Estimating fractional coverage of crop, crop residue, and bare soil using shortwave infrared angle index and Sentinel-2 MSI

Yue, Jibo; Fu, Yuanyuan; Guo, Wei; Feng, Haikuan; Qiao, Hui

doi:10.1080/01431161.2022.2032454

International Journal of Remote Sensing

2022

DOI: 10.1080/01431161.2022.2032454

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Estimating fractional coverage of crop, crop residue, and bare soil using shortwave infrared angle index and Sentinel-2 MSI

Jibo Yue

Yuanyuan Fu

Wei Guo

et al.

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Cited by 9 publications

(1 citation statement)

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“…Upadhyay et al [31] utilized high-resolution UAS-borne RGB imagery and identified Recursive Feature Elimination with SVM (RFE-SVM) as the best feature selection method, pinpointing texture features, especially Local Binary Pattern (LBP) features, as most effective, while deeming shape features irrelevant. Despite these advancements, the primary limitation of supervised ML models lies in their dependency on extensive ground measurements for accurate model calibration [32]. This dependence not only demands substantial human effort but also introduces challenges in applying these models universally across varied agricultural landscapes, where soil types, moisture levels, and crop varieties differ markedly.…”

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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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confidence: 99%

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

Azimi,

Jung

2024

Remote Sensing

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Satellite-based assessment of hailstorm-affected potato crop for insurance purpose

et al. 2022

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Assessing the extent of hailstorm affected crop is one of the thrust areas for quantifying mid-season adversaries under crop insurance values chain. This study evaluated the preand post-hailstorm responses on spectral bands and vegetation indices derived from Sentinel-2 data for assessing the severity class of the affected potato crop. The potato crop was mapped using pre-event satellite data with overall accuracy of 88% (k=0.82).Pair-wise Games-Howell t-test showed significant differences among the post-hailstorm potato severity classes in Red, Near Infrared & Short-wave Infra-red (SWIR) bands and Normalized vegetation indices. Percentage change (from pre-to post-event) in band reflectance and vegetation indices showed a better sensitivity in differentiating damage severity. Differential behaviour of SWIR-1 (Band-11) and SWIR-2 (Band-12) were observed within severely affected potato crop under dry and wet soil conditions. Decision matrix based on percentage change in Normalized difference Vegetation Index (DNDVI)and Normalized difference Tillage Index (DNDVI) could able to capture the damage severity classes with an overall accuracy of 86.7%. Higher proportion of affected area were found to be associated with larger percentage of Potato yield reduction based on measured yield data at Insurance unit level. The proposed methodology could be adopted for operational assessment of the impact of hailstorm events on crops.

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Spaceborne imaging spectroscopy enables carbon trait estimation in cover crop and cash crop residues

Jennewein,

Hively,

Lamb

et al. 2024

Precision Agric

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Purpose Cover crops and reduced tillage are two key climate smart agricultural practices that can provide agroecosystem services including improved soil health, increased soil carbon sequestration, and reduced fertilizer needs. Crop residue carbon traits (i.e., lignin, holocellulose, non-structural carbohydrates) and nitrogen concentrations largely mediate decomposition rates and amount of plant-available nitrogen accessible to cash crops and determine soil carbon residence time. Non-destructive approaches to quantify these important traits are possible using spectroscopy. Methods The objective of this study was to evaluate the efficacy of spectroscopy instruments to quantify crop residue biochemical traits in cover crop agriculture systems using partial least squares regression models and a combination of (1) the band equivalent reflectance (BER) of the PRecursore IperSpettrale della Missione Applicativa (PRISMA) imaging spectroscopy sensor derived from laboratory collected Analytical Spectral Devices (ASD) spectra (n = 296) of 11 cover crop species and three cash crop species, and (2) spaceborne PRISMA imagery that coincided with destructive crop residue collections in the spring of 2022 (n = 65). Spectral range was constrained to 1200 to 2400 nm to reduce the likelihood of confounding relationships in wavelengths sensitive to plant pigments or those related to canopy structure for both analytical approaches. Results Models using laboratory BER of PRISMA all demonstrated high accuracies and low errors for estimation of nitrogen and carbon traits (adj. R2 = 0.86 − 0.98; RMSE = 0.24 − 4.25%) and results indicate that a single model may be used for a given trait across all species. Models using spaceborne imaging spectroscopy demonstrated that crop residue carbon traits can be successfully estimated using PRISMA imagery (adj. R2 = 0.65 − 0.75; RMSE = 2.71 − 4.16%). We found moderate relationships between nitrogen concentration and PRISMA imagery (adj. R2 = 0.52; RMSE = 0.25%), which is partly related to the range of nitrogen in these senesced crop residues (0.38–1.85%). PRISMA imagery models were also influenced by atmospheric absorption, variability in surface moisture content, and some presence of green vegetation. Conclusion As spaceborne imaging spectroscopy data become more widely available from upcoming missions, crop residue trait estimates could be regularly generated and integrated into decision support tools to calculate decomposition rates and associated nitrogen credits to inform precision field management, as well as to enable measurement, monitoring, reporting, and verification of net carbon benefits from climate smart agricultural practice adoption in an emerging carbon marketplace.

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Estimating fractional coverage of crop, crop residue, and bare soil using shortwave infrared angle index and Sentinel-2 MSI

Cited by 9 publications

References 47 publications

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

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

Satellite-based assessment of hailstorm-affected potato crop for insurance purpose

Spaceborne imaging spectroscopy enables carbon trait estimation in cover crop and cash crop residues

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