Irrigation Map Research Articles

Irrigation Mapping on Two Contrasted Climatic Contexts Using Sentinel-1 and Sentinel-2 Data

This study aims to propose an operational approach to map irrigated areas based on the synergy of Sentinel-1 (S1) and Sentinel-2 (S2) data. An application is proposed at two study sites in Europe—in Spain and in Italy—with two climatic contexts (semiarid and humid, respectively), with the objective of proving the essential role of multi-site training for a robust application of the proposed methodologies. Several classifiers are proposed to separate irrigated and rainfed areas. They are based on statistical variables from Sentinel-1 and Sentinel-2 time series data at the agricultural field scale, as well as on the contrasted behavior between the field scale and the 5 km surroundings. The support vector machine (SVM) classification approach was tested with different options to evaluate the robustness of the proposed methodologies. The optimal number of metrics found is five. These metrics illustrate the importance of optical/radar synergy and the consideration of multi-scale spatial information. The highest accuracy of the classifications, approximately equal to 85%, is based on training dataset with mixed reference fields from the two study sites. In addition, the accuracy is consistent at the two study sites. These results confirm the potential of the proposed approaches towards the most general use on sites with different climatic and agricultural contexts.

Estimation of spatial distribution of irrigated crop areas in Europe for large-scale modelling applications

Regional-to-global studies of hydrology, water availability and water use, and their interaction with agricultural production and food security require accurate information on the location and extent of irrigated croplands. In this study, we derive the European Irrigation Map for the year 2010 (EIM2010) - underpinned by the agricultural census conducted in the European Union in 2010. The map contains 14 irrigated crop classes as well as total irrigable and irrigated areas centred on the year 2010. The thematic resolution of the map is tailored specifically to European agriculture, and contains most relevant staple crops (e.g. maize, rice, potato, other cereals) as well as major permanent crops (e.g. irrigated olive orchards and vineyards). The map was created using a consistent methodology and data gathered across the European Union Member States and the UK, and therefore provides comparable and representative information across the continental domain. The input data used to construct the map consisted of farm-level records on irrigated areas and total crop areas, collected during the 2010 agricultural census and aggregated to 10 km x 10 km grid, as well as regional-level statistics (at NUTS2 level) on irrigated areas by crop type. A custom-developed disaggregation algorithm was used to distribute the regional-level statistics into the 10 km x 10 km grid. Evaluation of the map against independent reference geospatial data indicated satisfactory agreement for both large-scale spatial patterns as well as specific grid cell values for the main crop production regions. The EIM2010 is publicly available and provides valuable inputs for large scale modelling applications involving irrigated agriculture.

Tracking spatiotemporal dynamics of irrigated croplands in China from 2000 to 2019 through the synergy of remote sensing, statistics, and historical irrigation datasets

Irrigation is widely implemented in China to enhance grain production and ensure food security. Spatiotemporal information on irrigation is critically important, but the existing global and regional irrigation products have a coarse spatial resolution, low accuracy, and short temporal coverage, which is a knowledge gap to be filled. Generating explicit and accurate information on the spatial and temporal extent of irrigation is essential to underpin and facilitate water resource allocation and management, as well as to understand catchment hydrology, and evaluate irrigation infrastructure investment. We proposed a two-step strategy to map annual irrigated areas at 500 m resolution in China from 2000 to 2019. We first generated initial irrigation maps using MODIS data and statistical data (MIrAD-GI). Then we combined MIrAD-GI, the existing irrigation maps, and land use/cover products with irrigation information, into an improved series of annual irrigation maps (IrriMap_Syn) with constrained statistics. Afterwards, pixel-wise accuracy assessment for IrriMap_Syn was conducted in four representative regions in three years. The resultant irrigation dataset performed well with a mean overall accuracy of 0.89 and a kappa coefficient of 0.82. According to our annual irrigation maps, the total irrigated area in China increased by 24.8%, from 52.8 million ha in 2000–65.9 million ha in 2019, at a rate of 690,000 ha per year. Spatially, irrigated croplands increased substantially in North China, especially in Xinjiang and Heilongjiang. Land reclamation largely contributed to the increase in irrigation in North China. A growing population and improved irrigation infrastructure also promoted the increase in irrigated areas. As the first of its kind in the country, our spatially explicit maps of irrigated croplands advance our understanding of the spatiotemporal pattern of irrigation dynamics in China and are expected to contribute to sustainable water resource management and irrigation strategies in the face of climate change.

