A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: An application example in the continental US

We present a dryland irrigation mapping methodology that relies on remotely sensed inputs from the MODerate Resolution Imaging Spectroradiometer (MODIS) instrument, globally extensive ancillary sources of gridded climate and agricultural data and on an advanced image classification algorithm. The methodology involves four steps. First, we use climate-based indices of surface moisture status and a map of cultivated areas to generate a potential irrigation index. Next, we identify remotely-sensed temporal and spectral signatures that are associated with presence of irrigation defined as full or partial artificial application of water to agricultural areas under dryland conditions excluding irrigated pastures, paddy rice fields, and other semi-aquatic crops. Here, the temporal indices are based on the difference in annual evolution of greenness between irrigated and non-irrigated crops, while spectral indices are based on the reflectance in the green and are sensitive to vegetation chlorophyll content associated with moisture stress. Third, we combine the climate-based potential irrigation index, remotely sensed indices, and learning samples within a decision tree supervised classification tool to make a binary irrigated/non-irrigated map. Finally, we apply a tree-based regression algorithm to derive the fraction of irrigated area within each pixel that has been identified as irrigated. Application of the proposed procedure over the continental US in the year 2001 produces an objective and comprehensive map that exhibits expected patterns: there is a strong east-west divide where the majority of irrigated areas is concentrated in the arid west along dry lowland valleys. Qualitative assessment of the map across different climatic and agricultural zones reveals a high quality product with sufficient detail when compared to existing large area irrigation databases. Accuracy assessment indicates that the map is highly accurate in the western US but less accurate in the east. Comparison of area estimates made with the new procedure to those reported at the state and county levels shows a strong correlation with a small bias and an estimated RMSE of 2500 km2, or little over 2% of the total irrigated area in the US. As a result, the future application of the new procedure at a global scale is promising but may require a better potential irrigation index, as well as the use of remotely sensed skin temperature measurements

Characterizing the Effects of Irrigation in the Middle East and North Africa Using Remotely Sensed Vegetation and Water Cycle Observations

A majority of the countries in the Middle East and North Africa (MENA) region suffer from water scarcity due in part to widespread rainfall deficits, unprecedented levels of water demand, and the inefficient use of renewable freshwater resources. Since a majority of the water withdrawal in the MENA is used for irrigation, there is a desperate need for improved understanding of irrigation practices and agricultural water use in the region. Here, satellite-derived irrigation maps and crop-type agricultural data are applied to the Land Data Assimilation System for the MENA region (MENA LDAS), designed to provide regional, gridded fields of hydrological states and fluxes relevant for water resources assessments. Within MENA-LDAS, the Catchment Land Surface Model (CLSM) simulates the location, timing, and amount of water applied through agricultural irrigation practices over the region from 2002-2012. In addition to simulating the irrigation impact on evapotranspiration, soil moisture, and runoff, we also investigate regional changes in terrestrial water storage (TWS) observed from the Gravity Recovery and Climate Experiment (GRACE) and simulated by CLSM

Changes in Summer Irrigated Crop Area and Water Use in Southeastern Turkey from 1993 to 2002: Implications for Current and Future Water Resources

Changes in summer irrigated cropland acreage and related water use are estimated from satellite remote sensing and ancillary data in semi-arid Southeastern Turkey where traditionally dry agricultural lands are being rapidly transformed into irrigated fields with the help of water from the Euphrates-Tigris Rivers. An image classification methodology based on thresholding of Landsat NDVI images from the peak summer period reveals that the total area of summer irrigated crops has increased three-fold (from 35,000 ha to over 100,000) in the Harran Plain between 1993 and 2002. Coupled analysis of annual irrigated crop area from remote sensing and potential evapotranspiration based estimates of irrigation water requirements for cotton indicate a corresponding increase in agricultural water use from about 370 million cubic meters to over one billion cubic meters, a volume in accordance with the state estimates. These estimates have important implications for understanding the rapid changes in current agricultural withdrawals in Southeastern Turkey and form a quantitative basis for exploring the changes in future water demands in the region. For example, expansion of irrigated lands have led to a steady decrease in potential evaporation due to increased roughness and decreased humidity deficit in the Harran Plain. Assuming that the changes in future evaporation conditions will be of similar nature, water use for irrigation is expected to decrease over 40 percent in future irrigation sites. Incorporating this decrease in overall planning of the irrigation projects currently under construction should lead to improved management, and by extension, sustainability of water resources in the region

How Universal Is the Relationship Between Remotely Sensed Vegetation Indices (VI) and Crop Leaf Area Index (LAI)?

