Cross-scale sensing of field-level crop residue cover: Integrating field photos, airborne hyperspectral imaging, and satellite data

Wang, Sheng; Guan, Kaiyu; Zhang, Chenhui; Zhou, Qu; Wang, Sibo; Wu, Xiaocui; Peng, Bin; Mei, Weiye; Li, Kaiyuan; Li, Ziyi; Yang, Yi; Zhou, Wang; Huang, Yizhi; Ma, Zewei

doi:10.1016/j.rse.2022.113366

Cited by 28 publications

(13 citation statements)

References 46 publications

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“…To advance the Tier 2 approach, prioritizing the inclusion of residue and floodwater management data is crucial. Tillage and burning data could help identify low and high residue areas for the following growing season (McCarty et al 2007, Hively et al 2018, Wang et al 2023b. Additionally, efforts to identify production areas and optimize irrigation management during the growing season would enhance the application of the Tier 2 approach at the field scale (Huang et al 2021, Liang et al 2021).…”

Section: Driving Factors For Regional Methane Emissions and Mitigatio...mentioning

confidence: 99%

Assessing the methane mitigation potential of innovative management in US rice production

Reavis,

Reba,

Shults

et al. 2023

Environ. Res. Lett.

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Rice is an important global crop while also contributing significant anthropogenic methane (CH4) emissions. To support the future of rice production, more information is needed on the impacts of sustainability-driven management used to grow rice with lower associated methane emissions. Recent support for the impacts of different growing practices in the US has prompted the application of a regional methodology (Tier 2) to estimate methane emissions in different rice growing regions. The methodology estimates rice methane emissions from the US Mid-South (MdS) and California (Cal) using region-specific scaling factors applied to a region-specific baseline flux. In our study, we leverage land cover data and soil clay content to estimate methane emissions using this approach, while also examining how changes in common production practices can affect overall emissions in the US. Our results indicated US rice cultivation produced between 0.32 and 0.45 Tg CH4 annually, which were approximately 7% and 42% lower on average compared to Food and Agriculture Organization of the UN (FAO) and US Environmental Protection Agency (EPA) inventories, respectively. Our estimates were 63% greater on average compared to similar methods that lack regional context. Introducing aeration events into irrigation resulted in the greatest methane reductions across both regions. When accounting for differences between baseline and reduction scenarios, the US MdS typically had higher mitigation potential compared to Cal. The differences in cumulative mitigation potential across the 2008–2020 period were likely driven by lower production area clay content for the US MdS compared to Cal. The added spatial representation in the Tier 2 approach is useful in surveying how impactful methane-reducing practices might be within and across regions.

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Section: Driving Factors For Regional Methane Emissions and Mitigatio...mentioning

confidence: 99%

Assessing the methane mitigation potential of innovative management in US rice production

Reavis,

Reba,

Shults

et al. 2023

Environ. Res. Lett.

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“…Crop residue plays a crucial role in mitigating soil erosion caused by wind and runoff [1], retains moisture and nutrients in the soil [2], and increases the organic content of the soil [3,4]. Additionally, crop residue improves water use efficiency, enhances soil fertility [5], and ultimately contributes to overall soil quality [6].…”

Section: Introductionmentioning

confidence: 99%

“…Stern et al [10] evaluated five spectral indices and classification methods using Landsat 5TM, 7ETM+, and 8 OLI data; their finding shows that no single approach is consistently superior for CRC estimation. Cai et al [21] identified the Normalized Difference Tillage Index (NDTI) as particularly effective, while Hively et al [9] noted higher accuracy with indices like the Shortwave Infrared Normalized Difference Residue Index (SINDRI) and the Lignin Cellulose Absorption Index (LCA) in predicting CRC, emphasizing the importance of the Shortwave Infrared (SWIR) spectrum, particularly within the 2100-2300 nm range [2,3,16,19,20,22]. Despite progress, the limited availability of free hyperspectral data and their low signal-to-noise ratio poses challenges [13,18,23].…”

