A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data

Toshihiro Sakamoto, Brian D. Wardlow, Anatoly A Gitelson, Shashi B. Verma, Andrew E. Suyker, Timothy J. Arkebauer

Research output: Contribution to journalArticle

117 Citations (Scopus)

Abstract

The crop developmental stage represents essential information for irrigation scheduling/fertilizer management, understanding seasonal ecosystem carbon dioxide (CO2) exchange, and evaluating crop productivity. In this study, we devised an approach called the Two-Step Filtering (TSF) for detecting the phenological stages of maize and soybean from time-series Wide Dynamic Range Vegetation Index (WDRVI) data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m observations. The TSF method consists of a Two-Step Filtering scheme that includes: (i) smoothing the temporal WDRVI data with a wavelet-based filter and (ii) deriving the optimum scaling parameters from shape-model fitting procedure. The date of key crop development stages are then estimated by using the optimum scaling parameters and an initial value of the specific phenological date on the shape model, which are preliminary defined in reference to ground-based crop growth stage observations. The shape model is a crop-specific WDRVI curve with typical seasonal features, which were defined by averaging smoothed, multi-year WDRVI profiles from MODIS 250-m data collected over irrigated maize and soybean study sites.In this study, the TSF method was applied to MODIS-derived WDRVI data over a 6-year period (2003 to 2008) for two irrigated sites and one rainfed site planted to either maize or soybean as part of the Carbon Sequestration Program (CSP) at the University of Nebraska-Lincoln. A comparison of satellite-based retrievals with ground-based crop growth stage observations collected by the CSP over the six growing seasons for these three sites showed that the TSF method can accurately estimate the date of four key phenological stages of maize (V2.5: early vegetative stage, R1: silking stage, R5: dent stage and R6: maturity) and soybean (V1: early vegetative stage, R5: beginning seed, R6: full seed and R7: beginning maturity). The root mean square error (RMSE) of phenological-stage estimation for maize ranged from 2.9 [R1] to 7.0 [R5] days and from 3.2 [R6] to 6.9 [R7] days for soybean, respectively. In addition, the TSF method was also applied for two years (2001 and 2002) over eastern Nebraska to test its ability to characterize the spatio-temporal patterns of these key phenological stages over a larger geographic area. The MODIS-derived crop phenological stage dates agreed well with the statistical crop progress data reported by the United State Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) for eastern Nebraska's three crop agricultural statistic districts (ASDs). At the ASD-level, the RMSE of phenological-stage estimation ranged from 1.6 [R1] to 5.6 [R5] days for maize and from 2.5 [R7] to 5.3 [R5] days for soybean.

Original languageEnglish (US)
Pages (from-to)2146-2159
Number of pages14
JournalRemote Sensing of Environment
Volume114
Issue number10
DOIs
StatePublished - Oct 1 2010

Fingerprint

moderate resolution imaging spectroradiometer
phenology
MODIS
Crops
soybean
Time series
time series analysis
maize
time series
soybeans
Imaging techniques
crop
corn
vegetation index
crops
agricultural statistics
Statistics
developmental stages
carbon sequestration
Mean square error

Keywords

  • Crop phenology
  • MODIS
  • Maize
  • Shape-model fitting
  • Soybean

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences

Cite this

Sakamoto, T., Wardlow, B. D., Gitelson, A. A., Verma, S. B., Suyker, A. E., & Arkebauer, T. J. (2010). A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data. Remote Sensing of Environment, 114(10), 2146-2159. https://doi.org/10.1016/j.rse.2010.04.019

A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data. / Sakamoto, Toshihiro; Wardlow, Brian D.; Gitelson, Anatoly A; Verma, Shashi B.; Suyker, Andrew E.; Arkebauer, Timothy J.

In: Remote Sensing of Environment, Vol. 114, No. 10, 01.10.2010, p. 2146-2159.

Research output: Contribution to journalArticle

Sakamoto, T, Wardlow, BD, Gitelson, AA, Verma, SB, Suyker, AE & Arkebauer, TJ 2010, 'A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data', Remote Sensing of Environment, vol. 114, no. 10, pp. 2146-2159. https://doi.org/10.1016/j.rse.2010.04.019
Sakamoto, Toshihiro ; Wardlow, Brian D. ; Gitelson, Anatoly A ; Verma, Shashi B. ; Suyker, Andrew E. ; Arkebauer, Timothy J. / A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data. In: Remote Sensing of Environment. 2010 ; Vol. 114, No. 10. pp. 2146-2159.
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