Global LAI-VI relationships are statistically significant, crop-specific, and mostly non-linear. This research enables the operationalization of large-area crop modeling and, by extension, has relevance to both fundamental and applied agroecosystem research

Simulating the Effects of Irrigation over the U.S. in a Land Surface Model Based on Satellite Derived Agricultural Data

A novel method is introduced for integrating satellite derived irrigation data and high-resolution crop type information into a land surface model (LSM). The objective is to improve the simulation of land surface states and fluxes through better representation of agricultural land use. Ultimately, this scheme could enable numerical weather prediction (NWP) models to capture land-atmosphere feedbacks in managed lands more accurately and thus improve forecast skill. Here we show that application of the new irrigation scheme over the continental US significantly influences the surface water and energy balances by modulating the partitioning of water between the surface and the atmosphere. In our experiment, irrigation caused a 12% increase in evapotranspiration (QLE) and an equivalent reduction in the sensible heat flux (QH) averaged over all irrigated areas in the continental US during the 2003 growing season. Local effects were more extreme: irrigation shifted more than 100 W/m from QH to QLE in many locations in California, eastern Idaho, southern Washington, and southern Colorado during peak crop growth. In these cases, the changes in ground heat flux (QG), net radiation (RNET), evapotranspiration (ET), runoff (R), and soil moisture (SM) were more than 3 W/m(sup 2), 20 W/m(sup 2), 5 mm/day, 0.3 mm/day, and 100 mm, respectively. These results are highly relevant to continental- to global-scale water and energy cycle studies that, to date, have struggled to quantify the effects of agricultural management practices such as irrigation. Based on the results presented here, we expect that better representation of managed lands will lead to improved weather and climate forecasting skill when the new irrigation scheme is incorporated into NWP models such as NOAA's Global Forecast System (GFS)

An Integrated Hydrological and Water Management Study of the Entire Nile River System - Lake Victoria to Nile Delta

The Nile basin River system spans 3 million km(exp 2) distributed over ten nations. The eight upstream riparian nations, Ethiopia, Eretria, Uganda, Rwanda, Burundi, Congo, Tanzania and Kenya are the source of approximately 86% of the water inputs to the Nile, while the two downstream riparian countries Sudan and Egypt, presently rely on the river's flow for most of the their needs. Both climate and agriculture contribute to the complicated nature of Nile River management: precipitation in the headwaters regions of Ethiopia and Lake Victoria is variable on a seasonal and inter-annual basis, while demand for irrigation water in the arid downstream region is consistently high. The Nile is, perhaps, one of the most difficult trans-boundary water issue in the world, and this study would be the first initiative to combine NASA satellite observations with the hydrologic models study the overall water balance in a to comprehensive manner. The cornerstone application of NASA's Earth Science Research Results under this project are the NASA Land Data Assimilation System (LDAS) and the USDA Atmosphere-land Exchange Inverse (ALEXI) model. These two complementary research results are methodologically independent methods for using NASA observations to support water resource analysis in data poor regions. Where an LDAS uses multiple sources of satellite data to inform prognostic simulations of hydrological process, ALEXI diagnoses evapotranspiration and water stress on the basis of thermal infrared satellite imagery. Specifically, this work integrates NASA Land Data Assimilation systems into the water management decision support systems that member countries of the Nile Basin Initiative (NBI) and Regional Center for Mapping of Resources for Development (RCMRD, located in Nairobi, Kenya) use in water resource analysis, agricultural planning, and acute drought response to support sustainable development of Nile Basin water resources. The project is motivated by the recognition that accurate, frequent, and spatially distributed estimates of the water balance are necessary for effective water management. This creates a challenge for watersheds that are large, include data poor regions, and/or span multiple nations. All of these descriptors apply to the Nile River basin, yet successful management of the Nile is critical for development and political stability in the region. For this reason, improved hydrological data to support cooperative water management in the Nile basin is a priority for USAID, the US State Department, the World Bank and other international organizations. In this project, the U.S. based research team is working with partners at RCMRD, Nile Basin Initiative (NBI), and their member national-level agencies to develop satellite-based land cover maps, satellite-derived evapotranspiration estimates (using the ALEXI algorithm), and NASA's Land Data Assimilation System (LDAS) customized to match identified information needs. The cornerstone applied sciences product of the project is the development of a customized "Nile LDAS" that will produce optimal estimates of hydrological states and fluxes, as vetted against the in situ observations of NBI and RCMRD member organizations and independent satellite-derived hydrological estimates. Nile LDAS will be applied to improve the reliability of emerging Decision Support Systems in applications that include drought monitoring, reservoir management, and irrigation planning. The end-users such as RCMRD, NBI, Ethiopian and Kenya Meteorological and Famine Early Warning System Network (FEWSNet) will be the eventual benefactors of this work. There will be a capacity building process involving the above end-user organizations and transfer the models and the results for these organizations to execute for future use. The team has already initiated this study and the early results of first years' work are shown. The plan is to complete this work by late 2013