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confidence: 99%

Automated Crop Residue Estimation via Unsupervised Techniques Using High-Resolution UAS RGB Imagery

Azimi,

Jung

2024

Remote Sensing

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Crop Residue Cover (CRC) is crucial for enhancing soil quality and mitigating erosion in agricultural fields. Accurately estimating CRC in near real-time presents challenges due to the limitations of traditional and remote sensing methods. This study addresses the challenge of accurately estimating CRC using unsupervised algorithms on high-resolution Unmanned Aerial System (UAS) imagery. We employ two methods to perform CRC estimation: (1) K-means unsupervised algorithm and (2) Principal Component Analysis (PCA) along with the Otsu thresholding technique. The advantages of these methods lie in their independence from human intervention for any supervised training stage. Additionally, these methods are rapid and suitable for near real-time estimation of CRC as a decision-making support in agricultural management. Our analysis reveals that the K-means method, with an R2=0.79, achieves superior accuracy in CRC estimation over the PCA-Otsu method with an R2=0.46. The accuracy of CRC estimation for both corn and soybean crops is significantly higher in winter than in spring, attributable to the more weathered state of crop residue. Furthermore, CRC estimations in corn fields exhibit a stronger correlation, likely due to the larger size of corn residue which enhances detectability in images. Nevertheless, the variance in CRC estimation accuracy between corn and soybean fields is minimal. Furthermore, CRC estimation achieves the highest correlation in no-till fields, while the lowest correlation is observed in conventionally tilled fields, a difference likely due to the soil disturbance during plowing in conventional tillage practices.

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“…Superpixel clusters image regions through grouping pixels; comparing the pixel, it provides image data in a more natural representation [11][12][13]. Learning-based superpixel segmentation method can effectively alleviate edge blur and classification noise [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Very High Resolution Images and Superpixel-Enhanced Deep Neural Forest Promote Urban Tree Canopy Detection

et al. 2023

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Urban tree canopy (UTC) area is an important index for evaluating the urban ecological environment; the very high resolution (VHR) images are essential for improving urban tree canopy survey efficiency. However, the traditional image classification methods often show low robustness when extracting complex objects from VHR images, with insufficient feature learning, object edge blur and noise. Our objective was to develop a repeatable method—superpixel-enhanced deep neural forests (SDNF)—to detect the UTC distribution from VHR images. Eight data expansion methods was used to construct the UTC training sample sets, four sample size gradients were set to test the optimal sample size selection of SDNF method, and the best training times with the shortest model convergence and time-consumption was selected. The accuracy performance of SDNF was tested by three indexes: F1 score (F1), intersection over union (IoU) and overall accuracy (OA). To compare the detection accuracy of SDNF, the random forest (RF) was used to conduct a control experiment with synchronization. Compared with the RF model, SDNF always performed better in OA under the same training sample size. SDNF had more epoch times than RF, converged at the 200 and 160 epoch, respectively. When SDNF and RF are kept in a convergence state, the training accuracy is 95.16% and 83.16%, and the verification accuracy is 94.87% and 87.73%, respectively. The OA of SDNF improved 10.00%, reaching 89.00% compared with the RF model. This study proves the effectiveness of SDNF in UTC detection based on VHR images. It can provide a more accurate solution for UTC detection in urban environmental monitoring, urban forest resource survey, and national forest city assessment.

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Cross-scale sensing of field-level crop residue cover: Integrating field photos, airborne hyperspectral imaging, and satellite data

Cited by 28 publications

References 46 publications

Assessing the methane mitigation potential of innovative management in US rice production

Assessing the methane mitigation potential of innovative management in US rice production

Automated Crop Residue Estimation via Unsupervised Techniques Using High-Resolution UAS RGB Imagery

Very High Resolution Images and Superpixel-Enhanced Deep Neural Forest Promote Urban Tree Canopy Detection

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