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Two Decades of MODIS Data for Land Surface Monitoring: Exploitation of Existing Products and Development of New Algorithms

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (1 November 2021) | Viewed by 19910

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Dr. Álvaro Moreno Martínez

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Guest Editor

Numerical Terradynamic Simulation Group, College of Forestry & Conservation, University of Montana, Missoula, MT 59812, USA
Interests: carbon and water fluxes; biophysical parameters retrieval; data fusion; machine learning

Dr. Emma Izquierdo Verdiguier

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Guest Editor

Institute of Surveying, Remote Sensing and Land Information (IVFL), University Of Natural Resource And Live Science (BOKU), Peter-Jordan-strasse 82, 1190 Vienna, Austria
Interests: machine learning; kernel methods; feature extraction/selection; image classification; cloud computing; land surface phenology

Dr. Nathaniel Paul Robinson

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Guest Editor

Conservation Scientist, Panthera, 8 West 40th Street, 18th Floor, NY 10018, USA
Interests: remote sensing for ecology and conservation; primary productivity; carbon and water fluxes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

NASA launched the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board the Terra (EOS AM-1) and Aqua (EOS PM-1) platforms in 1999 and 2002, respectively. With a one-to-two-day revisit cycle over the past 20 years, these sensors provide one of the most extensive and continuous earth observation datasets, enabling the routine quantification of land surface characteristics such as land cover type, snow cover, surface temperature, leaf area index, and fire occurrence.

The MODIS land science team continuously deploys and updates their algorithms to improve the estimates of leaf area, fraction of absorbed photosynthetically active radiation, vegetation phenology parameters, net and gross primary productivity, and evapotranspiration globally. With the Terra platform close to be decommissioned, the MODIS data record is the longest continuous daily global satellite observation record on Earth. This unique dataset and the vast array of derived products have been extensively validated and used by the scientific community. The variety of data access tools developed by NASA and the free-of-charge data use policy have provided the scientific community with the capacity to effectively use MODIS data for a multitude of applications.

This Special Issue calls for papers on applications, novel analyses, and algorithm development utilizing the MODIS land data products across multiple disciplines. While studies may cross different geographic scales, global studies that take advantage of the extensive MODIS data record are preferred, especially if they provide insights about the underlying processes of land surface change, their impacts, and the prediction of future changes. We also encourage contributions which point out limitations of the current MODIS products and propose solutions to overcome them (algorithm improvements or data fusion approaches).

Articles covering but not limited to recent research on the following topics are encouraged to be submitted to this Special Issue:

Algorithm development
Cloud processing
Algorithm validation and comparison
Forest and vegetation modelling and analysis
Land use and land change analysis
Data fusion approaches
Climate change impacts on vegetation
Spatio-temporal analysis
Water and carbon fluxes monitoring

Dr. Álvaro Moreno Martínez
Dr. Emma Izquierdo Verdiguier
Dr. Nathaniel Paul Robinson
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

MODIS
Vegetation monitoring
Spatio-temporal data
Machine learning
Statistical methods
Radiative transfer models

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Published Papers (4 papers)

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Research

18 pages, 18659 KiB

Open AccessArticle

Modify the Accuracy of MODIS PWV in China: A Performance Comparison Using Random Forest, Generalized Regression Neural Network and Back-Propagation Neural Network

