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

he 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.

中文翻译：

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星载成像光谱仪能够估计覆盖作物和经济作物残留物的碳特征

 目的

覆盖作物和减少耕作是两种关键的气候智能型农业实践，可以提供农业生态系统服务，包括改善土壤健康、增加土壤碳固存和减少化肥需求。作物残茬碳性状（即木质素、全纤维素、非结构碳水化合物）和氮浓度在很大程度上调节分解速率和经济作物可利用的植物有效氮量，并决定土壤碳停留时间。使用光谱学可以采用非破坏性方法来量化这些重要特征。

 方法

本研究的目的是使用偏最小二乘回归模型和 (1) PRecursore IperSpettrale della Missione 的波段等效反射率 (BER) 的组合，评估光谱仪器量化覆盖作物农业系统中作物残留生化性状的功效Applicativa (PRISMA) 成像光谱传感器源自实验室收集的 11 种覆盖作物物种和 3 种经济作物物种的分析光谱设备 (ASD) 光谱 (n = 296)，以及 (2) 星载 PRISMA 图像，该图像与在2022 年春季（n = 65）。两种分析方法的光谱范围被限制在 1200 至 2400 nm，以减少对植物色素敏感的波长或与冠层结构相关的波长混淆关系的可能性。

 结果

使用 PRISMA 实验室 BER 的模型在氮和碳性状估计方面均表现出高准确度和低误差（adj. R ² = 0.86 − 0.98；RMSE = 0.24 − 4.25%），结果表明单一模型可用于所有物种的给定性状。使用星载成像光谱学的模型表明，可以使用 PRISMA 图像成功估计农作物残留物碳特征（调整 R ² = 0.65 − 0.75；RMSE = 2.71 − 4.16%）。我们发现氮浓度和 PRISMA 图像之间存在中等关系（adj. R ² = 0.52；RMSE = 0.25%），这部分与这些衰老作物残留物中的氮范围有关（0.38–1.85%） 。 PRISMA 图像模型还受到大气吸收、地表水分含量变化以及某些绿色植被存在的影响。

 结论

随着即将到来的任务中越来越广泛地获得星载成像光谱数据，可以定期生成作物残留性状估计值并将其集成到决策支持工具中，以计算分解率和相关的氮信用额，为精确的田间管理提供信息，并能够进行测量、监测、报告和验证新兴碳市场中采用气候智能型农业实践所带来的净碳效益。