Irrigation plays a key role in increasing crop production [1
], especially in arid and semi-arid regions. It has been shown that the amount of water used for agricultural irrigation accounts for 84% of total human water consumption [2
]. Approximately 40% of the world's harvest is produced on irrigated arable land, which accounts for about 20% of the total arable land area [3
], 70% of which is located in Asia [4
]. Crop yields are usually higher when crops are fully irrigated as compared to non-irrigated crops that usually suffer water deficiencies [5
]. In China, irrigated land occupies more than 40% of total farmland, but produces 80% of the nation’s food [3
]. More than two-thirds of all water use in China is from the agriculture sector. It is a widespread concern that China will face serious water shortages as its economy booms and urbanization increases. Realistic estimations of irrigation area are important for evaluating regional water and carbon cycles and ensuring food security [6
Currently, three major methods are used to classify irrigated and rainfed areas. (1) The water resources inventory, developed by the Food and Agriculture Organization of the United Nations (FAO), has been used to map the worldwide distribution of irrigated areas using statistical data [11
]. Generally, inventory data cannot accurately and timely reflect the spatial distribution of irrigated and rainfed areas. Statistical data are usually based on county/regional information (or even coarser scales) and span different time intervals. Recently, the FAO released a 2010 land cover map of Afghanistan, derived from the Satellite Pour l’Observation de la Terre (SPOT), Landsat imagery and aerial photographs [13
]; (2) Unsupervised classification has been widely used for the classification of irrigated and rainfed areas [14
]. The U.S. Geological Survey (USGS) and International Water Management Institute (IWMI) classified and mapped global irrigated and rainfed areas in the U.S. Geological Survey Global Land Cover Characterization (GLCC) and Global Irrigated Area Map (GIMA), respectively [16
]. This method can be easily used when local information is sparse; however, it is hard to control the number of classes. In addition, the calibration and combination of each cover type after unsupervised classification is strongly affected by subjective factors; (3) Supervised classification was used by Ozdogan and Gutman (2008) [19
] to map irrigated and rainfed areas in the U.S. with a decision tree method based on eight-day synthesis Moderate Resolution Imaging Spectrometer (MODIS) Greenness Index data (500 × 500 m resolution). Kamthonkiat et al.
] classified irrigated and rainfed rice in Thailand using 10-day synthesis SPOT NDVI data at a 1 × 1 km resolution, 10-day synthesis precipitation data and ancillary data. The advantage of the supervised classification method is that training samples can be controlled; therefore, unnecessary classes can be avoided, though classification accuracy is usually significantly influenced by training samples.
Since the 1980s, satellite remote sensing has been widely used for the classification of irrigated areas because it can provide a near real-time capability to dynamically monitor land surfaces on a regional scale. However, improvements in classification methods for the identification of irrigated areas are still necessary [21
]. Currently, the most commonly-used satellite data are MODIS products, with a mean spatial resolution of 250 × 250 m. Such data are generally sufficient to characterize large-scale spatial patterns, but may bring large uncertainties in arid and semi-arid regions, where irrigation is typically practiced in small and scattered areas. Pervez et al.
] used the same growth period to calculate the time-integrated NDVI (TIN) for irrigated and rainfed wheat. However, there are distinct differences between irrigated and rainfed wheat in growth processes, the length of the growing season and the periods of peak NDVI. Moreover, the NDVI threshold used in the supervised classification method usually has to be modified for local conditions, which may reduce the accuracy of the classification [5
To overcome the aforementioned disadvantages in mapping irrigated and rainfed areas, we propose an improved classification method based on the following three aspects. First, we used data from two of China’s small environmental satellites, the HJ-1A/B (HuanJing(HJ) means environment in Chinese) satellites, launched on 6 September 2008. A spatial resolution of 30 × 30 m and a two-day repeat cycle make them suitable for dynamic monitoring of irrigated and rainfed areas on a regional scale [23
]. Second, the support vector machine (SVM) algorithm was used as a classification method to avoid subjectivity in the definition of NDVI thresholds. The SVM is a pattern recognition method developed on the basis of statistical learning theory with high flexibility, global optimization, high efficiency and robustness [24
]. It has been widely used in the remote sensing classification [27
]. Third, considering the differences between irrigated and rainfed wheat, peak NDVI and TIN were selected as feature vectors for classification. During this process, sowing and maturity dates derived from NDVI thresholds were set as the start and end points of the growing period for TIN calculations.
In this study, we mapped irrigated and rainfed land in the semi-arid hilly areas of Shanxi Province, China. Due to the high fragmentation of arable land, smallholder management, long growth period of winter wheat and dispersed irrigation times, it is difficult to estimate irrigated areas in this region. Until now, there are no reliable maps of irrigated winter wheat in Shanxi Province. Our major research objectives were to: (1) develop a novel classification method for irrigated and rainfed areas based on high resolution satellite data, the SVM algorithm, and site-level observations; (2) map irrigated and rainfed winter wheat in the south central part of Shanxi Province; and (3) quantify the spatial patterns of sowing and maturation dates of winter wheat under different water supply conditions.