by Zhaohui Xiong, Xiaogong Sun, Jizhang Sang and Xiaomin Wei

Remote Sens. 2021, 13(11), 2215; https://doi.org/10.3390/rs13112215 - 5 Jun 2021

Cited by 16 | Viewed by 2669

Abstract

Water vapor plays an important role in climate change and water cycling, but there are few water vapor products with both high spatial resolution and high accuracy that effectively monitor the change of water vapor. The high precision Global Navigation Satellite System (GNSS) Precipitable Water Vapor (PWV) is often used to calibrate the high spatial resolution Moderate-resolution Imaging Spectroradiometer (MODIS) PWV to produce new PWV product with high accuracy and high spatial resolution. In addition, the machine learning method has a good performance in modifying the accuracy of MODIS PWV. However, the accuracy improvement of different machine learning methods and different modeling timescale is different. In this article, we use three machine learning methods, namely, the Random Forest (RF), Generalized Regression Neural Network (GRNN), and Back-propagation Neural Network (BPNN) to calibrate MODIS PWV in 2019, at annual and monthly timescales. We also use the Multiple Linear Regression (MLR) method for comparison. The root mean squares (RMSs) at the annual timescale with the three machine learning methods are 4.1 mm (BPNN), 3.3 mm (RF), and 3.9 mm (GRNN), and the average RMSs become 2.9 mm (BPNN), 2.8 mm (RF), and 2.5 mm (GRNN) at the monthly timescale. Those results are all better than the MLR method (5.0 mm at the annual timescale and 4.6 mm at the monthly timescale). When there is an obvious variation pattern in the training sample, the RF method can capture the pattern to achieve the best results since the RF achieves the best performance at the annual timescale. Dividing such samples into several sub-samples each having higher internal consistency could further improve the performance of machine learning methods, especially for the GRNN, since GRNN achieves the best performance at the monthly timescale, and the performance of those three machine learning methods at the monthly timescale is better than that of annual timescale. The spatial and temporal variation patterns of the RMS values are significantly weakened after the modeling by machine learning methods for both three methods. Full article

(This article belongs to the Special Issue Two Decades of MODIS Data for Land Surface Monitoring: Exploitation of Existing Products and Development of New Algorithms)

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24 pages, 4919 KiB

Open AccessArticle

Using the MODIS Sensor for Snow Cover Modeling and the Assessment of Drought Effects on Snow Cover in a Mountainous Area

by Pouya Aghelpour, Yiqing Guan, Hadigheh Bahrami-Pichaghchi, Babak Mohammadi, Ozgur Kisi and Danrong Zhang

Remote Sens. 2020, 12(20), 3437; https://doi.org/10.3390/rs12203437 - 19 Oct 2020

Cited by 30 | Viewed by 4519

Abstract

Snow is one of the essential factors in hydrology, freshwater resources, irrigation, travel, pastimes, floods, avalanches, and vegetation. In this study, the snow cover of the northern and southern slopes of Alborz Mountains in Iran was investigated by considering two issues: (1) Estimating the snow cover area and the (2) effects of droughts on snow cover. The snow cover data were monitored by images obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The meteorological data (including the precipitation, minimum and maximum temperature, global solar radiation, relative humidity, and wind velocity) were prepared by a combination of National Centers for Environmental Prediction-Climate Forecast System Reanalysis (NCEP-CFSR) points and meteorological stations. The data scale was monthly and belonged to the 2000–2014 period. In the first part of the study, snow cover estimation was conducted by Multiple Linear Regression (MLR), Least Square Support Vector Machine (LSSVM), Group Method of Data Handling (GMDH), Multilayer Perceptron (MLP), and MLP with Grey Wolf Optimization (MLP-GWO) models. The most accurate estimations were produced by the MLP-GWO and GMDH models. The models produced better snow cover estimations for the northern slope compared to the southern slope. The GWO improved the MLP’s accuracy by 10.7%. In the second part, seven drought indices, including the Palmer Drought Severity Index (PDSI), Bahlme–Mooley Drought Index (BMDI), Standardized Precipitation Index (SPI), Multivariate Standardized Precipitation Index (MSPI), Modified Standardized Precipitation Index (SPImod), Joint Deficit Index (JDI), and Standardized Precipitation-Evapotranspiration Index (SPEI) were calculated for both slopes. The results showed that the effects of a drought event on the snow cover area would remain up to 5 (or 6) months in the region. The highest impact of drought appears after two months in the snow cover area, and the drought index most related to snow cover variations is the 2–month time window of SPI (SPI₂). The results of both subjects were promising and the methods can be examined in other snowy areas of the world. Full article

(This article belongs to the Special Issue Two Decades of MODIS Data for Land Surface Monitoring: Exploitation of Existing Products and Development of New Algorithms)

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22 pages, 11373 KiB

Open AccessArticle

Comparison of Harmonic Analysis of Time Series (HANTS) and Multi-Singular Spectrum Analysis (M-SSA) in Reconstruction of Long-Gap Missing Data in NDVI Time Series

by Hamid Reza Ghafarian Malamiri, Hadi Zare, Iman Rousta, Haraldur Olafsson, Emma Izquierdo Verdiguier, Hao Zhang and Terence Darlington Mushore

Remote Sens. 2020, 12(17), 2747; https://doi.org/10.3390/rs12172747 - 25 Aug 2020

