Version 1
: Received: 7 September 2023 / Approved: 14 September 2023 / Online: 15 September 2023 (05:40:19 CEST)
How to cite:
Noorazar, H.; Brady, M.; Savalkar, S.; Liu, M.; Beale, P.; McGuire, A.; Waters, T.; Rajagopalan, K. Identifying Double-cropped Fields with Remote Sensing in Areas with High Crop Diversity. Preprints2023, 2023091040. https://doi.org/10.20944/preprints202309.1040.v1
Noorazar, H.; Brady, M.; Savalkar, S.; Liu, M.; Beale, P.; McGuire, A.; Waters, T.; Rajagopalan, K. Identifying Double-cropped Fields with Remote Sensing in Areas with High Crop Diversity. Preprints 2023, 2023091040. https://doi.org/10.20944/preprints202309.1040.v1
Noorazar, H.; Brady, M.; Savalkar, S.; Liu, M.; Beale, P.; McGuire, A.; Waters, T.; Rajagopalan, K. Identifying Double-cropped Fields with Remote Sensing in Areas with High Crop Diversity. Preprints2023, 2023091040. https://doi.org/10.20944/preprints202309.1040.v1
APA Style
Noorazar, H., Brady, M., Savalkar, S., Liu, M., Beale, P., McGuire, A., Waters, T., & Rajagopalan, K. (2023). Identifying Double-cropped Fields with Remote Sensing in Areas with High Crop Diversity. Preprints. https://doi.org/10.20944/preprints202309.1040.v1
Chicago/Turabian Style
Noorazar, H., Timothy Waters and Kirti Rajagopalan. 2023 "Identifying Double-cropped Fields with Remote Sensing in Areas with High Crop Diversity" Preprints. https://doi.org/10.20944/preprints202309.1040.v1
Abstract
The extent of single and multi-cropping systems in any region, and potential changes to it, have consequences on food and resource use raising important policy questions. However, addressing these questions is limited by a lack of reliable data on multi-cropping practices at a high spatial resolution, especially in areas with high crop diversity. In this paper, we describe a relatively low-cost and scalable method to identify double cropping at the field-scale using satellite (Landsat) imagery. The process combines machine learning methods with expert labeling. We demonstrate the process by measuring double cropping extent in a portion of Washington State in the Pacific Northwest United States--- a region with significant production of more than 60 distinct types of crops including hay, fruits, vegetables, and grains in irrigated settings. Our results indicate that the current state-of-the-art methods for identifying cropping intensity---that apply rule-based thresholds on vegetation indices---do not work well in regions with high-crop-diversity. Our deep learning model was able to capture the diverse nuances and achieve a high accuracy (99\% overall accuracy and 0.92 Kappa coefficient). Our expert labeling process worked well and has potential as a relatively low-cost, scalable approach for remote sensing applications. The product developed here is valuable to inform several policy questions related to food production and resource use.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
