Cropland distributions from temporal unmixing of MODIS data

David B. Lobell, Gregory P. Asner

Research output: Contribution to journalArticle

242 Citations (Scopus)

Abstract

Knowledge of the distribution of crop types is important for land management and trade decisions, and is needed to constrain remotely sensed estimates of variables, such as crop stress and productivity. The Moderate Resolution Imaging Spectroradiometer (MODIS) offers a unique combination of spectral, temporal, and spatial resolution compared to previous global sensors, making it a good candidate for large-scale crop type mapping. However, because of subpixel heterogeneity, the application of traditional hard classification approaches to MODIS data may result in significant errors in crop area estimation. We developed and tested a linear unmixing approach with MODIS that estimates subpixel fractions of crop area based on the temporal signature of reflectance throughout the growing season. In this method, termed probabilistic temporal unmixing (PTU), endmember sets were constructed using Landsat data to identify pure pixels, and uncertainty resulting from endmember variability was quantified using Monte Carlo simulation. This approach was evaluated using Landsat classification maps in two intensive agricultural regions, the Yaqui Valley (YV) of Mexico and the Southern Great Plains (SGP). Performance of the mixture model varied depending on the scale of comparison, with R 2 ranging from roughly 50% for estimating crop area within individual pixels to greater than 80% for crop cover within areas over 10 km 2. The results of this study demonstrate the importance of subpixel heterogeneity in cropland systems, and the potential of temporal unmixing to provide accurate and rapid assessments of land cover distributions using coarse resolution sensors, such as MODIS.

Original languageEnglish (US)
Pages (from-to)412-422
Number of pages11
JournalRemote Sensing of Environment
Volume93
Issue number3
DOIs
StatePublished - Nov 15 2004
Externally publishedYes

Fingerprint

moderate resolution imaging spectroradiometer
MODIS
Crops
Imaging techniques
crop
crops
Landsat
pixel
sensor
taxonomy
Pixels
cover crop
cover crops
land management
land cover
Sensors
reflectance
distribution
cropland
spatial resolution

Keywords

  • Agriculture
  • Croplands
  • Decision tree
  • Landsat
  • Mixture modeling
  • MODIS

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences

Cite this

Cropland distributions from temporal unmixing of MODIS data. / Lobell, David B.; Asner, Gregory P.

In: Remote Sensing of Environment, Vol. 93, No. 3, 15.11.2004, p. 412-422.

Research output: Contribution to journalArticle

@article{cc120d2b7d404ac78b40103ad443db76,
title = "Cropland distributions from temporal unmixing of MODIS data",
abstract = "Knowledge of the distribution of crop types is important for land management and trade decisions, and is needed to constrain remotely sensed estimates of variables, such as crop stress and productivity. The Moderate Resolution Imaging Spectroradiometer (MODIS) offers a unique combination of spectral, temporal, and spatial resolution compared to previous global sensors, making it a good candidate for large-scale crop type mapping. However, because of subpixel heterogeneity, the application of traditional hard classification approaches to MODIS data may result in significant errors in crop area estimation. We developed and tested a linear unmixing approach with MODIS that estimates subpixel fractions of crop area based on the temporal signature of reflectance throughout the growing season. In this method, termed probabilistic temporal unmixing (PTU), endmember sets were constructed using Landsat data to identify pure pixels, and uncertainty resulting from endmember variability was quantified using Monte Carlo simulation. This approach was evaluated using Landsat classification maps in two intensive agricultural regions, the Yaqui Valley (YV) of Mexico and the Southern Great Plains (SGP). Performance of the mixture model varied depending on the scale of comparison, with R 2 ranging from roughly 50{\%} for estimating crop area within individual pixels to greater than 80{\%} for crop cover within areas over 10 km 2. The results of this study demonstrate the importance of subpixel heterogeneity in cropland systems, and the potential of temporal unmixing to provide accurate and rapid assessments of land cover distributions using coarse resolution sensors, such as MODIS.",
keywords = "Agriculture, Croplands, Decision tree, Landsat, Mixture modeling, MODIS",
author = "Lobell, {David B.} and Asner, {Gregory P.}",
year = "2004",
month = "11",
day = "15",
doi = "10.1016/j.rse.2004.08.002",
language = "English (US)",
volume = "93",
pages = "412--422",
journal = "Remote Sensing of Environment",
issn = "0034-4257",
publisher = "Elsevier Inc.",
number = "3",

