used the dataset provided by the United States Department of Agriculture to access the accuracy of classification. The changes in cropping patterns were quantified by preparing a transition matrix through image the differencing technique in Geographical Information System (GIS). The spatial analysis of change was characterized by generating a map showing the change in cropping proportion for major crop types over the two-year period. We obtained the overall classification accuracy of 85% for each year.

Classification results showed that dominant crops, including alfalfa, mixed grasses, and sugar beet, could be categorized more accurately than scant crops, such as wheat and corn. In terms of total acreage, alfalfa, mixed crops, and mixed grasses increased in 2019, whereas there was reduction in corn, wheat, and sugar beet acreages. A change analysis showed that the spatial variation of alfalfa fields was prominent, whereas mixed grasses were the most stable. The changes mainly occurred in the northeast and southeast of the valley. We found that the wheat intensity reduced significantly in 2019 and was concentrated in the region where expansion of alfalfa, mixed crops, and mixed grasses occurred. The predictor variables of the red edge band and SWIR band were found to be most important in identification of the crops studied. The contribution of NDVI was least among all, and the reason was attributed to the saturation of NDVI at the late season stage, producing an indistinctive signature between crops.

Secondly, we estimated spatially distributed irrigation equity, adequacy, and crop water productivity (CWP) of two water intensive crops, i.e., alfalfa and sugar beet, in the IV, using remotely sensed data and GIS. The analysis was performed for the 2018/2019 crop growing

season. The actual evapotranspiration (ETa) of a crop was mapped utilizing the automated Mapping Evapotranspiration at High Resolution using Internalized Calibration (METRIC) algorithm in Google Earth Engine Evapotranspiration Flux (EEFlux) platform. We utilized the linear interpolation method in R version 4.0.2 to produce daily ETa maps, which were then totaled to compute ETa for the whole season. The within and among field coefficients of variation of water use i.e …. CVw and CVa respectively were computed utilizing the United States Bureau of Reclamation field boundary layer as a measure of irrigation equity. Similarly, Relative Evapotranspiration (RET) was computed to address the adequacy as a ratio of ETa to potential evapotranspiration (ETp). We computed the crop water productivity (CWP) as a ratio of crop yield to crop water use. The yield disaggregation method was employed to map the crop yield, which uses county-level production statistics data and NDVI images as a bridge.

The results were validated with various data reported in the literature, as well as compared with ET from crop coefficient-reference ET (kc-ETo) approach. The relative error of ETas, when compared to literature reported values, were in the range of (7-27) % for alfalfa and (0-3) % for sugar beet. The predicted ETa values and ET computed using kc-ETo approach for different growth stages were different. The average CVws were found to be low; however, spatial variation within fields showed that 36.14% of sugar beet and 34.17% of alfalfa fields had variability greater than 10%. CVas were estimated to be about 19% for both. The relative ET was high, indicating adequate irrigation. About 31.5% of alfalfa fields and 12% of sugar beet fields were consuming water more than its potential visibly, clustered in the central corner of the valley. CWP showed a wide variation with CV of 32.92% for alfalfa and 25.4% for sugar beet, signifying a substantial scope of CWP enhancement.


Agriculture; Crop water use; EEFlux; Irrigation performance; Random Forest; Sustainable goals


Agricultural Science | Agriculture | Remote Sensing | Water Resource Management

File Format


File Size

5400 KB

Degree Grantor

University of Nevada, Las Vegas




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