A Deep Learning Approach to Mapping Irrigation Using Landsat: IrrMapper U-Net

Accurate maps of irrigation are essential for understanding and managing water resources. We present a new method of mapping irrigation based on an ensemble of convolutional neural networks that use reflectance information from Landsat imagery to classify irrigated pixels. The methodology does not rely on extensive feature engineering and does not condition the classification with land use information from existing geospatial datasets. The ensemble does not need exhaustive hyperparameter tuning and the analysis pipeline is lightweight enough to be implemented on a personal computer. Furthermore, the proposed methodology provides an estimate of the uncertainty associated with classification. We evaluated our methodology and the resulting irrigation maps using a highly accurate novel spatially-explicit ground truth data set, using county-scale USDA surveys of irrigation extent, and using cadastral surveys. We demonstrate the accuracy of the method by mapping irrigation over the state of Montana from years 2000-2019. We found that our method outperforms other methods that use satellite remote sensing information in terms of overall accuracy and precision. We found that our method agrees better statewide with the USDA National Agricultural Statistics Survey estimates of irrigated area compared to other methods, and has far fewer errors of commission in rainfed agriculture areas. This methodology has the potential to be applied across the entire United States and for the complete Landsat record.

Intelligent IoT-multiagent precision irrigation approach for improving water use efficiency in irrigation systems at farm and district scales

The fourth industrial revolution in agriculture seeks the automation of traditional practices, using modern smart technologies. Advances in electronics, computation and the internet of things are integrated for improving field inputs management. The aim of this paper is to present the design and implementation of an intelligent IoT-multiagent precision irrigation approach for improving water use efficiency in irrigation systems. The study site was the large-scale irrigation and drainage district of Chicamocha and Firavitoba (Usochicamocha) located in Boyacá - Colombia, where water is distributed from the Chicamocha riverbed. In the proposed system, irrigation is supervised and controlled in each field by an intelligent irrigation agent that autonomously prescribes and applies water amounts with agronomical criteria. The methodology was applied with real (cyber-physical) and virtual (simulated) intelligent agents and was extended to eleven pump stations that supply water to 5911 fields. Using a MQTT protocol, hundreds of irrigation intelligent agents report water prescriptions and crop characteristics to a master agent in each pump station, who creates a regional irrigation map to manage georeferenced field information and performs negotiation of water resources between agents according to supply availability. Field maps and intelligent irrigation agents can be visualized using devices with internet access. Results demonstrated that irrigation amounts were correctly applied on the fields, thus improving the water use efficiency. This technology is a novel support to decision-making in water resources management applications at field and district scales.

Landsat-based Irrigation Dataset (LANID): 30 m resolution maps of irrigation distribution, frequency, and change for the US, 1997–2017

Abstract. Data on irrigation patterns and trends at field-level detail across broad extents are vital for assessing and managing limited water resources. Until recently, there has been a scarcity of comprehensive, consistent, and frequent irrigation maps for the US. Here we present the new Landsat-based Irrigation Dataset (LANID), which is comprised of 30 m resolution annual irrigation maps covering the conterminous US (CONUS) for the period of 1997–2017. The main dataset identifies the annual extent of irrigated croplands, pastureland, and hay for each year in the study period. Derivative maps include layers on maximum irrigated extent, irrigation frequency and trends, and identification of formerly irrigated areas and intermittently irrigated lands. Temporal analysis reveals that 38.5×106 ha of croplands and pasture–hay has been irrigated, among which the yearly active area ranged from ∼22.6 to 24.7×106 ha. The LANID products provide several improvements over other irrigation data including field-level details on irrigation change and frequency, an annual time step, and a collection of ∼10 000 visually interpreted ground reference locations for the eastern US where such data have been lacking. Our maps demonstrated overall accuracy above 90 % across all years and regions, including in the more humid and challenging-to-map eastern US, marking a significant advancement over other products, whose accuracies ranged from 50 % to 80 %. In terms of change detection, our maps yield per-pixel transition accuracy of 81 % and show good agreement with US Department of Agriculture reports at both county and state levels. The described annual maps, derivative layers, and ground reference data provide users with unique opportunities to study local to nationwide trends, driving forces, and consequences of irrigation and encourage the further development and assessment of new approaches for improved mapping of irrigation, especially in challenging areas like the eastern US. The annual LANID maps, derivative products, and ground reference data are available through https://doi.org/10.5281/zenodo.5548555 (Xie and Lark, 2021a).

