A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach

Cai, Yaping; Guan, Kaiyu; Peng, Jian; Wang, Shaowen; Seifert, Christopher; Wardlow, Brian D.; Ли, Жан

doi:10.1016/j.rse.2018.02.045

Cited by 408 publications

(238 citation statements)

References 44 publications

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“…Dong et al [41] mapped paddy rice planting areas in northeastern Asia with Landsat-8 images, a phenology-based algorithm, and the Google Earth Engine. Zhang et al [42] mapped paddy rice planting areas in China using the Google Earth Engine and Sentinel images.Time-series curves of vegetation indices are widely used for mapping crops [43,44]. This is because every crop has a unique phenology during its period of growth [23], and multi-temporal vegetation indices are closely related to crop phenology [45], making them beneficial for improving classification accuracy.…”

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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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“…Time-series curves of vegetation indices are widely used for mapping crops [43,44]. This is because every crop has a unique phenology during its period of growth [23], and multi-temporal vegetation indices are closely related to crop phenology [45], making them beneficial for improving classification accuracy.…”

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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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“…Six spectral bands, including blue, green, red, NIR (near infrared), SWIR1 (shortwave infrared), and SWIR2, were selected and investigated to participate in the classification task. The former four bands with 10-m spatial resolutions were normal bands that were used to distinguish vegetation, while the latter two bands with 20-m spatial resolution were tested in relation to crop water content and have the potential to recognize corn and soybean crops [53]. In addition, three familiar and useful vegetation indices were employed, including the NDVI [54], enhanced vegetation index (EVI) [55], and land surface water index (LSWI) [56], which is defined with the following formulas:…”

Section: Sentinel-2 Imagery and Preprocessingmentioning

confidence: 99%

“…Summarizing all of the band metrics, the NIR is a paramount band and accounts for 16.1%, followed by the NDVI, which accounts for 15.4%; the SWIR1, which accounts for 14.5%; and the SWIR2, which accounts for 11.1% overall. Although the SWIR bands have not been widely used in crop-type classifications, SWIR bands play a major role in corn classification, are related to crop water content [71], and can be used to identify crop types [53] and estimate crop yields [72]. Figure 12.…”

Section: Contribution Of the Features To Classificationmentioning

confidence: 99%

Efficient Identification of Corn Cultivation Area with Multitemporal Synthetic Aperture Radar and Optical Images in the Google Earth Engine Cloud Platform

Tian

Zeng

et al. 2019

Remote Sensing

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The distribution of corn cultivation areas is crucial for ensuring food security, eradicating hunger, adjusting crop structures, and managing water resources. The emergence of high-resolution images, such as Sentinel-1 and Sentinel-2, enables the identification of corn at the field scale, and these images can be applied on a large scale with the support of cloud computing technology. Hebei Province is the major production area of corn in China, and faces serious groundwater overexploitation due to irrigation. Corn was mapped using multitemporal synthetic aperture radar (SAR) and optical images in the Google Earth Engine (GEE) cloud platform. A total of 1712 scenes of Sentinel-2 data and 206 scenes of Sentinel-1 data acquired from June to October 2017 were processed to composite image metrics as input to a random forest (RF) classifier. To avoid speckle noise in the classification results, the pixel-based classification result was integrated with the object segmentation boundary completed in eCognition software to generate an object-based corn map according to crop intensity. The results indicated that the approach using multitemporal SAR and optical images in the GEE cloud platform is reliable for corn mapping. The corn map had a high F1-Score of 90.08% and overall accuracy of 89.89% according to the test dataset, which was not involved in model training. The corn area estimated from optical and SAR images was well correlated with the census data, with an R 2 = 0.91 and a root mean square error (RMSE) of 470.90 km 2 . The results of the corn map are expected to provide detailed information for optimizing crop structure and water management, which are critical issues in this region.Traditional approaches to obtaining crop information, such as ground surveys and sampling, are time consuming, labor intensive, and costly, and cannot obtain continuous spatial distribution data for crops [1,6]. Earth observation satellites that monitor and regularly revisit cropland are inexpensive and excellent data sources that provide full spatial detailed information for crop mapping [8,9]. In recent decades, many studies have focused on crop monitoring with optical [10-14] and synthetic aperture radar (SAR) images [1,[15][16][17][18]. Multitemporal and time series data are powerful tools for identifying different crops and handling the problem of spectral similarity in heterogeneous underlying surfaces in single-period imagery [19][20][21][22][23]. The use of coarse spatial resolution images as original data at scales of hundreds of meters, such as Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High-Resolution Radiometer (AVHRR), is predominant in previous studies because of the fine temporal resolution of the Aqua and Terra satellites. These two satellites revisit the same location at least four times per day, and provide the composition products for free [24][25][26][27][28]. To distinguish cropland and grassland, Estel et al. used MODIS normalized differenced vegetation index (NDVI) time series and rand...

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“…Deep neural networks (DNN) have become ubiquitous these days. They have been successfully applied in a wide range of sectors including automotive [12], government [27], wearable [21], dairy [16], home appliances [25], security and surveillance [15], health [6] and many more, mainly for regression, classification, and anomaly detection problems [5,7,19,20]. The neural network's capability of automatically discovering features to solve any task at hand makes them particularly easy to adapt to new problems and scenarios.…”

Section: Introductionmentioning

confidence: 99%

TSXplain: Demystification of DNN Decisions for Time-Series Using Natural Language and Statistical Features

Munir

Siddiqui

Küsters

et al. 2019

Lecture Notes in Computer Science

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Neural networks (NN) are considered as black-boxes due to the lack of explainability and transparency of their decisions. This significantly hampers their deployment in environments where explainability is essential along with the accuracy of the system. Recently, significant efforts have been made for the interpretability of these deep networks with the aim to open up the black-box. However, most of these approaches are specifically developed for visual modalities. In addition, the interpretations provided by these systems require expert knowledge and understanding for intelligibility. This indicates a vital gap between the explainability provided by the systems and the novice user. To bridge this gap, we present a novel framework i.e. Time-Series eXplanation (TSXplain) system which produces a natural language based explanation of the decision taken by a NN. It uses the extracted statistical features to describe the decision of a NN, merging the deep learning world with that of statistics. The two-level explanation provides ample description of the decision made by the network to aid an expert as well as a novice user alike. Our survey and reliability assessment test confirm that the generated explanations are meaningful and correct. We believe that generating natural language based descriptions of the network's decisions is a big step towards opening up the black-box.

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A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach

Cited by 408 publications

References 44 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

Efficient Identification of Corn Cultivation Area with Multitemporal Synthetic Aperture Radar and Optical Images in the Google Earth Engine Cloud Platform

TSXplain: Demystification of DNN Decisions for Time-Series Using Natural Language and Statistical Features

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