remotesensing-02-02274-v2

Remote Sensing 2010, 2, 2274-2304; doi:10.3390/rs2092274 OPEN ACCESS

Remote Sensing

ISSN 2072-4292

http://wendang.chazidian.com/journal/remotesensing

Review

Remote Sensing of Irrigated Agriculture: Opportunities and Challenges

Mutlu Ozdogan *, Yang Yang, George Allez and Chelsea Cervantes

Center for Sustainability and the Global Environment (SAGE), University of Wisconsin-Madison, 1710 University Avenue, Madison, WI 53726, USA; E-Mails: yyang72@wisc.edu (Y.Y.);

nikallez@http://wendang.chazidian.com (G.A.); cervantes@wisc.edu (C.C.)

* Author to whom correspondence should be addressed; E-Mail: ozdogan@wisc.edu.

Received: 29 July 2010; in revised form: 15 September 2010 / Accepted: 25 September 2010 / Published: 27 September 2010

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

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.

Keywords: irrigation; agriculture; remote sensing; image classification; resolution

1. Introduction

The intensification of agricultural practices—under the auspices of the “Green Revolution” that includes better seeds, extensive fertilizer use, and irrigation—has dramatically altered the relationship between humans and environmental systems across the world. Today many agricultural lands are being used much more intensively as opportunities for expansion are being exhausted elsewhere. In the last 40 years, global agricultural production has more than doubled—although cropland has increased by only 12%—in part through increased reliance on irrigation [1,2]. Currently, irrigated agriculture is the from lakes, rivers, and groundwater aquifers [3]. As the earth’s population continues to increase and the demand for food, fuel, and fiber rises, continued agricultural intensification will require at least a 50 percent increase in water resources, especially in arid and semi-arid regions [4].

While these modern agricultural practices have successfully increased food production, they have also caused significant environmental change in many regions. Accurate information on the extent of irrigation is thus fundamental to many aspects of Earth System Science, and global change research in general. These aspects include modeling of water exchange between the land surface and atmosphere [5-8], analysis of the impact of climate change and variability on irrigation water requirements and supply [9-13], management of water resources that affect global food security [14], and climatic feedbacks, including the effect that results from evaporative cooling in intensely irrigated arid areas [15,16].

Despite their significance for food security and the water and energy cycles, the extent and distribution of irrigated areas worldwide still remain uncertain [17]. Existing maps, especially those covering large areas, have been derived primarily from country-level statistics. The politically charged nature of irrigation often sets the stage for under-reporting of water use; this is especially true in countries that share resources across borders with their neighbors [18]. Country-level estimates also mask the considerable spatial variability in irrigation practices, and simply cannot reflect the location or extent of irrigation across large areas [19]. Even in countries such as the U.S., where the extent of irrigated areas is known, irrigation-related information exists in disparate sources and cannot be easily synthesized into a single continental scale database [20]. Also, information on irrigated areas in many countries is reported only from officially recognized management units (or command areas) serviced

by large scale irrigation projects. As a result, subsistence-scale irrigation is not reported, although these areas could collectively account for a substantial land area and significant amount of water use. Satellite remote sensing offers tremendous potential for routine monitoring of irrigation due to the synoptic nature of the data and readily available archives of imagery. Yet studies that have used remote sensing to map irrigated lands remain relatively rare. This is a direct result of the complexity associated with trying to map land use as opposed to land cover. While it may be straightforward to detect the high near-infrared signal of mature crops given appropriate spatial, spectral and temporal resolution data (i.e., land cover), detecting irrigation requires knowledge of land management, or some understanding of where and when humans have provided water or supplemented rain-fed crops (i.e., land use). Because of the difficulty in isolating these practices with satellite observations, a literature search reveals only 65 peer-reviewed papers that use remote sensing to map irrigation, compared to thousands that report agricultural or land-cover mapping activities. Thus, from the remote sensing perspective, studies that attempt to map irrigated areas have been rare and scientific consensus on mapping methodologies is fragmented and evolving.

This review seeks to synthesize current studies on identification and mapping of irrigated areas by remote sensing. Our goal is two-fold. First, we will provide a reference guide to the spatial, spectral, and temporal information requirements for monitoring irrigated areas, derived from case studies that have successfully mapped irrigated lands. Second, and more important, we will establish the state-of-the-art in this field by providing a comprehensive assessment and a taxonomic synthesis of studies to date. This information can provide a foundation for future studies to expand on these methods and fill data gaps. The approaches that have been adopted to tackle irrigation are diverse; they vary in scale, extent, data inputs and processing requirements. It is also clear that a consensus within the scientific community as to the ‘best practices’ for mapping irrigation are still evolving, although certain methods appear to be common among different studies. Moreover, reviews such as this portray information needs for timely and accurate monitoring of irrigation. This is necessary in order to form the basis for development of sustainable water management practices within the context of what is perhaps the greatest human intervention in the hydrological cycle. We will begin with a brief discussion of the benefits and drawbacks of remote sensing for mapping crop location, productivity, and change in irrigated settings. Remote sensing has been an effective tool to monitor irrigated lands in many locations around the world under a variety of environmental conditions [19-21,28,33,35,50,60,65]. It provides synoptic coverage of irrigated fields in several spectral regions and with temporal frequencies sufficient to assess vegetation growth, maturity, and harvest. Archived data that span many years allow comparison of images, thus revealing change. The digital nature of satellite data also makes it relatively easy to integrate into a Geographic Information System (GIS) for synthesis or comparison with other data sources. Remotely sensed data are also less costly and time-consuming than traditional statistical surveys that may require aerial photography over large areas. This makes remote sensing particularly valuable for inventories of irrigated land and for monitoring in developing countries, where funds are limited and little objective information is available. Moreover, remote sensing delivers useful spatial information on the exact locations of

irrigated lands rather than mere totals within arbitrary political units. This is important for prioritizing water delivery, assessing irrigation performance, providing irrigation intensities (e.g., single crop vs. double crop), quantifying environmental impact, objectively assessing irrigation water use and understanding changes where irrigation occurs. Finally, remote sensing can provide information on timing, both in the number of irrigation-related vegetation peaks and in the length of time irrigation is utilized over the course of a year.