A Review of Irrigation Information Retrievals from Space and Their Utility for Users

Irrigation represents one of the most impactful human interventions in the terrestrial water cycle. Knowing the distribution and extent of irrigated areas as well as the amount of water used for irrigation plays a central role in modeling irrigation water requirements and quantifying the impact of irrigation on regional climate, river discharge, and groundwater depletion. Obtaining high-quality global information about irrigation is challenging, especially in terms of quantification of the water actually used for irrigation. Here, we review existing Earth observation datasets, models, and algorithms used for irrigation mapping and quantification from the field to the global scale. The current observation capacities are confronted with the results of a survey on user requirements on satellite-observed irrigation for agricultural water resources’ management. Based on this information, we identify current shortcomings of irrigation monitoring capabilities from space and phrase guidelines for potential future satellite missions and observation strategies.

Contemporary procedure characteristics and outcomes of accessory atrioventricular pathway ablations in an integrated community-based health care system using a tiered approach

BackgroundSince the early descriptions of large series of accessory atrioventricular pathway ablations in adults and adolescents over 20 years ago, there have been limited published reports based on more recent experiences of large referral centers. We aimed to characterize accessory pathway distribution and features in a large community-based population that influence ablation outcomes using a tiered approach to ablation.MethodsRetrospective analysis of 289 patients (age 14–81) who underwent accessory ablation from 2015–2019 was performed. Pathways were categorized into anteroseptal, left freewall, posteroseptal, and right freewall locations. We analyzed patient and pathway features to identify factors associated with prolonged procedure time parameters.ResultsInitial ablation success rate was 94.7% with long-term success rate of 93.4% and median follow-up of 931 days. Accessory pathways were in left freewall (61.6%), posteroseptal (24.6%), right freewall (9.6%), and anteroseptal (4.3%) locations. Procedure outcome was dependent on pathway location. Acute success was highest for left freewall pathways (97.1%) with lowest case times (144 ± 68 min) and fluoroscopy times (15 ± 19 min). Longest procedure time parameters were seen with anteroseptal, left anterolateral, epicardial-coronary sinus, and right anterolateral pathway ablations.ConclusionsIn this community-based adult and adolescent population, majority of the accessory pathways are in the left freewall and posteroseptal region and tend to be more easily ablated. A tiered approach with initial use of standard ablation equipment before the deployment of more advance tools, such as irrigated tips and 3D mapping, is cost effective without sacrificing overall efficacy.

A two-step mapping of irrigated corn with multi-temporal MODIS and Landsat analysis ready data

Timely and reliable information about irrigated croplands is important for crop water stress analysis and studies of water, energy, and food security. This study mapped irrigated and non-irrigated corn at 30 m resolution for the state of Nebraska using a two-step multi-temporal image classification of MODIS and Landsat Analysis Ready Data (ARD). Starting from the drought year of 2012, when there was a high contrast between irrigated and non-irrigated fields, we first conducted image classification using the 250 m MODIS multi-temporal NDVI data. Training pixels were automatically derived, based on counties with predominant irrigated and non-irrigated cornfields. The MODIS-derived irrigated vs. non-irrigated map was further spatially filtered to generate training data covering the entire Nebraska to support automated Landsat ARD classification, footprint-by-footprint. Three classification algorithms of multi-layer perceptron (MLP) neural network, Random Forest (RF), and Support Vector Machine (SVM) were implemented to classify all available Landsat ARD images within the growing season (i.e. May to November). Given the issues of scanline corrector (SLC) error and cloud contamination, the provisional Landsat-based classifications were finally gap-filled to generate a seamless state-wise irrigation map guided by decreasing cross-validation accuracy. Pixel-wise accuracy assessments showed similar overall accuracies of 89.6%, 89.3%, and 90.0% for MLP, RF, and SVM, respectively. They are 3-6% higher than a commonly used gap-filing procedure based on valid (cloud free) pixel count for growing season images. The estimated areas of irrigated corn from Landsat-based mapping were consistent with the 2012 USDA county level census data (R2 = 0.97 and RMSE = 37.70 km2). Using the 2012 Landsat-derived irrigation map and the USDA’s annual Cropland Data Layer as inputs, we further developed training data for annual irrigation mapping between 2013 and 2018. Pixel-wise assessment of the 2016 map showed reasonable overall accuracies of 78.4–79.6% for three classification algorithms. The annual maps yielded R2 of 0.94–0.98 and RMSE values of 37.70–57.62 km2 for various mapping years compared with USDA county statistics. These results suggest that our proposed two-step analytical method has a high potential for automated annual irrigation mapping at 30 m spatial resolution (especially for the arid and semi-arid western U.S.), providing clear field boundaries and irrigation frequency information that are vitally important for accurate agricultural water use analysis.

