A method of estimating crop acreage in large-scale by unmixing of MODIS data

An accurate assessment of evapotranspiration (ET) is crucially needed at the basin scale for studying the hydrological processes and water balance especially from upstream to downstream. In the mountains, this term is poorly understood because of various challenges, including the vegetation complexity, plant diversity, lack of available data and because the in situ direct measurement of ET is difficult in complex terrain. The main objective of this work was to investigate the potential of a Two-Source-Energy-Balance model (TSEB) driven by the Landsat and MODIS data for estimating ET over a complex mountain region. The complexity is associated with the type of the vegetation canopy as well as the changes in topography. For validating purposes, a large-aperture scintillometer (LAS) was set up over a heterogeneous transect of about 1.4 km to measure sensible (H) and latent heat (LE) fluxes. Additionally, two towers of eddy covariance (EC) systems were installed along the LAS transect. First, the model was tested at the local scale against the EC measurements using multi-scale remote sensing (MODIS and Landsat) inputs at the satellite overpasses. The obtained averaged values of the root mean square error (RMSE) and correlation coefficient (R) were about 72.4 Wm−2 and 0.79 and 82.0 Wm−2 and 0.52 for Landsat and MODIS data, respectively. Secondly, the potential of the TSEB model for evaluating the latent heat fluxes at large scale was investigated by aggregating the derived parameters from both satellites based on the LAS footprint. As for the local scale, the comparison of the latent heat fluxes simulated by TSEB driven by Landsat data performed well against those measured by the LAS (R = 0.69, RMSE = 68.0 Wm−2), while slightly more scattering was observed when MODIS products were used (R = 0.38, RMSE = 99.8 Wm−2). Based on the obtained results, it can be concluded that (1) the TSEB model can be fairly used to estimate the evapotranspiration over the mountain regions; and (2) medium- to high-resolution inputs are a better option than coarse-resolution products for describing this kind of complex terrain.

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Large-scale mapping of aboveground biomass of tropical rainforest in Sulawesi, Indonesia, using Landsat ETM+ and MODIS data

GIScience & Remote Sensing ◽

10.1080/15481603.2013.850305 ◽

2013 ◽

Vol 50 (6) ◽

pp. 633-651 ◽

Cited By ~ 10

Author(s):

Pavel Propastin

Keyword(s):

Aboveground Biomass ◽

Large Scale ◽

Tropical Rainforest ◽

Modis Data

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Forest and Field Fire Search System Using MODIS Data

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2007.p1043 ◽

2007 ◽

Vol 11 (8) ◽

pp. 1043-1048 ◽

Cited By ~ 2

Author(s):

Eiji Nunohiro ◽

◽

Kei Katayama ◽

Kenneth J. Mackin ◽

Jong Geol Park ◽

...

Keyword(s):

Large Scale ◽

Image Database ◽

System Configuration ◽

Search System ◽

Modis Data ◽

Level Data ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Process Level

Tokyo University of Information Sciences receives MODIS (Moderate Resolution Imaging Spectroradiometer) data from NASA’s Terra and Aqua satellites, and provides the processed data to universities and research institutes as part of the academic frontier project. This paper considers the utilization of MODIS data for a system to search for fire regions in forests and fields. For the search system to be effective, the system must be able to extract the location, range and distribution of fires in forests and fields from a large scale image database quickly with high accuracy. In order to achieve high search response time and to improve the accuracy of the analysis, we propose a forest and field fire search system which implements a) a parallel distributed system configuration using multiple PC clusters, and b) MOD02, MOD03 and MOD09 process levels of MODIS data for input data which provide higher resolution and more accurate readings than the standard MOD14 process level data.

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New Statistical Model for Variability of Aerosol Optical Thickness: Theory and Application to MODIS Data over Ocean*

Journal of the Atmospheric Sciences ◽

10.1175/jas-d-15-0130.1 ◽

2016 ◽

Vol 73 (2) ◽

pp. 821-837 ◽

Cited By ~ 5

Author(s):

Mikhail D. Alexandrov ◽

Igor V. Geogdzhayev ◽

Kostas Tsigaridis ◽

Alexander Marshak ◽

Robert Levy ◽

...

