Remote Sensing of Irrigated Agriculture: Opportunities and Challenges

Abstract

Over the last several decades, remote sensing has emerged as an effective tool to monitor irrigated lands over a variety of climatic conditions and locations. The objective of this review, which summarizes the methods and the results of existing remote sensing studies, is to synthesize principle findings and assess the state of the art. We take a taxonomic approach to group studies based on location, scale, inputs, and methods, in an effort to categorize different approaches within a logical framework. We seek to evaluate the ability of remote sensing to provide synoptic and timely coverage of irrigated lands in several spectral regions. We also investigate the value of archived data that enable comparison of images through time. This overview of the studies to date indicates that remote sensing-based monitoring of irrigation is at an intermediate stage of development at local scales. For instance, there is overwhelming consensus on the efficacy of vegetation indices in identifying irrigated fields. Also, single date imagery, acquired at peak growing season, may suffice to identify irrigated lands, although to multi-date image data are necessary for improved classification and to distinguish different crop types. At local scales, the mapping of irrigated lands with remote sensing is also strongly affected by the timing of image acquisition and the number of images used. At the regional and global scales, on the other hand, remote sensing has not been fully operational, as methods that work in one place and time are not necessarily transferable to other locations and periods. Thus, at larger scales, more work is required to indentify the best spectral indices, best time periods, and best classification methods under different climatological and cultural environments. Existing studies at regional scales also establish the fact that both remote sensing and national statistical approaches require further refinement with a substantial OPEN ACCESSRemote Sensing 2010, 2 2275 investment of time and resources for ground-truthing. An additional challenge in mapping irrigation across large areas occurs in fragmented landscapes with small irrigated and cultivated fields, where the spatial scale of observations is pitted against the need for high frequency temporal acquisitions. Finally, this review identifies passive and active microwave observations, advanced image classification methods, and data fusion including optical and radar sensors or with information from sources with multiple spatial and temporal characteristics as key areas where additional research is needed.

Identifying Irrigated Areas in the Snake River Plain, Idaho: Evaluating Performance across Composting Algorithms, Spectral Indices, and Sensors

“…Ozdogan et al [5,6] demonstrate that using a single observation date for NDVI, preferably during the peak of the growing season, is sufficient for distinguishing irrigated from non-irrigated land use using Landsat data in southeastern Turkey. When applying this method to other regions, however, classification errors may arise if farmers use inconsistent harvesting and irrigation practices.…”

“…We thus introduce methods that extend the window of observation from a single date to a time interval that captures within-season differences in greenness trajectories between irrigated and non-irrigated sites. We evaluate two new algorithms and the existing, single-date algorithm from Ozdogan et al [5,6] for classifying irrigated lands in semi-arid and arid regions using multispectral data from both MODIS and Landsat. The two new algorithms classify land use based on summary statistics that describe the trajectory of greenness over the duration of the growing season (April to October).…”

“…Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat have been found to be the most effective imagery for observing differences in NDVI in semi-arid and arid regions [6]. The MODIS instrument, aboard NASA's Aqua and Terra satellites, has been applied to map land use using multiple reflectance spectra and vegetation indices [17,20].…”

“…Remote sensing yields a source of readily available data that can be used to identify and quantify irrigated area on an annual timescale based on within-season trends in greenness. In particular, the Normalized Difference Vegetation Index (NDVI), derived using near infrared and red reflectance, has been used for mapping irrigated areas on the local scale and is effective because of differential spectral responses between irrigated and non-irrigated croplands [6].…”

Applying Earth Observation to Detect Non-Authorised Irrigation: The Case Study of Consorzio Sannio Alifano (Italy)

“…Several authors point out that satellitebased technology, particularly those related to Earth observation, may be an effective tool for mapping irrigated areas all over the world and at different spatial scales (global, regional, local) (Ozdogan et al, 2010) (D'Urso et al, 2010).…”