Abstract
Crop identification in large irrigation districts is important for crop yield estimation, hydrological simulation, and agricultural water management. Remote sensing provides an opportunity to visualize crops in the regional scale. However, the use of coarse resolution remote sensing images for crop identification usually causes great errors due to the presence of mixed pixels in regions with complex planting structure of crops. Therefore, it is preferable to use remote sensing data with high spatial and temporal resolutions in crop identification. This study aimed to map multi-year distributions of major crops (maize and sunflower) in Hetao Irrigation District, the third largest irrigation district in China, using HJ-1A/1B CCD images with high spatial and temporal resolutions. The Normalized Difference Vegetation Index (NDVI) series obtained from HJ-1A/1B CCD images was fitted with an asymmetric logistic curve to find the NDVI characteristics and phenological metrics for both maize and sunflower. Nine combinations of NDVI characteristics and phenological metrics were compared to obtain the optimal classifier to map maize and sunflower from 2009 to 2015. Results showed that the classification ellipse with the NDVI characteristic of the left inflection point in the NDVI curve and the phenological metric from the left inflection point to the peak point normalized, with mean values of corresponding grassland indexes achieving the minimum mean relative error of 10.82% for maize and 4.38% for sunflower. The corresponding Kappa coefficient was 0.62. These results indicated that the vegetation and phenology-based classifier using HJ-1A/1B data could effectively identify multi-year distribution of maize and sunflower in the study region. It was found that maize was mainly distributed in the middle part of the irrigation district (Hangjinhouqi and Linhe), while sunflower mainly in the east part (Wuyuan). The planting sites of sunflower had been gradually expanded from Wuyuan to the north part of Hangjinhouqi and Linhe. These results were in agreement with the local economic policy. Results also revealed the increasing trends of both maize and sunflower planting areas during the study period.
Highlights
Crop identification in large irrigation districts is important for crop yield estimation [1,2], hydrological simulation [3], water resources management [4], and agricultural management [5].The cultivated area is usually acquired through agricultural census [6], which is both time-consuming and costly
The results showed the spatial deviation of phenological metrics generated from Normalized Difference Vegetation Index (NDVI) was the largest
Crop Identification Results Based on Optimal Classifier
Summary
Crop identification in large irrigation districts is important for crop yield estimation [1,2], hydrological simulation [3], water resources management [4], and agricultural management [5]. The cultivated area is usually acquired through agricultural census [6], which is both time-consuming and costly. 2017, 9, 855 resolution and large coverage area [8,9,10]. Due to their capability in characterizing crop conditions, various remote sensing-based methods had been widely used as effective tools for crop identification and cultivation area estimation [11,12]. In order to achieve good classification results, two important issues need to be considered: the selection of an appropriate classifier and the sources of remote sensing images [13]
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