多年生草净初级产量遥感的Carnegie-Ames-Stanford方法验证

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment Pub Date : 2025-06-06 DOI:10.1016/j.rse.2025.114857

Shaohui Zhang , Poul Erik Lærke , Mathias Neumann Andersen , Junxiang Peng , Esben Øster Mortensen , Johannes Wilhelmus Maria Pullens , Sheng Wang , Klaus Steenberg Larsen , Davide Cammarano , Uffe Jørgensen , Kiril Manevski

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引用次数: 0

摘要

在最佳生长条件下，净初级生产力（NPP）是截留光合有效辐射（Ipar）和最大辐射利用效率(RUEmax；Ipar转化为生物质)。本研究的目的是改进和验证基于ruemax的卡耐基-阿姆斯-斯坦福方法（CASA），在考虑环境约束的情况下，利用野外（手持传感器）和空中（无人机）尺度采集的冠层多光谱反射率来确定草地NPP。该分析基于对丹麦沙质壤土进行的多年田间试验、测量的茎部生物量和估算的根系生物量来计算NPP、长期气象数据以及通过CO2通量室测量估算的每日NPP来推导环境约束。CO2通量数据分析结果表明，第二季采收前的季中温度较高，NPP和植物呼吸均较高。最适宜草生物量生产的日最高气温为16.5℃。在考虑最高气温、蒸汽压亏缺、云量和水分胁迫等最佳环境约束条件时，与无约束条件相比，本研究建立的改进CASA模型在日尺度（nRMSE降低9%）和季节尺度（nRMSE降低8 - 34%）下均能准确地模拟NPP。最高气温和水分胁迫是影响草地RUEmax的主要环境因素。在考虑最佳环境约束条件后，黑麦草的季节RUEmax为1.9-2.7 g C MJ−1，草-豆科混合物的季节RUEmax为1.9-2.2 g C MJ−1，用于测量光谱反射率。在整个生长季节，高羊茅（3.1 g C MJ−1）和羊茅（2.9 g C MJ−1）的RUEmax高于多年生黑麦草（2.3 g C MJ−1）。本研究强调了利用最高温度和水分胁迫函数改进的CASA模型进行实时、基于遥感的草地生产力评估的实际意义。

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Validating the Carnegie-Ames-Stanford Approach for remote sensing of perennial grass net primary production

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Validating the Carnegie-Ames-Stanford Approach for remote sensing of perennial grass net primary production

Under optimal growth conditions, net primary productivity (NPP) is a product of intercepted photosynthetic active radiation (Ipar) and maximum radiation use efficiency (RUE_max; conversion of Ipar to biomass). The objective of this study was to improve and validate the RUE_max-based Carnegie-Ames-Stanford Approach (CASA) for the determination of grassland NPP by canopy multispectral reflectance collected at field (handheld sensor) and airborne (UAV) scale considering environmental constraints. The analysis was based on multi-year field experiments on sandy loam soil in Denmark, measured shoot and estimated root biomass to calculate NPP, long-term meteorological data, and daily NPP estimated from CO₂ flux chamber measurements for deriving environmental constraints.

The results derived from CO₂ flux data showed that NPP and plant respiration were higher in the middle of the season before the second harvest when temperature was also high. The daily maximum air temperature optimal for grass biomass production was 16.5 °C. The improved CASA model built in this study was accurate for modeling NPP at both daily (nRMSE decrease of 9 %) and seasonal (nRMSE decrease of 8–34 %) scales when considering the best environmental constraints such as maximum air temperature, vapor pressure deficit, cloudiness, and water stress, compared to no constraints. Maximum air temperature and water stress were the most important environmental constraints to the grass RUE_max. Seasonal RUE_max for modeling NPP after considering best environmental constraints was 1.9–2.7 g C MJ⁻¹ for ryegrass and 1.9–2.2 g C MJ⁻¹ for grass-legume mixture using the two remote sensors for measuring spectral reflectance. Over the whole growing season, tall fescue (3.1 g C MJ⁻¹) and festulolium (2.9 g C MJ⁻¹) obtained higher RUE_max than perennial ryegrass (2.3 g C MJ⁻¹).

This study highlights the practical implications of using the CASA model improved by maximum temperature and water stress functions for real-time, remote sensing-based assessments of grassland productivity.

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来源期刊

Remote Sensing of Environment 环境科学-成像科学与照相技术

CiteScore

25.10

自引率

8.90%

发文量

455

审稿时长

53 days

期刊介绍： Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.