A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine

Kong, Dongdong; Zhang, Yongqiang; Gu, Xihui; Wang, Dagang

doi:10.1016/j.isprsjprs.2019.06.014

Cited by 116 publications

(78 citation statements)

References 42 publications

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“…Moreover, since the quality of every input data directly affects the final accuracy of studies, image processing must be considered a necessity. Precision, level of automation, reliability, computational complexity, and time-consumption are the most critical criteria in developing image processing algorithms [149], [150]. Therefore, to ensure high-quality results, it is inevitable to develop and enhance the existing image processing algorithms within GEE protocols.…”

Section: H Image Processingmentioning

confidence: 99%

“…Therefore, to ensure high-quality results, it is inevitable to develop and enhance the existing image processing algorithms within GEE protocols. In this regard, researchers have employed GEE to develop various efficient and useful image processing algorithms, such as cloud masking [12], [149], data selection and enhancement [13], [150], image-based sensor calibration [151], [152], and training sample migration [153]. For instance, [150] introduced weighted Whittaker with a dynamic parameter (wWHd) de-noising method within GEE to reconstruct the vegetation phenology based on 500m MODIS EVI products.…”

Section: H Image Processingmentioning

confidence: 99%

See 1 more Smart Citation

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

662

216

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Remote Sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 and May 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest (RF), were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use (LCLU) classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications has significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.

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Section: H Image Processingmentioning

confidence: 99%

Section: H Image Processingmentioning

confidence: 99%

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

662

216

View full text Add to dashboard Cite

show abstract

“…Similarly, 105°C was taken as the threshold to predict the flowering date from the heading date [31]. The improved Whittaker smoother [32,33] was chosen to reconstruct the data on time-series MODIS LAI; based on this, the iLAI-Logistic method was utilized to extract the transition date [34,35]. According to the growth characteristics of winter wheat, green-up is the stage of rapid greening for leaves, while heading is the best stage of nutritional and reproductive growth for the crop, corresponding to the maximum values of the first curvature of cumulative data and that of the reconstructed data, respectively (Figure 3) [35].…”

Section: Host and Habitat Conditions Extractionmentioning

confidence: 99%

Dynamic Remote Sensing Prediction for Wheat Fusarium Head Blight by Combining Host and Habitat Conditions

et al. 2020

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Remote sensing technology provides a feasible option for early prediction for wheat Fusarium head blight (FHB). This study presents a methodology for the dynamic prediction of this classic meteorological crop disease. Host and habitat conditions were comprehensively considered as inputs of the FHB prediction model, and the advantages, accuracy, and generalization ability of the model were evaluated. Firstly, multi-source satellite images were used to predict growth stages and to obtain remote sensing features, then weather features around the predicted stages were extracted. Then, with changes in the inputting features, the severity of FHB was dynamically predicted on February 18, March 6, April 23, and May 9, 2017. Compared to the results obtained by the Logistic model, the prediction with the Relevance Vector Machine performed better, with the overall accuracy on these four dates as 0.71, 0.78, 0.85, and 0.93, and with the area under the receiver operating characteristic curve as 0.66, 0.67, 0.72, and 0.75. Additionally, compared with the prediction with only one factor, the integration of multiple factors was more accurate. The results showed that when the date of the remote sensing features was closer to the heading or flowering stage, the prediction was more accurate, especially in severe areas. Though the habitat conditions were suitable for FHB, the infection can be inhibited when the host’s growth meets certain requirements.

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“…The calibrated images were used to composite the 16-day TOA reflectance time series of each pixel. Based on the harmonized TOA reflectance composite, the following vegetation indices were calculated: weighted Whittaker smoother (Kong et al 2019) was adopted to generate a dataset of smoothed, seamless image time series of vegetation indices at a resolution of 30 m.…”

Section: Satellite Images and Vegetation Indicesmentioning

confidence: 99%

GCI30: a global dataset of 30-m cropping intensity using multisource remote sensing imagery

Zhang

Zeng

et al. 2021

Preprint

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Abstract. The global distribution of cropping intensity (CI) is essential to our understanding of agricultural land use management on Earth. Optical remote sensing has revolutionized our ability to map CI over large areas in a repeated and cost-efficient manner. Previous studies have mainly focused on investigating the spatiotemporal patterns of CI ranging from regions to the entire globe with the use of coarse-resolution data, which are inadequate for characterizing farming practices within heterogeneous landscapes. To fill this knowledge gap, in this study, we utilized multiple satellite data to develop a global, spatially continuous CI map dataset at 30-m resolution (GCI30). Accuracy assessments indicated that GCI30 exhibited high agreement with visually interpreted validation samples and in situ observations from the PhenoCam network. We carried out both statistical and spatial comparisons of GCI30 with existing global CI estimates. Based on GCI30, we estimated that the global average annual CI during 2016–2018 was 1.05, which is close to the mean (1.04) and median (1.13) CI values of the existing six estimates, although the spatial resolution and temporal coverage vary significantly among products. A spatial comparison with two other satellite based land surface phenology products further suggested that GCI30 was not only capable of capturing the overall pattern of global CI but also provided many spatial details. GCI30 indicated that single cropping was the primary agricultural system on Earth, accounting for 81.57 % (12.28 million km2) of the world’s cropland extent. Multiple-cropping systems, on the other hand, were commonly observed in South America and Asia. We found large variations across countries and agroecological zones, reflecting the joint control of natural and anthropogenic drivers on regulating cropping practices. As the first global coverage, fine-resolution CI product, GCI30 can facilitate ongoing efforts to achieve sustainable development goals (SDGs) by improving food production while minimizing environmental impacts. The data are available on Harvard Dataverse: https://doi.org/10.7910/DVN/86M4PO (Zhang et al, 2020).

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A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine

Cited by 116 publications

References 42 publications

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Dynamic Remote Sensing Prediction for Wheat Fusarium Head Blight by Combining Host and Habitat Conditions

GCI30: a global dataset of 30-m cropping intensity using multisource remote sensing imagery

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