}

TY - JOUR

T1 - Cropland distributions from temporal unmixing of MODIS data

AU - Lobell, David B.

AU - Asner, Gregory P.

PY - 2004/11/15

Y1 - 2004/11/15

N2 - Knowledge of the distribution of crop types is important for land management and trade decisions, and is needed to constrain remotely sensed estimates of variables, such as crop stress and productivity. The Moderate Resolution Imaging Spectroradiometer (MODIS) offers a unique combination of spectral, temporal, and spatial resolution compared to previous global sensors, making it a good candidate for large-scale crop type mapping. However, because of subpixel heterogeneity, the application of traditional hard classification approaches to MODIS data may result in significant errors in crop area estimation. We developed and tested a linear unmixing approach with MODIS that estimates subpixel fractions of crop area based on the temporal signature of reflectance throughout the growing season. In this method, termed probabilistic temporal unmixing (PTU), endmember sets were constructed using Landsat data to identify pure pixels, and uncertainty resulting from endmember variability was quantified using Monte Carlo simulation. This approach was evaluated using Landsat classification maps in two intensive agricultural regions, the Yaqui Valley (YV) of Mexico and the Southern Great Plains (SGP). Performance of the mixture model varied depending on the scale of comparison, with R 2 ranging from roughly 50% for estimating crop area within individual pixels to greater than 80% for crop cover within areas over 10 km 2. The results of this study demonstrate the importance of subpixel heterogeneity in cropland systems, and the potential of temporal unmixing to provide accurate and rapid assessments of land cover distributions using coarse resolution sensors, such as MODIS.

AB - Knowledge of the distribution of crop types is important for land management and trade decisions, and is needed to constrain remotely sensed estimates of variables, such as crop stress and productivity. The Moderate Resolution Imaging Spectroradiometer (MODIS) offers a unique combination of spectral, temporal, and spatial resolution compared to previous global sensors, making it a good candidate for large-scale crop type mapping. However, because of subpixel heterogeneity, the application of traditional hard classification approaches to MODIS data may result in significant errors in crop area estimation. We developed and tested a linear unmixing approach with MODIS that estimates subpixel fractions of crop area based on the temporal signature of reflectance throughout the growing season. In this method, termed probabilistic temporal unmixing (PTU), endmember sets were constructed using Landsat data to identify pure pixels, and uncertainty resulting from endmember variability was quantified using Monte Carlo simulation. This approach was evaluated using Landsat classification maps in two intensive agricultural regions, the Yaqui Valley (YV) of Mexico and the Southern Great Plains (SGP). Performance of the mixture model varied depending on the scale of comparison, with R 2 ranging from roughly 50% for estimating crop area within individual pixels to greater than 80% for crop cover within areas over 10 km 2. The results of this study demonstrate the importance of subpixel heterogeneity in cropland systems, and the potential of temporal unmixing to provide accurate and rapid assessments of land cover distributions using coarse resolution sensors, such as MODIS.

KW - Agriculture

KW - Croplands

KW - Decision tree

KW - Landsat

KW - Mixture modeling

KW - MODIS

UR - http://www.scopus.com/inward/record.url?scp=5044236302&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=5044236302&partnerID=8YFLogxK

U2 - 10.1016/j.rse.2004.08.002

DO - 10.1016/j.rse.2004.08.002

M3 - Article

AN - SCOPUS:5044236302

VL - 93

SP - 412

EP - 422

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

IS - 3

ER -