Mapping croplands, cropping patterns, and crop types using MODIS time-series data

Chen, Yaoliang; Lu, Dengsheng; Moran, Emílio F.; Batistella, Mateus; Dutra, Luciano Vieira; Sanches, I. D.; Silva, Ramon Felipe Bicudo da; Huang, Jingfeng; Luiz, A. J. B.; Oliveira, Maria Antonia Falcão de

doi:10.1016/j.jag.2018.03.005

Cited by 107 publications

(72 citation statements)

References 57 publications

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“…Medium-high resolution crop mapping at regional scales can provide basic data for the more precise regulation of agricultural production and global change governance [1][2][3][4][5][6][7], and it is an important support for implementing sustainable agriculture [8]. At present, crop classification based on remote sensing data mainly adopts the strategy of supervised classification [9][10][11], which means that sample data must be used for the model training. Crop samples are difficult to obtain by visual interpretation, so they can only be collected in the field.…”

Section: Introductionmentioning

confidence: 99%

Crop Mapping Based on Historical Samples and New Training Samples Generation in Heilongjiang Province, China

Lin

Liu

et al. 2019

Sustainability

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Accurate, year-by-year crop distribution information is a key element in agricultural production regulation and global change governance. However, due to the high sampling costs and insufficient use of historical samples, a supervised classifying method for sampling every year is unsustainable for mapping crop types over time. Therefore, this paper proposes a method for the generation and screening of new samples for 2018 based on historical crop samples, and then it builds a crop mapping model for that current season. Pixels with the same crop type in the historical year (2013-2017) were extracted as potential samples, and their spectral features and spatial information in the current year (2018) were used to generate new samples based on clustering screening. The research result shows that when the clustering number is different, the number and structure of new generated sample also changes. The sample structure generated in Luobei County was not balanced, with the 'other crop' representing less than 3.97%, but the structure of southwest Hulin City was more balanced. Based on the newly generated samples and the ground reference data of classified year, the classification models were constructed. The average classification accuracies of Luobei County in 2018 based on new generated samples and field samples were 69.35% and 77.59%, respectively, while those of southwest Hulin City were 80.44% and 82.94%, respectively. Combined with historical samples and the spectral information of the current year, this study proposes a method to generate new samples. It can overcome the problem of crop samples only being collected in the field due to the difficulty of visual interpretation, effectively improve the use of historical data, and also provide a new idea for sustainable crop mapping in many regions lacking seasonal field samples.Sustainability 2019, 11, 5052 2 of 17 samples. In addition, in existing crop classification studies, seasonal samples are often only used for crop classification in the current season, and they are rarely used for the next season or even the next few seasons, resulting in a low utilization rate of historical data [1,6,8]. The difficulty of sampling and the low utilization of historical data have led to the high cost of year-by-year mapping. At the same time, after collecting data every year, it is necessary to carry out complex operations such as data preprocessing, and the final crop map is often obtained after the harvest, which leads to a lag of crop mapping and its guiding role for farming being greatly weakened. Therefore, it is difficult to meet the demand for crop mapping by sampling every year. The question of how to sustainably carry out crop classification based on historical data has become a research hotspot.There are three main methods for classifying crops based on historical data: One is to reuse the spectral curve, the second is to reuse the training model, and the third is to generate "training samples."Reusing spectral curves is a method for classification based on the fact t...

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Section: Introductionmentioning

confidence: 99%

Crop Mapping Based on Historical Samples and New Training Samples Generation in Heilongjiang Province, China

Lin

Liu

et al. 2019

Sustainability

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“…The overall ICL classification accuracies, in the two validations, were similar to those reported by previous studies that classified summer and winter crops in Mato Grosso using a MODIS vegetation index. Using MODIS/NDVI, Chen et al [40] classified six crop arrangements; the soya-pasture (ICLm) class had a UA of 0.81 and PA of 0.74. Maus et al [29] demonstrated the functionality of the TWDTW method for property-scale classification with an overall accuracy of 90%.…”

Section: Discussionmentioning

confidence: 99%

“…We chose to use the Enhanced Vegetation Index (EVI) product of the MODIS/Terra sensor as it had images recorded throughout the study period and adequate time resolution for seasonal agricultural identification [39][40][41]. Among the MODIS products available, we selected EVI from MOD13Q1, with a 16-day composition and 250 m spatial resolution [42].…”

Section: Modis Datamentioning

confidence: 99%

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Framework for Mapping Integrated Crop-Livestock Systems in Mato Grosso, Brazil

2018

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Integrated crop-livestock (ICL) systems combine livestock and crop production in the same area, increasing the efficiency of land use and machinery, while mitigating greenhouse gas emissions, and reducing production risks, plant diseases and pests. ICL systems are primarily divided into annual (ICLa) and multi-annual (ICLm) systems. Projects such as the “Integrated crop-livestock-forest Network” and the “Livestock Rally” have estimated the ICL areas for Brazil on a state or regional basis. However, it remains necessary to create methods for spatial identification of ICL areas. Thus, we developed a framework for mapping ICL areas in Mato Grosso, Brazil using the Enhanced Vegetation Index time-series of Moderate Resolution Imaging Spectroradiometer and a Time-Weighted Dynamic Time Warping (TWDTW) classification method. The classification of ICL areas occurred in three phases. Phase 1 corresponded to the classification of land use from 2008 to 2016. In Phase 2, the ICLa areas were identified. Finally, Phase 3 corresponded to the ICLm identification. The framework showed overall accuracies of 86% and 92% for ICL areas. ICLm accounted for 87% of the ICL areas. Considering only agricultural areas or only pasture areas, ICL systems represented 5% and 15%, respectively.

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“…A large number of studies on crop type and cropland mapping used field data from the same mapping year [24][25][26]. This way of mapping is limited in situations, where there are no ground truth data available, or data collection is impossible for the period of interest.…”

Section: Introductionmentioning

confidence: 99%

Multi-Temporal Agricultural Land-Cover Mapping Using Single-Year and Multi-Year Models Based on Landsat Imagery and IACS Data

Kyere¹,

Astor²,

Graß³

et al. 2019

Agronomy

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The spatial distribution and location of crops are necessary information for agricultural planning. The free availability of optical satellites such as Landsat offers an opportunity to obtain this key information. Crop type mapping using satellite data is challenged by its reliance on ground truth data. The Integrated Administration and Control System (IACS) data, submitted by farmers in Europe for subsidy payments, provide a solution to the issue of periodic field data collection. The present study tested the performance of the IACS data in the development of a generalized predictive crop type model, which is independent of the calibration year. Using the IACS polygons as objects, the mean spectral information based on four different vegetation indices and six Landsat bands were extracted for each crop type and used as predictors in a random forest model. Two modelling methods called single-year (SY) and multiple-year (MY) calibration were tested to find out their performance in the prediction of grassland, maize, summer, and winter crops. The independent validation of SY and MY resulted in a mean overall accuracy of 71.5% and 77.3%, respectively. The field-based approach of calibration used in this study dealt with the ‘salt and pepper’ effects of the pixel-based approach.

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Mapping croplands, cropping patterns, and crop types using MODIS time-series data

Cited by 107 publications

References 57 publications

Crop Mapping Based on Historical Samples and New Training Samples Generation in Heilongjiang Province, China

Crop Mapping Based on Historical Samples and New Training Samples Generation in Heilongjiang Province, China

Framework for Mapping Integrated Crop-Livestock Systems in Mato Grosso, Brazil

Multi-Temporal Agricultural Land-Cover Mapping Using Single-Year and Multi-Year Models Based on Landsat Imagery and IACS Data

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