A shape-attention Pivot-Net for identifying central pivot irrigation systems from satellite images using a cloud computing platform: an application in the contiguous US

ABSTRACT

Forty percent of global food production relies upon irrigation, which accounts for 70% of total global freshwater use. Thus, the mapping of cropland irrigation plays a significant role in agricultural water management and estimating food production. However, current spaceborne irrigated cropland mapping is highly reliant upon its spectral behavior, which often has high uncertainty and lacks information about the method of irrigation. Deep learning (DL) allows for the classification of irrigated cropland according to unique spatial patterns, such as the central pivot irrigation system (CPIS). But convolutional neural networks (CNNs) are usually biased toward color and texture features, a spatial transferable and accurate CPIS identification model is lacking owing to previous model seldom involves the round shapes of CPIS, which is usually key to distinguishing CPIS. To address this lack, we proposed a shape attention neural network by integrating spatial-attention gate, residual block, and multi-task learning, Pivot-Net, to incorporate shape information identify CPIS in satellite imagery. Specifically, we employed CPIS in Kansas to train our model using Sentinel-2 and Landsat-8 optical images. We found that Pivot-Net is superior to seven state-of-the-art semantic segmentation models on second-stage validation. We also evaluated the performance of Pivot-Net at three validation sites, which had an average F₁-score and mean IOU of 90.68% and 90.45%, respectively, which further demonstrated the high accuracy of the proposed model. Moreover, to show that the proposed Pivot-Net can map CPIS at the country scale, we generated the first CPIS map at 30 m for the contiguous US using a cloud computing platform and our Pivot-Net model. The total CPIS area for the contiguous US was 61,094 km² in 2018, which comprised 26.22% of all irrigated lands. Our results can be accessed at https://tianfyou.users.earthengine.app/view/cpisus. Therefore, the proposed shape-attention Pivot-Net demonstrates the ability to classify CPIS at large spatial scales and are feasible to map CPIS at national scales.

KEYWORDS:

• Shape-attention
• multi-task learning
• irrigation mapping
• central pivot irrigation system
• cloud computing

Acknowledgment

This research was financially supported by the National Key Research and Development Project of China (No.2019YFE0126900) and the National Natural Science Foundation of China (41861144019, 42071271), the Strategic Priority Research Program of Chinese Academy of Sciences (XDA19030201).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability

The result could be accessed at https://tianfyou.users.earthengine.app/view/cpisus. The data that support the findings of this study are available from the corresponding author, Bingfang Wu, upon reasonable request.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15481603.2023.2165256