Version 1
: Received: 7 September 2023 / Approved: 14 September 2023 / Online: 15 September 2023 (05:40:19 CEST)
Version 2
: Received: 11 October 2024 / Approved: 13 October 2024 / Online: 14 October 2024 (11:18:07 CEST)
How to cite:
Noorazar, H.; Brady, M.; Savalkar, S.; Norouzi Kandelati, A.; 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.v2
Noorazar, H.; Brady, M.; Savalkar, S.; Norouzi Kandelati, A.; 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.v2
Noorazar, H.; Brady, M.; Savalkar, S.; Norouzi Kandelati, A.; 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.v2
APA Style
Noorazar, H., Brady, M., Savalkar, S., Norouzi Kandelati, A., Liu, M., Beale, P., McGuire, A., Waters, T., & Rajagopalan, K. (2024). Identifying Double-cropped Fields with Remote Sensing in Areas with High Crop Diversity. Preprints. https://doi.org/10.20944/preprints202309.1040.v2
Chicago/Turabian Style
Noorazar, H., Timothy Waters and Kirti Rajagopalan. 2024 "Identifying Double-cropped Fields with Remote Sensing in Areas with High Crop Diversity" Preprints. https://doi.org/10.20944/preprints202309.1040.v2
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 simpler rule-based thresholds on vegetation indices---do not work well in regions with a high crop diversity, and likely significantly overestimate double-cropped extent. Multiple machine learning models were able to perform better by capturing nuances that the simple rule-based approaches are unable to. In particular, our (image-based) deep learning model was able to capture nuances in this crop-diverse environment and achieve a high accuracy (96-99% overall accuracy and 83– 93% producer accuracy for the double-cropped class with standard error of less than 2.5%) while also identifying double-cropping in the right crop types and locations based on expert knowledge. 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.
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