However, satellite imagery also has limitations. Because of the spatial resolution of most operational imagery (15–60 m), it is difficult to identify small irrigated areas which, taken together, may cover significant parts of the earth. It is also difficult to separate irrigated fields from non-irrigated plots in humid areas because of substantial overlap in their spectral signatures. For example, the signatures of flooded irrigated fields at certain growth stages may overlap with those of natural wetlands, thus limiting accuracy in mapping. Researchers have overcome these limitations by using temporal information on crop planting, maturity, and harvest in conjunction with spectral information [21]. Unfortunately, the collection of remotely-sensed data is fixed by a given satellite’s orbit and return interval, and thus observations are not always captured at ideal times (e.g., green-up or harvest). Optical data availability is also problematic in areas with frequent cloud cover, such as humid tropical and sub-tropical environments.

Having stated this, it is important to point out the technological advances made in remote sensing. For example, satellite constellations such as Rapideye with 5 meter spatial resolution and providing data in five spectral bands has already covered nearly 95% of USA geographic area in less than one year after launch. Further, multi-sensor data fusion (e.g., IRS, Rapideye, Landsat) are becoming increasingly important and feasible. Finally, looking at the limitations of conventional datasets such as subjectivity in data collection and varying statistical design in different studies, limitations from remote sensing by itself are less certain.

The final limitation considered here comes from the fact that identification of agricultural fields using remote sensing is difficult because irrigated landscapes are a subclass of croplands that themselves have traditionally been difficult to map [22-24]. Agricultural fields (and especially irrigated fields) are highly dynamic because each field may be at a different stage of development, and thus subject to being confused with natural land cover classes. Accuracy of land-cover maps is often inversely related to their categorical detail. Since agriculture is already inherently difficult to identify and map, the task of identifying irrigated areas as a subclass of cultivation becomes even more difficult. Perhaps this is where temporal data profiles will be invaluable to separate irrigation from rainfed agriculture as successfully demonstrated by [21,61]. Moreover, ancillary datasets on precipitation and evapotranspiration will come in handy when interpreting these temporal profiles [20].

If we are to identify and map irrigation with remote sensing, a precise definition of what is considered to be irrigated is needed. In this review, we define irrigated lands as areas that receive full or partial application of water by artificial means to offset periods of precipitation shortfalls during the growing period. Fully irrigated areas are those where more than 60 percent of crop water requirements are met artificially; partially irrigated lands (or supplementally irrigated areas) receive between 30 and

60 percent artificially. Note that both surface and groundwater deliveries are included in this definition provided human intervention to move water from one location to another is involved.

Irrigation is practiced in virtually every country, at scales ranging from subsistence farming to national enterprise. The precise location is determined by a combination of factors that include climate, resource availability, crop patterns, and technical expertise. Climate plays an important role in the distribution of irrigation as it determines natural moisture availability (precipitation), crop demand (evaporation), and crop schedules. In humid climates, irrigation often takes the form of a supplemental water supply to meet the excess demand of crops whose growth cycle may be out of sync with natural precipitation. In arid and semi-arid climates, continual irrigation is often necessary to assure agricultural production.

While climate is an important driver of the need for irrigation, it is water availability that primarily determines its existence and sustainability. Currently, groundwater is by far the greater source for irrigation [25]. In regions where withdrawals for agriculture exceed recharge rates, the quantity and quality of groundwater quickly deteriorates, jeopardizing its sustainability. Thus nations may be forced to decide between agricultural and domestic use in their allocation of groundwater. Surface water for irrigation purposes appears more sustainable, but this is deceptive. It requires large structures involving complex engineering such as dams, conveyers, and canals to redistribute the resources. Furthermore, most river basins span international boundaries. Diversions of water for irrigation upstream often reduce its availability downstream, leading to international conflicts as in the case in the Middle East and Central Asia.

2. Review of Existing Studies

In this section, we review existing studies on irrigation mapping with remote sensing and assess data and methodological features that are common and practical. We have chosen spatial scale to categorize these studies. Here spatial scale identifies the scope of the study area and is defined as local, regional, or global. Local studies refer to one or more irrigation basins or command areas. Regional studies include large river basins and continental areas, while global studies present attempts to map irrigation worldwide. This conceptual framework is helpful both to understand the processes involved in each category and to classify mapping approaches. Within each category, the discussion is further organized around the nature of remote sensing imagery and methods of processing these images. In tabular format we describe the advantages and disadvantages of satellite sensors that have been used to identify and map irrigated lands for the following imaging systems: Landsat, Satellite Pour l’Observation de la Terre (SPOT), China-Brazil Earth Resources Satellite (CBERS), Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS), and Indian Remote Sensing Satellite (IRS) (Table 1). Finally, we provide selected examples from the literature for image classification techniques to determine the most successful options in identifying irrigated areas and separating them from other land cover types (Table 2). The accuracy of these classifications is included to give an idea of each method’s success.