Mapping interannual variability of maize cover in a large irrigation district using a vegetation index – phenological index classifier

Jiang, Lei; Shang, Songhao; Guan, Huade

doi:10.1016/j.compag.2016.03.008

Cited by 22 publications

(29 citation statements)

References 48 publications

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“…The identification accuracy for maize may be very low if only the features from a single metric are used [17]. To solve this problem, many researchers have attempted to combine multiple metrics, such as vegetation indices [18,19], multi-spectral reflectance [20] and phenological metrics [21,22]. However, these metrics are always constrained by the high demand of simultaneity about high spatial and temporal resolution of remote sensing images which is difficult to be reached currently.…”

Section: Introductionmentioning

confidence: 99%

An Identification Method for Spring Maize in Northeast China Based on Spectral and Phenological Features

et al. 2018

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Accurate data about the spatial distribution and planting area of maize is important for policy making, economic development, environmental protection and food security under climate change. This paper proposes a new identification method for spring maize based on spectral and phenological features derived from the moderate resolution imaging spectroradiometer (MODIS) land surface reflectance time-series data. The method focused on the spectral differences of different land cover types in the specific phenological phases of spring maize by testing the selections and combinations of classification metrics, feature extraction methods and classifiers. Taking Liaoning province, a representative planting region of spring maize in Northeast China, as the study area, the results indicated that the combined multiple metrics, including the red reflectance, near-infrared reflectance and normalized difference vegetation index (NDVI), were conducive to the maize identification and were better than any single metric. With regard to the feature extraction and selection, maize identification based on different phenological features selected with prior knowledge was more efficient than that based on statistical features derived from the principal component analysis. Compared with the maximum likelihood classification method, the decision tree classification based on expert knowledge was more suitable for phenological features selected from some prior knowledge. In summary, discriminant rules were defined with those phenological features from multiple metrics, and the decision tree classification was used to identify maize in the study area. The producer's accuracy of maize identification was 98.57%, and the user's accuracy was 81.18%. This method can be potentially applied to an operational identification of maize at large scales based on remote sensing time-series data.

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Section: Introductionmentioning

confidence: 99%

An Identification Method for Spring Maize in Northeast China Based on Spectral and Phenological Features

et al. 2018

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“…The phenology-based crop identification is a combination of phenology and vegetation indexes, which had been effectively applied to multi-year maize classification in the Hetao Irrigation District [11]. This method considered the differences of growth stage and growth condition to identify crops.…”

Section: Crop Identification Model Of Characteristic Ellipsesmentioning

confidence: 99%

“…Survey of crop planting structure was carried out in Hetao Irrigation District during 28 to 30 August 2012 [11]. We obtained 41 sampling sites of maize and 53 sampling sites of sunflower using the Global Positioning System (GPS), which has a positioning accuracy of 2 m-5 m (Figure 1).…”

Section: Sampling and Verification Datamentioning

confidence: 99%

“…Therefore, the intervals between two phenological phases were applied instead of a single phenological phase to represent the growth stage of crops. We also used the fast growth phase (FGP = t_max − t_inf) as a typical phenological metric as Jiang et al [11].…”

Section: Crop Identification Model Of Characteristic Ellipsesmentioning

confidence: 99%

“…Thus, we calculated NDVI from the preprocessed images according to Equation (6). In order to decrease the effects of noise of existing data, the resulting HJ-1A/1B NDVI time series was further fitted with an asymmetric logistic curve [50] (Figure 4), which was also used by Jiang et al [11] in crop identification. The fitting curve equation is…”

Section: Derivation Of Phenological Metricsmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Year Mapping of Maize and Sunflower in Hetao Irrigation District of China with High Spatial and Temporal Resolution Vegetation Index Series

Shang

2017

Remote Sensing

Self Cite

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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.

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Simulating the impact of subsurface pipe drainage systems on crop water productivity at a regional scale in the upper Yellow River Basin

Xue,

Ren

2023

Irrigation and Drainage

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Subsurface pipe drainage (SPD) could provide a suitable environment for crop production in arid and semi‐arid agricultural areas; however, less attention has been given to the design layout of SPD on the regional scale in Hetao Irrigation District (HID). In this study, the appropriate layout of the final subsurface pipe was determined using the distributed SWAP‐WOFOST (Soil–Water–Atmosphere–Plant and WOrld FOod STudies) model in HID. Subsequently, the impact of the proper SPD on crop yield and water productivity (WP) were evaluated. Finally, the crop zoning was adjusted to further enhance crop WP, and the corresponding newly added cultivated land area was preliminarily estimated based on the suggested SPD. The results indicated that after zoning in the suggested SPD, the annual average yields of spring wheat, spring corn and sunflower increased by 19%, 7% and 11%, respectively, and WP improved by 13%, 5% and 10%, respectively, compared to the traditional layout. The newly added cultivated land area was estimated to be approximately 3489 ha through the replacement of traditional end ditches with subsurface pipes in HID. Therefore, the suggested subsurface pipe designs and cropping pattern adjustment improved crop growth and led to an increase in the cultivated land area in HID.

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Mapping interannual variability of maize cover in a large irrigation district using a vegetation index – phenological index classifier

Cited by 22 publications

References 48 publications

An Identification Method for Spring Maize in Northeast China Based on Spectral and Phenological Features

An Identification Method for Spring Maize in Northeast China Based on Spectral and Phenological Features

Multi-Year Mapping of Maize and Sunflower in Hetao Irrigation District of China with High Spatial and Temporal Resolution Vegetation Index Series

Simulating the impact of subsurface pipe drainage systems on crop water productivity at a regional scale in the upper Yellow River Basin

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