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

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Precision Agriculture Pub Date : 2024-06-27 DOI:10.1007/s11119-024-10159-4

Jyoti S. Jennewein, W. Hively, Brian T. Lamb, Craig S. T. Daughtry, Resham Thapa, Alison Thieme, Chris Reberg-Horton, Steven Mirsky

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Abstract

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.

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利用空间成像光谱学估算覆盖作物和经济作物残留物的碳性状

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，可提供农业生态系统服务，包括改善土壤健康、增加土壤固碳和减少肥料需求。作物残留物的碳特征（即木质素、全纤维素、非结构性碳水化合物）和氮浓度在很大程度上介导着分解率和经济作物可利用的植物氮量，并决定着土壤碳的停留时间。可以利用光谱学的非破坏性方法来量化这些重要特征。方法本研究的目的是评估光谱仪器的功效，以利用偏最小二乘回归模型和以下两种方法的组合来量化覆盖作物农业系统中作物残留物的生化性状：（1）PRecursore IperSpettrale della Missione Applicativa（PRISMA）成像光谱传感器的波段等效反射率（BER），该传感器来自实验室收集的 11 种覆盖作物和 3 种经济作物的分析光谱设备（ASD）光谱（n = 296）；以及（2）空间PRecursore IperSpettrale della Missione Applicativa（PRISMA）成像光谱传感器的波段等效反射率（BER）、(2) 与 2022 年春季破坏性作物残留物采集相吻合的星载 PRISMA 图像（n = 65）。结果使用 PRISMA 实验室 BER 的模型在估算氮和碳性状时都表现出了高精确度和低误差（adj. R2 = 0.86 - 0.98; RMSE = 0.24 - 4.25%），结果表明一个单一模型可用于所有物种的特定性状。利用空间成像光谱学建立的模型表明，可以利用 PRISMA 图像成功估算作物残留碳性状（adj. R2 = 0.65 - 0.75; RMSE = 2.71 - 4.16%）。我们发现氮浓度与 PRISMA 图像之间的关系适中（adj. R2 = 0.52；RMSE = 0.25%），这部分与这些衰老作物残留物中的氮含量范围（0.38-1.85%）有关。结论随着空间成像光谱数据在即将到来的飞行任务中越来越广泛地使用，作物残留物性状估算可以定期生成并集成到决策支持工具中，以计算分解率和相关的氮信用额度，为精确的田间管理提供信息，并在新兴的碳市场中测量、监测、报告和验证气候智能农业实践所带来的净碳效益。

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来源期刊

Precision Agriculture 农林科学-农业综合

CiteScore

12.30

自引率

8.10%

发文量

103

审稿时长

>24 weeks

期刊介绍： Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.