Evaluation ofthe Middle East and North Africa Land Data Assimilation System

The Middle East and North Africa (MENA) region is dominated by dry, warm deserts, areas of dense population, and inefficient use of fresh water resources. Due to the scarcity, high intensity, and short duration of rainfall in the MENA, the region is prone to hydro climatic extremes that are realized by devastating floods and times of drought. However, given its widespread water stress and the considerable demand for water, the MENA remains relatively poorly monitored. This is due in part to the shortage of meteorological observations and the lack of data sharing between nations. As a result, the accurate monitoring of the dynamics of the water cycle in the MENA is difficult. The Land Data Assimilation System for the MENA region (MENA LDAS) has been developed to provide regional, gridded fields of hydrological states and fluxes relevant for water resources assessments. As an extension of the Global Land Data Assimilation System (GLDAS), the MENA LDAS was designed to aid in the identification and evaluation of regional hydrological anomalies by synergistically combining the physically-based Catchment Land Surface Model (CLSM) with observations from several independent data products including soil-water storage variations from the Gravity Recovery and Climate Experiment (GRACE) and irrigation intensity derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). In this fashion, we estimate the mean and seasonal cycle of the water budget components across the MENA

How Universal is the Relationship Between Remotely Sensed Vegetation Indices and Crop Leaf Area Index? A Global Assessment

Leaf Area Index (LAI) is a key variable that bridges remote sensing observations to the quantification of agroecosystem processes. In this study, we assessed the universality of the relationships between crop LAI and remotely sensed Vegetation Indices (VIs). We first compiled a global dataset of 1459 in situ quality-controlled crop LAI measurements and collected Landsat satellite images to derive five different VIs including Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), two versions of the Enhanced Vegetation Index (EVI and EVI2), and Green Chlorophyll Index (CI(sub Green)). Based on this dataset, we developed global LAI-VI relationships for each crop type and VI using symbolic regression and Theil-Sen (TS) robust estimator. Results suggest that the global LAI-VI relationships are statistically significant, crop-specific, and mostly non-linear. These relationships explain more than half of the total variance in ground LAI observations (R2 greater than 0.5), and provide LAI estimates with RMSE below 1.2 m2/m2. Among the five VIs, EVI/EVI2 are the most effective, and the crop-specific LAI-EVI and LAI-EVI2 relationships constructed by TS, are robust when tested by three independent validation datasets of varied spatial scales. While the heterogeneity of agricultural landscapes leads to a diverse set of local LAI-VI relationships, the relationships provided here represent global universality on an average basis, allowing the generation of large-scale spatial-explicit LAI maps. This study contributes to the operationalization of large-area crop modeling and, by extension, has relevance to both fundamental and applied agroecosystem research

Building Climate Resilience in the Blue Nile/Abay Highlands: A Role for Earth System Sciences

The Blue Nile (Abay) Highlands of Ethiopia are characterized by significant interannual climate variability, complex topography and associated local climate contrasts, erosive rains and erodible soils, and intense land pressure due to an increasing population and an economy that is almost entirely dependent on smallholder, low-input agriculture. As a result, these highland zones are highly vulnerable to negative impacts of climate variability. As patterns of variability and precipitation intensity alter under anthropogenic climate change, there is concern that this vulnerability will increase, threatening economic development and food security in the region. In order to overcome these challenges and to enhance sustainable development in the context of climate change, it is necessary to establish climate resilient development strategies that are informed by best-available Earth System Science (ESS) information. This requirement is complicated by the fact that climate projections for the Abay Highlands contain significant and perhaps irreducible uncertainties. A critical challenge for ESS, then, is to generate and to communicate meaningful information for climate resilient development in the context of a highly uncertain climate forecast. Here we report on a framework for applying ESS to climate resilient development in the Abay Highlands, with a focus on the challenge of reducing land degradation