Distinguishing Rain-fed and Irrigated Crops in Hamadan Province Using Spectral Indices of Satellite Images

Remote sensing methods for mapping farms and crops have been widely used in the last three decades. This method is applied to identify irrigated areas around the world (Alipour et al., 2014), although most of these studies are in areas with semi-arid climates and low rainfall or lack of rainfall which has a significant effect on the spectral characteristics of plants. In this study, Landsat 8 and MODIS satellite images were used to identify and separate two irrigated and rain-fed wheat farms in Hamadan province. Two algorithms of support vector machine (SVM) and minimum distance (MD) were used simultaneously to classify irrigated and rain-fed farms. In the next step, the area under cultivation of rain-fed and irrigated wheat was predicted in the whole cultivated area of Hamadan province. Finally, the cultivation area of rain-fed and irrigated crops was calculated in the province using Sentinel 3 satellite images based on the random forest algorithm in 2016.

The study area is Hamedan province, which is located between 59◦ 33′ and 49◦ 35′ north latitude and also from 34◦ 47′ to 34◦ 49′ east longitude of the Greenwich meridian. A 50-hectare rain-fed wheat farm in Amzajerd was used as a sample to extract the properties of rain-fed wheat. Also, irrigated indices were extracted from a 100-hectare irrigated wheat farm located in Kaboudrahang. Satellite images were applied to separate irrigated and rain-fed wheat in Hamadan province. NDVI, EVI and NDWI indices were extracted from 16-day images of Landsat, MODIS, and Sentinel 3 sensors in the five-year period (2015-2019). Google Earth Engine (GEE) system was the environment for performing image processing calculations and extracting indices and maps.

The NDVI and EVI of irrigated and rain-fed wheat farms were calculated in 2015-2019. A small peak was observed in the rain-fed and irrigated NDVI trend in November due to the early germination of wheat leaves in winter, and the larger peak in May and June showed the maximum greenness of irrigated and rain-fed wheat, respectively. The ascending or descending trend of NDVI / EVI had no constant slope. This can be due to changes in meteorological parameters, which sometimes cause a sudden increase or decrease in the values of these indices. Despite the non-linearity of the NDVI / EVI trend over time, the maximum greenness was recorded just a month before the wheat harvest, which was seen in the third decade of May to the first decade of June. One of the cases is the sharp drop of NDVI / EVI after its final peak, which was definitely due to yellowing wheat and harvesting. Since the distinction between rain-fed and irrigated crops was difficult only based on NDVI, NDWI was also used to determine the water content of wheat so that irrigated wheat could be identified. However, the difference between rain-fed and irrigated wheat in terms of NDWI spectral density was insignificant; the maximum and minimum occurrence times of NDWI and NDVI of rain-fed and irrigated wheat were chosen for their separation. In order to map the cultivation area, in addition to the MODIS sensor, Sentinel 3 was used due to its ability to detect chlorophyll accurately. Due to the fact that the imaging of the Sentinel 3 satellite started since 2016, the map of rain-fed and irrigated cultivation as well as the cultivation area and their separation was done based on the random forest algorithm in 2016.

The results of this study showed that the appropriate method for distinguishing between rain-fed and irrigated wheat is the simultaneous use of several indices. Also, the greatest difference is in the maximum greenness, which happened almost one month before harvest. MD and SVM classification algorithms could distinguish irrigated and rain-fed wheat from other crops with 90% and 80% accuracy, respectively. Distinguished maps of irrigated and rain-fed crops based on the random forest algorithm were obtained using Sentinel 3 satellite imagery which can show the fertility of agricultural lands in the province.