2. Study Area
Shanxi Province, located on the central Loess Plateau of Northern China, is characterized by typical loess hills and gully topography (Figure 1
). The surface area of Shanxi Province is 156,700 km2
, and approximately 80.1% of the land area is covered with mountains and hills. As the largest coal-producing province, Shanxi Province accounts for one fourth of the total coal production in China [30
]. Groundwater is exploited for food production, as well as for coal mining. The climate in most areas of Shanxi Province is semi-arid and is under the influence of the continental monsoon, with obvious zonal and vertical variations. The annual average temperature varies between −1 °C and 14 °C, decreasing from south to north and from plain to mountain areas. The annual average precipitation varies from 400 mm in the northwest to 650 mm in the southeast. Approximately 70% to 80% of the annual precipitation occurs during the monsoon season from June to September.
In the study area, crop cultivation is vulnerable to drought due to little precipitation during critical growth stages. Smallholders comprise a large force in Shanxi’s agricultural sector, and the most commonly planted crops include wheat, corn, millet, beans and tubers. Wheat is the most widely-grown staple crop throughout south central Shanxi; it is usually sown during mid-September to late October and harvested from mid-May to early July. The wheat-planting area in south central Shanxi Province was about 710,100 ha during 2010 to 2011. In general, irrigated areas are distributed throughout south central Shanxi. Irrigation activities mainly occur before dormancy, greening, jointing and grain filling, and irrigation is largely determined by the availability of surface and groundwater.
Irrigated areas can be divided into the area equipped for irrigation and the actual irrigated area. The irrigated area is typically smaller than the area equipped for irrigation due to water availability, climatic conditions, economic factors, labor availability, land management decisions, etc. In this study, we focus on the actual irrigated area, which reflects the real irrigation conditions.
5.1. Validation of SVM Classification Model
Water availability is the main limiting factor for crop growth in arid and semi-arid regions. There are large discrepancies between irrigated and rainfed wheat in Shanxi Province. Compared to irrigated wheat, rainfed wheat matures earlier and experiences inferior growth conditions. The peak NDVI and TIN values extracted from remote sensing data can realistically reflect these discrepancies. Therefore, these two feature vectors were used to establish an SVM classification model for irrigated and rainfed wheat. Quality and quantitative evaluations were conducted to validate the SVM classification model. The results showed that our proposed peak NDVI and TIN-based method has obvious advantages and obtains higher classification accuracy as compared to conventional NDVI time series-based methods.
5.2. Spatial Distribution and Variation Patterns of Wheat Sowing and Maturity Dates
The growth period and crop yield are strongly influenced by water deficit. Rainfed wheat may suffer severe water stress and exhibit significant differences compared to irrigated wheat. During the growing season, an increase in air temperature may result in a shorter growth period and earlier maturity date under deficient water supply, as shown in rainfed wheat in arid and semi-arid areas in this study. The growth and crop yield of rainfed wheat are severely affected by water deficit, e.g., during the filling stage of winter wheat when photosynthetic products in roots, stems and other organs are transported to grains.
This study quantified spatial variations and rates changes of the wheat growth period under different water supply conditions based on an enhanced classification method. The results show that the sowing dates of rainfed and irrigated wheat exhibit similar spatial patterns, and both are significantly related to elevation. Irrigated wheat varies faster than rainfed wheat. The maturity dates of rainfed and irrigated wheat are significantly postponed when elevation increases; however, they exhibit distinct spatial patterns.
5.3. Uncertainties and Future Needs
Uncertainties are inherent for most satellite data, including the HJ-1A/B satellite. Errors may exist when translating satellite data into sowing and maturity dates. In this study, a 30 × 30 m pixel was considered as 100% irrigated or rainfed, and thus, fragmented small fields may not have been well resolved. The above two aspects may have induced uncertainty in our estimated irrigated and rainfed surface areas. Typically, satellite data with higher spatial resolution perform better in mapping irrigated and rainfed areas for regions with small and scattered crop fields, but such data may be limited by a long satellite revisit time. Satellite data, such as MODIS, have a high temporal resolution and provide broad coverage area. Therefore, data assimilation is required to take advantage of different satellites and obtain high spatiotemporal resolution datasets for irrigated area mapping. Heterogeneous and small patchy crop areas are another challenge for classification. Some methods (e.g., mixed pixel decomposition) are needed to estimate the fraction of irrigated and rainfed areas in one grid cell, especially for regions with scattered farmland and a variable topographic surface.
A simple, but effective method based on an SVM algorithm was developed to map irrigated and rainfed wheat areas in a semi-arid region of China. This method avoids repeated tuning of thresholds in supervised classification. The overall accuracy of the Google-Earth testing samples is 96.0%, indicating that the SVM classification model is robust. The estimated ratio of irrigated-to-rainfed wheat area in this study is close to that estimated by the agricultural sector of Shanxi Province. The classification of irrigated and rainfed areas for the survey points also agrees with the field level photographs. In addition, maps of maturity dates and sowing dates were generated. It was shown that irrigated and rainfed wheat exhibit distinct maturity dates, although the spatial patterns of sowing dates are similar.
Accurate training samples, which were obtained from ground surveys in this study, are essential for the presented method. The proposed method can be applied in other arid and semi-arid regions with the availability of accurate training samples and multi-temporal data at high spatial and temporal resolutions similar to HJ-1 A/B (or higher). The HJ-1 A/B has an advantage over Landsat in temporal resolution, although they have the same spatial resolution in the red and near-infrared band. Different temporal resolution may influence the classification result of irrigated and rainfed wheat area, and the comparison of HJ-1 A/B time series with Landsat or Sentinel time series for classifications in partially-irrigated areas is a potential field area of future research. In addition, the performance of the proposed method in areas with more than one major crop type needs to be evaluated in the future.