Winter wheat mapping combining variations before and after estimated heading dates

Qiu, Bingwen; Luo, Yi; Tang, Zhenghong; Chen, Chongcheng; Lu, Difei; Huang, Hongyu; Chen, Yunzhi; Chen, Nan; Wang, Xu

doi:10.1016/j.isprsjprs.2016.09.016

Cited by 101 publications

(77 citation statements)

References 34 publications

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“…Although the MODIS images can provide daily observations for characterizing the phenology of winter crops well, their coarse spatial resolution can hardly accurately identify winter crops in China. As the field size of most farmland in China is smaller than a MODIS pixel (250 × 250 m 2 ), village and spring-seeding farmland can be misclassified into winter crops, which has been reported by Qiu et al [2]. With the Landsat or Sentinel-2 images, we can't accurately identify winter crops in the entire study area too; the reason being that there are not enough available images to provide dense observations in the temporal dimension due to the revisit time and the influence of cloudy weather in some regions.…”

Section: Discussionmentioning

confidence: 90%

“…Timely and accurate updates of crop planting area are very important for assessing national food security, for agricultural management, and for the evaluation of ecological functions [1][2][3]. Regional crop-type mapping provides basic data for crop growth monitoring and yield forecasting [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses.Resolution Imaging Spectroradiometer (MODIS) Land Cover Collections [12], and the 1 km Global Land Cover 2000 (GLC2000) [13]). However, more specific information on winter crops is still limited in existing land-cover products [2,14]. On the other hand, the mapping of winter crop planting areas using the traditional agricultural census approach requires a good deal of manpower, money, and time [15].…”

mentioning

confidence: 99%

“…For example, the Chinese government generally publishes this information at least six months after the crops have been harvested. Furthermore, the quality of the survey data tends to suffer from the influence of human errors [3].Remote sensing techniques provide a very effective means to map crops [2,11,16,17], due to their fast responses, periodic observations, wide field of view, and low cost [18][19][20]. However, there are still some challenges associated with large-scale (e.g., provincial-or national-scale) winter crop mapping by remote sensing.…”

mentioning

confidence: 99%

“…Remote sensing techniques provide a very effective means to map crops [2,11,16,17], due to their fast responses, periodic observations, wide field of view, and low cost [18][19][20]. However, there are still some challenges associated with large-scale (e.g., provincial-or national-scale) winter crop mapping by remote sensing.…”

mentioning

confidence: 99%

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Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

et al. 2019

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Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize the growth cycle of crops. However, with the moderate spatial resolution optical imagery acquired by Landsat and Sentinel-2, it is difficult to obtain complete time-series curves for vegetation indices due to the influence of the revisit cycle of the satellite and weather conditions. Therefore, in this study, we propose a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images. The algorithm composites the multi-temporal NDVI into three key values, according to two time-windows-a period of low NDVI values and a period of high NDVI values-for the winter crops. First, we identify the two time-windows, according to the time-series of the NDVI obtained from daily Moderate Resolution Imaging Spectroradiometer observations. Second, the 30 m spatial resolution multi-temporal NDVI curve, derived from the Landsat-7 and -8 and Sentinel-2 optical images, is composited by selecting the maximal value in the high NDVI value period, and the minimal and median values in the low NDVI value period, using an algorithm of the Google Earth Engine. Third, a decision tree classification method is utilized to perform the winter crop classification at a pixel level. The results indicate that this method is effective for the large-scale mapping of winter crops. In the study area, the area of winter crops in 2018 was determined to be 207,641 km 2 , with an overall accuracy of 96.22% and a kappa coefficient of 0.93. The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses.Resolution Imaging Spectroradiometer (MODIS) Land Cover Collections [12], and the 1 km Global Land Cover 2000 (GLC2000) [13]). However, more specific information on winter crops is still limited in existing land-cover products [2,14]. On the other hand, the mapping of winter crop planting areas using the traditional agricultural census approach requires a good deal of manpower, money, and time [15]. Further, the timeliness of the traditional approach is poor. For example, the Chinese government generally publishes this information at least six months after the crops have been harvested. Furthermore, the quality of the survey data tends to suffer from the influence of human errors [3].Remote sensing techniques provide a very effective means to map crops [2,11,16,17], due to their fast responses, periodic observations, wide field of view, and low cost [18][19][20]. However, there are still some challenges associated with large-scale (e.g., provincial-or national-scale) winter crop mapping by remote sensing. MODIS data was the major data source for large-scale crop mapping in previous studies, due to its high revisit...