WITHDRAWN: Observation and monitoring in vegetation cover in Al - Qadisiyah Governorate, Iraq based on remote sensing and GIS

For the purpose of monitoring and controlling the change of plant areas (all types of plants, whether natural or planted) in Al Qadisiyah Governorate, by using satellite data (5) and (8) were used with exception (6) because it is thermal. (30 m) for multi-spectral beams. The research found the extent of changes in vegetation cover in the study area. The values were limited between (+1) and (−1) for the treatment of space images After applying the vegetation cover for the study area. This study investigate changes in the vegetation cover area in Al Qadisiyah Governorate for the years 2007 and 2017. An ArcGIS V.10.4 software was used in this study. 1- Georeferencing and digitizing maps, which are covered the study area at a different scale such as seven topographical maps at scale of 1:100000, four geological maps at scale of 1:250000, geomorphological map at scale 1:1000000, soil map at scale 1:1000000, irrigation map of Al-Qadisiyah governorate at scale 1:130000. 2- Image processing such as Digital Elevation Models (DEMs), which is adopted from Shuttle Radar Topography Mission (SRTM) V.3 with spatial resolution of 30 m. The DEMs was processed to extract slope map, topographic contour line map and gradients elevation map with color-coded of DEMs. 3- Projected water symbols, which are conducted from fieldwork as points with WGS84 datum and UTM projection within zone 38 N and. 4- Layout all the maps.

Mapping Management Zones Based on Soil Apparent Electrical Conductivity and Remote Sensing for Implementation of Variable Rate Irrigation—Case Study of Corn under a Center Pivot

Climate change, especially the trend towards global warming, will significantly affect the global hydrological cycle, leading to a general reduction of the water available for agriculture. In this scenario, it is essential that research should focus on the development of ‘water saving’ techniques and technologies. This work summarizes the methodology followed in a project for large scale implementation of variable rate irrigation (VRI) systems using center pivots in corn crop. This is based on technologies for monitoring (i) soil electrical conductivity (ECa) and altimetry, (ii) soil moisture content, (iii) vegetation indices (Normalized Difference Vegetation Index, NDVI) obtained from satellite images, and automatic pivot travel speed control technologies. ECa maps were the basis for the definition of first homogeneous management zones (HMZ) in an experimental corn field of 28 ha. NDVI time-series were used to establish the subsequent HMZ and the respective dynamic prescription irrigation maps. The main result of this study was the reduction of spatial yield variability with the VRI management in 2017 compared to the conventional irrigation management. This study demonstrates how a relatively simple approach could be designed and implemented on a large scale, which represents an important and sustainable contribution to the resolution of practical farmer issues.

DETERMINATION OF NITROGEN AND PHOSPHORUS SATURATION OF SOILS AT THE BOTTOM OF COTTON, SUBJECT TO IRRIGATION EROSION

The article presents the results of a study on the result of irrigation erosion, within one irrigation map, soils are divided into non, eroded, medium, strongly eroded and soil accumulations. To obtain an evenly large yield of raw cotton, the annual NPK rates on the slope should be differentiated.

P960Non-inducible ventricular arrhythmia soon after ventricular tachycardia ablation predicts long-term recurrence in patients with reduced ejection fraction