Cited by 28 | Viewed by 6106

Abstract

Monitoring vegetation changes over time is very important in dry areas such as Iran, given its pronounced drought-prone agricultural system. Vegetation indices derived from remotely sensed satellite imageries are successfully used to monitor vegetation changes at various scales. Atmospheric dust as well as airborne particles, particularly gases and clouds, significantly affect the reflection of energy from the surface, especially in visible, short and infrared wavelengths. This results in imageries with missing data (gaps) and outliers while vegetation change analysis requires integrated and complete time series data. This study investigated the performance of HANTS (Harmonic ANalysis of Time Series) algorithm and (M)-SSA ((Multi-channel) Singular Spectrum Analysis) algorithm in reconstruction of wide-gap of missing data. The time series of Normalized Difference Vegetation Index (NDVI) retrieved from Landsat TM in combination with 250m MODIS NDVI time image products are used to simulate and find periodic components of the NDVI time series from 1986 to 2000 and from 2000 to 2015, respectively. This paper presents the evaluation of the performance of gap filling capability of HANTS and M-SSA by filling artificially created gaps in data using Landsat and MODIS data. The results showed that the RMSEs (Root Mean Square Errors) between the original and reconstructed data in HANTS and M-SSA algorithms were 0.027 and 0.023 NDVI value, respectively. Further, RMSEs among 15 NDVI images extracted from the time series artificially and reconstructed by HANTS and M-SSA algorithms were 0.030 and 0.025 NDVI value, respectively. RMSEs of the original and reconstructed data in HANTS and M-SSA algorithms were 0.10 and 0.04 for time series 6, respectively. The findings of this study present a favorable option for solving the missing data challenge in NDVI time series. Full article

(This article belongs to the Special Issue Two Decades of MODIS Data for Land Surface Monitoring: Exploitation of Existing Products and Development of New Algorithms)

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24 pages, 8282 KiB

Open AccessArticle

Estimation of Climatologies of Average Monthly Air Temperature over Mongolia Using MODIS Land Surface Temperature (LST) Time Series and Machine Learning Techniques

by Munkhdulam Otgonbayar, Clement Atzberger, Matteo Mattiuzzi and Avirmed Erdenedalai

Remote Sens. 2019, 11(21), 2588; https://doi.org/10.3390/rs11212588 - 4 Nov 2019

Cited by 21 | Viewed by 5107

Abstract

The objective of this research was to develop a robust statistical model to estimate climatologies (2002–2017) of monthly average near-surface air temperature (Ta) over Mongolia using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) time series products and terrain parameters. Two regression models were analyzed in this study linking automatic weather station data (Ta) with Earth observation (EO) images: Partial least squares (PLS) and random forest (RF). Both models were trained to predict Ta climatologies for each of the twelve months, using up to 17 variables as predictors. The models were applied to the entire land surface of Mongolia, the eighteenth largest but most sparsely populated country in the world. Twelve of the predictor variables were derived from the LST time series products of the Terra MODIS satellite. The LST MOD11A2 (collection 6) products provided thermal information at a spatial resolution of 1 km and with 8-day temporal resolution from 2002 to 2017. Three terrain variables, namely, elevation, slope, and aspect, were extracted using a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), and two variables describing the geographical location of weather stations were extracted from vector data. For training, a total of 8544 meteorological data points from 63 automatic weather stations were used covering the same period as MODIS LST products. The PLS regression resulted in a coefficient of determination (R²) between 0.74 and 0.87 and a root-mean-square error (RMSE) from 1.20 °C to 2.19 °C between measured and estimated monthly Ta. The non-linear RF regression yielded even more accurate results with R² in the range from 0.82 to 0.95 and RMSE from 0.84 °C to 1.93 °C. Using RF, the two best modeled months were July and August and the two worst months were January and February. The four most predictive variables were day/nighttime LST, elevation, and latitude. Using the developed RF models, spatial maps of the monthly average Ta at a spatial resolution of 1 km were generated for Mongolia (~1566 × 10⁶ km²). This spatial dataset might be useful for various environmental applications. The method is transparent and relatively easy to implement. Full article

(This article belongs to the Special Issue Two Decades of MODIS Data for Land Surface Monitoring: Exploitation of Existing Products and Development of New Algorithms)

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