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Section: Discussionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

et al. 2019

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Evaluating the sensitivity of vegetation and water indices to monitor drought for three Mediterranean crops

et al. 2021

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Understanding drought effects on vegetative health for crop producing locations, such as the Seferihisar‐Kavakdere region in western Turkey, is vital for water and crop management. This study used Landsat‐derived spectral vegetation indices, specifically normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and land surface water index (LSWI) to evaluate and compare their performance and sensitivity for three major crops (citrus, grape, and olive) under dry (2008), wet (2010), and normal (2015) climatic conditions. Results showed that all three crops were affected by drought despite two (citrus and grape) being irrigated. However, grape and olive were more affected by drought than citrus. Drought conditions were exacerbated during September and October because of lack of precipitation and because irrigation is not a common practice during these months in this region. Therefore, this study aimed to use and compare drought sensitivities of Landsat‐derived spectral vegetation indices. All studied indices were closely related (.48 ≤ R2 ≤ .98) for citrus, grape, and olive. The EVI and LSWI were greater than NDVI when NDVI values were greater than 0.65 for citrus, and LSWI values were larger than NDVI when NDVI values were greater than 0.6 for grape and olive during the wet year. The results showed that different vegetation indices are needed to detect drought from one crop to another under different climatic conditions. Our findings indicate that the irrigation applications in 2008 (drought year) were not sufficient to alleviate the severe drought stress experienced throughout the winter and growing seasons.

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Urban expansion or poor productivity: Explaining regional differences in cropland abandonment in China during the early 21st century

et al. 2020

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Cropland abandonment has long been recognized as a marginalization process in rural regions but recently also as a precursor to urbanization in peri-urban regions. This study aimed to evaluate the relative importance of urbanization and poor productivity in cropland abandonment. A thorough investigation was conducted on 345 cities/ towns at the prefecture level or higher in China using 500-m annual maps for 2003-2016. Cropland abandonment was spatiotemporally coupled with the urbanization. At a national scale, approximately 0.55% of the total cropland area (approximately 862 km 2 per year) has been abandoned. Cropland abandonment increased significantly until 2009, declined in the early 2010s, and then leveled off in recent years. In particular, the new first-tier cities suffered the greatest abandonment per capita. However, cropland abandonment was increasingly shifted to lower-tier cities, particularly the high-quality cropland abandonment in the medium-tier cities. Lowquality cropland in rural regions of the medium-and lower-tier cities experienced incessant abandonment. Results showed that the extent of cropland abandonment increased with city sizes described either by population/GDP or by built-up area. High-quality cropland abandonment was more closely related to the built-up area than to population and GDP for total abandonment. Urbanization-induced abandonment in peri-urban areas was dominant for most cities, with exceptions only for a few small cities. Contrary to expectation, cropland abandonment for most cities/ towns in rural regions was not more driven by poor productivity, compared to periurban regions. Our study highlights that urbanization mainly accounted for the regional differences in cropland abandonment in China during the past two decades.

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Winter wheat mapping combining variations before and after estimated heading dates

Cited by 101 publications

References 34 publications

Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

Evaluating the sensitivity of vegetation and water indices to monitor drought for three Mediterranean crops

Urban expansion or poor productivity: Explaining regional differences in cropland abandonment in China during the early 21st century

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