Using Multi-Temporal Remote Sensing Data to Analyze the Spatio-Temporal Patterns of Dry Season Rice Production in Bangladesh

Shew, Aaron M.; Ghosh, Aniruddha

doi:10.5194/isprs-annals-iv-4-w2-61-2017

Cited by 6 publications

(3 citation statements)

References 21 publications

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“…GEE is mainly a free cloud-based service without having to download and manage data locally [35]. It is built upon the Google cloud computing infrastructure and computations are automatically handled by Google itself.…”

Section: A Advantages 1) Cloud Infrastructurementioning

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

651

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: A Advantages 1) Cloud Infrastructurementioning

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

651

216

View full text Add to dashboard Cite

show abstract

“…Regardless of strong relationship between the forecasted and estimated Boro production, it would be meaningful to note that the model-based forecasting may be affected by unexpected weather condition [86,87]. Nevertheless, these error rates at the potential relationship between the sum of MODIS-NDVIs and estimated Boro rice production may be found due to the presence of cloud [88] and atmospheric-moisture contamination in the NDVI signals as well as some methodological errors in the ground-based data collection procedure due to field data collection procedure and data entry, delays in data accumulation to the office, and delivery due to natural disasters like flood, drought, and cyclone. [89,90].…”

Section: Accuracy Assessment Of Boro Rice Production Modelmentioning

confidence: 99%

Remotely Sensed Boro Rice Production Forecasting Using MODIS-NDVI: A Bangladesh Perspective

Faisal¹,

Rahman²,

Sharifee³

et al. 2019

AgriEngineering

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This research work dealt with the development of an operational methodology with appropriate technical components for monitoring and forecasting of rice crop (Boro) production in Bangladesh. Designed system explores integrated application of remote sensing (RS) sciences and Geographic Information System (GIS) technology. Terra MODIS 16-day Normalized Difference Vegetation Index (NDVI) maximum value composite (MVC) image product MOD13A1 of 500 m spatial resolution covering Bangladesh have been utilized for a period 2011–2017. Hence the district-wise sum of NDVI on pixel-by-pixel has been calculated from Jan–April during 2011–2017. Regression analysis between district-based pixel-wise summation of MODIS-NDVI and district-wise BBS (Bangladesh Bureau of Statistics) estimated Boro production revealed strong correlation (R2 = 0.57–0.85) where in March most of the regression coefficient shows significant correlation due to maximize photosynthetic activities. Therefore, the highest regression coefficient value from derived set of coefficient value (BCP-Boro Crop Production Model 2) has been utilized to obtain year-wise rice productions for all the years (2011–2017). Global Positioning System (GPS)-based field verification, accuracy assessment and validation operation have been carried out at randomly selected geographical positions over the country using various statistical tools. The results demonstrate good agreement between estimated and predicted yearly Boro rice production during 2011–2017 time period with Mean Bias Error (MBE) = −29,881 to 19,431 M.Ton; Root Mean Square Error (RMSE) = 5238 to 11,852 M.Ton; Model Efficiency (ME) = (0.86–0.94); Correlation Coefficients = 0.65 to 0.87. Therefore MODIS-NDVI based regression model seems to be effective for Boro production forecasting. The system generally appears to be relatively fast, simple, reasonably accurate and suitable for nation-wide crop statistics generation and food security issues.

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“…The HANTS models predict the NDVI variations with respect to time (i.e., day of the year (DOY) or representing DOY as a fraction of a year) at each pixel locations. The HANTS model for predicting NDVI values with respect to the time component is as follows (Shew and Ghosh, 2017),…”

Section: Hants Model For Reconstructing Ndvi At Cloud Cover Pixelsmentioning

confidence: 99%

Reconstructing NDVI and land surface temperature from cloud cover pixels of Landsat-8 images and assessing vegetation health index in the Northeast region of Thailand

Mohanasundaram

Baghel

Thakur

et al. 2022

Preprint

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Critical applications of satellite data products include monitoring vegetation dynamics using vegetation indices such as Normalized Difference Vegetation Index (NDVI) and thermal characteristics such as Land Surface Temperature (LST) variations across different spatial and temporal scales. One of the major problems with passive remote sensing satellite data products is cloud and shadow cover that void the information from a specific image. The cloud cover problems further amplified during monsoon seasons as the formation of clouds are more frequent during these seasons. The present study proposes temporal aggregation of images and developing Harmonic Analysis of Time Series (HANTS) and Pixel-wise Multiple Linear Regression (PMLR) algorithms for retrieving cloud contaminated NDVI and LST information from Landsat-8 (L8) data products, respectively. The developed algorithms were applied in the northeastern part of Thailand to recover the missing NDVI and LST values from time series of L8 images acquired in 2018. The predicted NDVI and LST values at artificially clouded locations were compared with the corresponding clear pixel values. Additionally, the model predicted LST and NDVI values were also compared with MODIS LST and NDVI data sets. The calculated root mean square (RMSE) values were ranging from 0.004 to 0.05 and 1.05°C to 3.11°C for NDVI and LST variables, respectively. The validation statistics show that these models can be satisfactorily applied to retrieve NDVI and LST values from cloud-contaminated pixels of L8 images. Furthermore, a vegetation health index (VHI) calculated at province level show that most of the western provinces showed a healthy vegetation condition than other provinces in the northeast of Thailand.

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Using Multi-Temporal Remote Sensing Data to Analyze the Spatio-Temporal Patterns of Dry Season Rice Production in Bangladesh

Cited by 6 publications

References 21 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

Remotely Sensed Boro Rice Production Forecasting Using MODIS-NDVI: A Bangladesh Perspective

Reconstructing NDVI and land surface temperature from cloud cover pixels of Landsat-8 images and assessing vegetation health index in the Northeast region of Thailand

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