Abstract Introduction Radiofrequency (RF) ablation for ventricular arrhythmia in patients with reduced left ventricular ejection fraction (LVEF) is recognized to be one of the important strategies for suppressing the fatal arrhythmia events. Although, RF ablation for those patients has been performed for decades, the optimal endpoint remains unclear. Hypothesis We assessed the hypothesis that non-inducibility of any ventricular tachycardia (VT) or ventricular fibrillation (VF) immediately after VT ablation predicts long-term recurrence free rate of ventricular arrhythmia in patients who were diagnosed sustained VT with reduced LVEF. Methods From January 2014 to August 2019, we conducted a single center retrospective analysis for 127 consecutive patients with right or left ventricular arrhythmia who performed the first time RF ablation. Exclusion criteria were LVEF >50% and RF ablations for premature ventricular contraction or for non-sustained VT. Then 26 patients (age 69 ± 7, male 92%) were enrolled. All of the ablation procedures were performed using irrigated RF catheters and 3D mapping systems. We defined non-inducible group as the patients without monomorphic VT (clinical monomorphic VT or non-clinical monomorphic VT with any cycle length), polymorphic VT and VF by electrophysiological study (EPS) immediately after the ablation. The primary endpoint of this study was a recurrence of any sustained VT and VF during the follow up period. Results All of 26 patients were followed for a mean of 30.9 ± 22.3months.Of those patients, 7 patients were non-inducible group and 19 patients were inducible group. Age, sex, body mass index, coronary risk factors, LVEF (non-inducible:42 ± 5% vs inducible:35 ± 10%, p = 0.12), renal function and the etiology of LV dysfunction did not differ between patients with non-inducible group and inducible group (all non-significant). Catheter ablation procedural characteristics including activation mapping (non-inducible:29% vs inducible:36%, p = 1.00), entrainment mapping (14% vs 42%, p = 0.19), substrate mapping (86% vs 95%, p = 0.47), pace-mapping (86% vs 68%, p = 0.63), RF time (21 ± 13vs 18 ± 21min, p = 0.70), number of RF applications (27 ± 13vs 27 ± 32, p = 0.89), fluoroscopy time (77 ± 50vs 87 ± 42min, p = 0.59) and procedure time (309 ± 87vs 282 ± 61min, p = 0.37) did not differ between two groups. Medications before and after the VT ablation did not differ between two groups. The recurrence of any sustained VT and VF significantly lower in patients with non-inducible group than those with inducible group (Figure). Conclusion This study demonstrated that, in patients with reduced LVEF presenting sustained VT who performed RF ablation, non-inducibility of any VT and VF immediately after RF ablation predicts long-term decreased risk of recurrence of any sustained VT and VF. Not only clinical but also non-clinical VT and VF may be targeted as well at the first time VT ablation for those patients. Abstract Figure

Mapping three decades of annual irrigation across the US High Plains Aquifer using Landsat and Google Earth Engine

Understanding how irrigated areas change over time is vital to effectively manage limited agricultural water resources, but long-term, high-resolution, and spatially explicit datasets are rare. The High Plains Aquifer (HPA) in the central United States is one of the largest and most stressed aquifer systems in the world. It supports a $20 billion economy, but groundwater use is unsustainable over much of the aquifer. Emerging cloud computing tools like Google Earth Engine (GEE) make it possible to use the full Landsat record to monitor regional systems like the HPA with high spatial and temporal resolution over multiple decades. However, challenges remain to develop irrigation classification methods that are robust to a wide range of climate conditions and crop types, evolving management, and missing data. Here, we addressed these challenges to produce an annual, moderately high resolution (30 m) irrigation map time series from 1984 to 2017 over the aquifer. Leveraging GEE's extensive data catalog, we combined Landsat imagery, environmental covariables, and a large heterogeneous ground truth dataset to create a single random forest classifier applied annually to the entire region. Following classification, we applied the Bayesian Updating of Land-Cover (BULC) algorithm to fill imagery gaps and reduce commission errors in the provisional irrigation time series. Novel neighborhood greenness indices contributed to an overall 91.4% map accuracy across years; county statistics (r2 = 0.86) were similarly well-matched. Trend analysis of irrigated area through time identified regions of stable, expanding, and declining irrigated area. Given declining aquifer storage, we estimate that up to 24% of currently irrigated area may be lost this century. To date, the map dataset is the longest, highest resolution large-scale record of where and when irrigation occurs. It is freely available for stakeholders, managers, and researchers to inform policies and management decisions, as well as for use in hydrology, agronomy, and climate models.

Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain

Mapping irrigated plots is essential for better water resource management. Today, the free and open access Sentinel-1 (S1) and Sentinel-2 (S2) data with high revisit time offers a powerful tool for irrigation mapping at plot scale. Up to date, few studies have used S1 and S2 data to provide approaches for mapping irrigated plots. This study proposes a method to map irrigated plots using S1 SAR (synthetic aperture radar) time series. First, a dense temporal series of S1 backscattering coefficients were obtained at plot scale in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations over a study site located in Catalonia, Spain. In order to remove the ambiguity between rainfall and irrigation events, the S1 signal obtained at plot scale was used conjointly to S1 signal obtained at a grid scale (10 km × 10 km). Later, two mathematical transformations, including the principal component analysis (PCA) and the wavelet transformation (WT), were applied to the several SAR temporal series obtained in both VV and VH polarization. Irrigated areas were then classified using the principal component (PC) dimensions and the WT coefficients in two different random forest (RF) classifiers. Another classification approach using one dimensional convolutional neural network (CNN) was also performed on the obtained S1 temporal series. The results derived from the RF classifiers with S1 data show high overall accuracy using the PC values (90.7%) and the WT coefficients (89.1%). By applying the CNN approach on SAR data, a significant overall accuracy of 94.1% was obtained. The potential of optical images to map irrigated areas by the mean of a normalized differential vegetation index (NDVI) temporal series was also tested in this study in both the RF and the CNN approaches. The overall accuracy obtained using the NDVI in RF classifier reached 89.5% while that in the CNN reached 91.6%. The combined use of optical and radar data slightly enhanced the classification in the RF classifier but did not significantly change the accuracy obtained in the CNN approach using S1 data.

Mapping irrigated cropland extent across the conterminous United States at 30 m resolution using a semi-automatic training approach on Google Earth Engine

Accurate and timely information on the distribution of irrigated croplands is crucial to research on agriculture, water availability, land use, and climate change. While agricultural land use has been well characterized, less attention has been paid specifically to croplands that are irrigated, in part due to the difficulty in mapping and distinguishing irrigation in satellite imagery. In this study, we developed a semi-automatic training approach to rapidly map irrigated croplands across the conterminous United States (CONUS) at 30 m resolution using Google Earth Engine. To resolve the issue of lacking nationwide training data, we generated two intermediate irrigation maps by segmenting Landsat-derived annual maximum greenness and enhanced vegetation index using county-level thresholds calibrated from an existing coarse resolution irrigation map. The resulting intermediate maps were then spatially filtered to provide a training data pool for most areas except for the upper midwestern states where we visually collected samples. We then used random samples extracted from the training pool along with remote sensing-derived features and climate variables to train ecoregion-stratified random forest classifiers for pixel-level classification. For ecoregions with a large training pool, the procedure of sample extraction, classifier training, and classification was conducted 10 times to obtain stable classification results. The resulting 2012 Landsat-based irrigation dataset (LANID) identified 23.3 million hectares of irrigated croplands in CONUS. A quantitative assessment of LANID showed superior accuracy to currently available maps, with a mean Kappa value of 0.88 (0.75–0.99), overall accuracy of 94% (87.5–99%), and producer’s and user’s accuracy of the irrigation class of 97.3% and 90.5%, respectively, at the aquifer level. Evaluation of feature importance indicated that Landsat-derived features played the primary role in classification in relatively arid regions while climate variables were important in the more humid eastern states. This methodology has the potential to produce annual irrigation maps for CONUS and provide insights into the field-level spatial and temporal aspects of irrigation.

Detecting global irrigated areas by using satellite and reanalysis products

Despite the importance of irrigation in meeting the world's food demand and as an essential human modification to water and energy cycles, the reliable extent and distribution of the global irrigated areas remain undefined. In this study, an intuitive method is proposed, based on the aftereffects of irrigation, to detect global irrigated areas by combining satellite and reanalysis datasets. The proposed methodology assumes that irrigation is an unmodeled land surface process, while satellite observations can effectively detect irrigation signals in near real-time. The spatial extents of irrigation were derived by calculating the difference between the remotely sensed and reanalysis datasets. To detect the irrigated areas, three irrigation-dependent variables, soil moisture (SM), land surface temperature (LST), and surface albedo (AL), were used. In the absence of reliable ground truths, the proposed irrigation map was compared to the commonly used global irrigation maps, namely Global Map of Irrigated areas, Global Irrigated Area Map, and recently developed Global Irrigated Areas by Meier et al. (2018). Individual detection by SM, LST, and AL has discrepancies in detecting irrigation signals in highly irrigated, urbanized, and semi-arid regions. However, by combining the individual detection maps, the proposed method showed reasonable agreement with the reference irrigated maps overlapping with approximately 70% of the irrigated areas. We believe that the proposed method, as stand-alone or in combination with the existing irrigation maps, will benefit the studies regarding water and energy balance closure in near-real time for large-scale land surface models by minimizing the uncertainties in model parameterization.

