Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery

Abstract

The ability to regionally monitor crop progress and condition through the growing season benefits both crop management and yield estimation. In the United States, these metrics are reported weekly at state or district (multiple counties) levels by the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) using field observations provided by trained local reporters. However, the ground data collection process supporting this effort is time consuming and subjective. Furthermore, operational crop management and yield estimation efforts require information with more granularity than at the state or district level. This paper evaluates remote sensing approaches for mapping crop phenology using vegetation index time-series generated by fusing Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) surface reflectance imagery to improve temporal sampling over that provided by Landsat alone. The case study focuses on an agricultural region in central Iowa from 2001 to 2014. Our objectives are 1) to assess Landsat-MODIS data fusion results over cropland; 2) to map crop phenology at 30m resolution using fused surface reflectance data; and 3) to identify the relationships between remotely sensed crop phenology metrics and the crop progress stages reported by NASS. The results show that detailed spatial and

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

“…Recently, there has been an increasing demand for delivering information on the spatial distribution and dynamics of different crop types as early as possible, as in-season the crop maps are curtailed when taken as input to crop area forecasting, hazard prediction, or water use calculations [6]. However, high accuracy and early identifications of crop distribution across an entire growing period is challenging [7,8]. Since traditional agricultural statistics on crop acreages are usually provided by the end of the season or later, in-season agricultural production managers lack necessary information about the current year's crops [9,10].…”

Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) of the 30-m Reflective Wavelength Bands to Sentinel-2 20-m Resolution

“…It is sometimes termed data fusion, or more specifically, pansharpening, when the higher resolution ancillary data is the panchromatic band [35][36][37][38][39]. Hereafter, for consistency, the term downscaling is used throughout this paper.…”

Modeling the Effects of the Urban Built-Up Environment on Plant Phenology Using Fused Satellite Data

“…Version 1.2.1 improves computational efficiency by allowing predictions for multiple dates with one run when the pair image is the same. It can also run in parallel computing mode [30]. STARFM can fill small gaps or clouds in the prediction MODIS image using information from neighbor pixels if Landsat pixels are valid.…”

“…The Landsat and MODIS pair images may be gap-filled first before data fusion to increase valid prediction. Different weights from original and the fused data sources may be considered similar to the strategy used in mapping crop phenology [30].…”

“…STARFM is, as of 2013, widely used [27]. Originally shown to capture phenology changes for forested or cropland areas, as well as a mixture of cropland, evergreen, and deciduous forest [28], it has since been applied to observe, for example, community level phenology [29], crop growth stages [30], urban phenomena such as the land surface temperature (LST) of the urban heat island [31], and the Normalized Difference Vegetation Index (NDVI) values of a semi-arid rangeland of northern Utah [32].…”