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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #389151
Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Evaluating the robustness of NISAR’s cropland product to time of observation, observing mode and dithering

Author
item KRAATZ, S. - University Of Massachusetts, Amherst
item SIQUEIRA, P. - University Of Massachusetts, Amherst
item KELLNDORFER, J. - Collaborator
item TORBICK, N. - Applied Geosolutions, Llc
item HUANG, X. - Applied Geosolutions, Llc
item Cosh, Michael

Submitted to: Earth and Space Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/28/2022
Publication Date: 6/9/2022
Citation: Kraatz, S., Siqueira, P., Kellndorfer, J., Torbick, N., Huang, X., Cosh, M.H. 2022. Evaluating the robustness of NISAR’s cropland product to time of observation, observing mode and dithering. Earth and Space Science. 8(1). Article e2020EA001363. https://doi.org/10.1029/2022EA002366.
DOI: https://doi.org/10.1029/2022EA002366

Interpretive Summary: Understanding cropping status across the globe is critical to understanding food security issues internationally. But the monitoring of cropping status is restricted by borders and human resources. Radar remote sensing provides a new opportunity to efficiently and accurately assess the amount of acreage in crop and also, potentially identifies the crop type. Using field and aircraft data collected to support future radar missions, an assessment is made of the capabilities of future cropland algorithms. Satellites on repeated ground tracks can provide both AM and PM imagery for a given location and it was determined that cropland algorithms were most effective with combining this imagery in the analysis, versus using only one or the other set of imagery. This information will be utilized by satellite mission managers when they are evaluating methods for orbit deployment and operational cropland mapping.

Technical Abstract: Cropland mapping is important for monitoring agricultural practices, cropland distribution and for supporting food security programs. Radar remote sensing will likely provide a means of cropland mapping which can be efficient, accurate, globally applied. But the algorithms used for radar cropland analysis are still underdevelopment and it is necessary to conduct ground and aircraft based campaigns to build the knowledge base, prior to a satellite being launched. The upcoming NASA ISRO SAR (NISAR) mission (2023) will map croplands at 1 ha spatial resolution using a coefficient of variation approach. NASA Jet Propulsion Laboratory (JPL) recently made several simulated NISAR data over croplands available – with a few sites having both morning (AM), evening (PM), and dithered data available. These data allow for making a priori estimates of how NISAR’s cropland science algorithm is impacted by factors such as orbit (i.e., the incidence angle, time of day), bandwidth (i.e., whether the 5, 20 or 40 MHz channel is used), and varying the systems Pulse Repetition Frequency (PRF) in a process known as dithering. This study investigates these questions over agricultural regions in Mississippi and Arkansas. Results show that compositing data from both orbits (AM+PM) tended to improve crop/non-crop classifications (a less than 0.5% reduction in accuracy) compared to using one of AM or PM data; at two of the three locations studied AM+PM yielded the best results. The study also found that dithering only had slightly decreased cropland mapping accuracy ((<0.3% accuracy reduction).