Keyword(s):

Remote Sensing ◽

Optical Thickness ◽

Large Scale ◽

Climate Models ◽

Marine Boundary Layer ◽

Structure Functions ◽

Aerosol Optical Thickness ◽

Small Scale ◽

Scale Invariant ◽

Modis Data

Abstract A novel model for the variability in aerosol optical thickness (AOT) is presented. This model is based on the consideration of AOT fields as realizations of a stochastic process that is the exponent of an underlying Gaussian process with a specific autocorrelation function. In this approach, AOT fields have lognormal PDFs and structure functions with the correct asymptotic behavior at large scales. The latter is an advantage compared with fractal (scale invariant) approaches. The simple analytical form of the structure function in the proposed model facilitates its use for the parameterization of AOT statistics derived from remote sensing data. The new approach is illustrated using a 1-yr-long global MODIS AOT dataset (over ocean) with 10-km resolution. It was used to compute AOT statistics for sample cells forming a grid with 5° spacing. The observed shapes of the structure functions indicated that, in a large number of cases, the AOT variability is split into two regimes that exhibit different patterns of behavior: small-scale stationary processes and trends reflecting variations at larger scales. The small-scale patterns are suggested to be generated by local aerosols within the marine boundary layer, while the large-scale trends are indicative of elevated aerosols transported from remote continental sources. This assumption is evaluated by comparison of the geographical distributions of these patterns derived from MODIS data with those obtained from the GISS GCM. This study shows considerable potential to enhance comparisons between remote sensing datasets and climate models beyond regional mean AOTs.

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A method of estimating crop acerage in large scale by unmixing of MODIS data

10.1117/12.651855 ◽

2005 ◽

Author(s):

Wenbo Xu ◽

Yichen Tian ◽

Jun Qing ◽

Jianxi Huang ◽

Yong Zhang

Keyword(s):

Large Scale ◽

Modis Data

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The Research of the Quantitative Method of Desertification Assessment at Large Scale Based on MODIS Data and Decision Tree Model - A Case Study in Farming-Pastoral Region of North China

2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering ◽

10.1109/rsete.2012.6260783 ◽

2012 ◽

Cited By ~ 2

Author(s):

Duanyang Xu ◽

Chunlei Li ◽

Xiao Song

Keyword(s):

Decision Tree ◽

Quantitative Method ◽

Large Scale ◽

North China ◽

Decision Tree Model ◽

Tree Model ◽

Modis Data ◽

Desertification Assessment

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Monitoring of rapid land cover changes in eastern Japan using Terra/MODIS data

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w3-403-2015 ◽

2015 ◽

Vol XL-7/W3 ◽

pp. 403-408 ◽

Cited By ~ 2

Author(s):

I. Harada ◽

K. Hara ◽

J. Park ◽

I. Asanuma ◽

M. Tomita ◽

...

Keyword(s):

Land Cover ◽

Nuclear Power ◽

Large Scale ◽

Agricultural Land ◽

Modern World ◽

Remotely Sensed Data ◽

Modis Data ◽

Terra Modis ◽

Eastern Japan ◽

Conservation Of Biodiversity

Vegetation and land cover in Japan are rapidly changing. Abandoned farmland in 2010, for example, was 396,000 ha, or triple that of 1985. Efficient monitoring of changes in land cover is vital to both conservation of biodiversity and sustainable regional development. The Ministry of Environment is currently producing 1/25,000 scale vegetation maps for all of Japan, but the work is not yet completed. Traditional research is time consuming, and has difficulty coping with the rapid nature of change in the modern world. In this situation, classification of various scale remotely sensed data can be of premier use for efficient and timely monitoring of changes in vegetation.. In this research Terra/MODIS data is utilized to classify land cover in all of eastern Japan. Emphasis is placed on the Tohoku area, where large scale and rapid changes in vegetation have occurred in the aftermath of the Great Eastern Japan Earthquake of 11 March 2011. Large sections of coastal forest and agricultural lands, for example, were directly damaged by the earthquake or inundated by subsequent tsunami. Agricultural land was also abandoned due to radioactive contamination from the Fukushima nuclear power plant accident. The classification results are interpreted within the framework of a Landscape Transformation Sere model developed by Hara et al (2010), which presents a multi-staged pattern for tracking vegetation changes under successively heavy levels of human interference. The results of the research will be useful for balancing conservation of biodiversity and ecosystems with the needs for regional redevelopment.

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