Addressing Challenges for Mapping Irrigated Fields in Subhumid Temperate Regions by Integrating Remote Sensing and Hydroclimatic Data

High-resolution mapping of irrigated fields is needed to better estimate water and nutrient fluxes in the landscape, food production, and local to regional climate. However, this remains a challenge in humid to subhumid regions, where irrigation has been expanding into what was largely rainfed agriculture due to trends in climate, crop prices, technologies and practices. One such region is southwestern Michigan, USA, where groundwater is the main source of irrigation water for row crops (primarily corn and soybeans). Remote sensing of irrigated areas can be difficult in these regions as rainfed areas have similar characteristics. We present methods to address this challenge and enhance the contrast between neighboring rainfed and irrigated areas, including weather-sensitive scene selection, applying recently developed composite indices and calculating spatial anomalies. We create annual, 30m-resolution maps of irrigated corn and soybeans for southwestern Michigan from 2001 to 2016 using a machine learning method (random forest). The irrigation maps reasonably capture the spatial and temporal pattern of irrigation, with accuracies that exceed available products. Analysis of the irrigation maps showed that the irrigated area in southwestern Michigan tripled in the last 16 years. We also discuss the remaining challenges for irrigation mapping in humid to subhumid areas.

Soil Moisture and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data

This paper presents a technique for the mapping of soil moisture and irrigation, at the scale of agricultural fields, based on the synergistic interpretation of multi-temporal optical and Synthetic Aperture Radar (SAR) data (Sentinel-2 and Sentinel-1). The Kairouan plain, a semi-arid region in central Tunisia (North Africa), was selected as a test area for this study. Firstly, an algorithm for the direct inversion of the Water Cloud Model (WCM) was developed for the spatialization of the soil water content between 2015 and 2017. The soil moisture retrieved from these observations was first validated using ground measurements, recorded over 20 reference fields of cereal crops. A second method, based on the use of neural networks, was also used to confirm the initial validation. The results reported here show that the soil moisture products retrieved from remotely sensed data are accurate, with a Root Mean Square Error (RMSE) of less than 5% between the two moisture products. In addition, the analysis of soil moisture and Normalized Difference Vegetation Index (NDVI) products over cultivated fields, as a function of time, led to the classification of irrigated and rainfed areas on the Kairouan plain, and to the production of irrigation maps at the scale of individual fields. This classification is based on a decision tree approach, using a combination of various statistical indices of soil moisture and NDVI time series. The resulting irrigation maps were validated using reference fields within the study site. The best results were obtained with classifications based on soil moisture indices only, with an accuracy of 77%.

An Unsupervised Classification Algorithm for Multi-Temporal Irrigated Area Mapping in Central Asia

Sound water resources planning and management requires adequate data with sufficient spatial and temporal resolution. This is especially true in the context of irrigated agriculture, which is one of the main consumptive users of the world’s freshwater resources. Existing remote sensing methods for the management of irrigated agricultural systems are often based on empirical cropland data that are difficult to obtain, and that put into question the transferability of mapping algorithms in space and time. Here we implement an automatic irrigation mapping procedure in Google Earth Engine that uses surface reflectance satellite imagery from different sensors. The method is based on unsupervised training of a pixel-by-pixel classification algorithm within image regions identified through unsupervised object-based segmentation, followed by multi-temporal image analysis to distinguish productive irrigated fields from non-productive and non-irrigated areas. Ground-based data are not required. The final output of the mapping algorithm are monthly and annual irrigation maps (30 m resolution). The novel method is applied to the Central Asian Chu and Talas River Basins that are shared between upstream Kyrgyzstan and downstream Kazakhstan. We calculate the development of irrigated areas from 2000 to 2017 and assess the classification results in terms of robustness and accuracy. Based on seven available validation scenes (in total more than 2.5 million pixels) the classification accuracy is 77–96%. We show that on the Kyrgyz side of the Talas basin, the identified increasing trends over the years are highly significant (23% area increase between 2000 and 2017). In the Kazakh parts of the basins the irrigated acreages are relatively stable over time, but the average irrigation frequency within Soviet-era irrigation perimeters is very low, which points to a poor physical condition of the irrigation infrastructure and inadequate water supply.