Chickpea leaf water potential estimation from ground and VENµS satellite

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Precision Agriculture Pub Date : 2024-03-02 DOI:10.1007/s11119-024-10129-w

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Abstract

Chickpea (Cicer arietinum L.) is a major grain legume grown worldwide as a staple protein source. Traditionally, it is a rain-fed crop, but supplemental irrigation can increase yields and counteract the challenges posed by the changing climate worldwide. A fast and non-destructive plant water status assessment method may streamline irrigation management. The main objective of this study was to remotely assess the leaf water potential (LWP) and leaf area index (LAI) of field-grown chickpea. Five irrigation treatments were applied in two farm experiments and two commercial fields. Ground hyperspectral canopy reflectance and Vegetation and Environment monitoring on a New Micro-Satellite (VENµS) images acquired throughout the study. In parallel, LWP and LAI measurements were captured in the field. Vegetation indices (VIs) and machine learning (ML) based on all spectral bands were used to calibrate and validate spectral estimation models. The normalized difference spectral index (NDSI) that used bands on 1600 and 1730 nm (NDSI_(1600,1730)) selected in the current study yielded the LWP lowest estimation error on independent validation (RMSE = 0.19 [MPa]) using linear regression. VENµS based VIs resulted in relatively lower LWP estimation accuracy (RMSE = 0.23–0.29 [MPa]) compared to VIs calculated from ground hyperspectral data (RMSE = 0.19–0.21 [MPa]). Artificial neural network (ANN) models for LWP from ground and space spectral data showed similar performances (RMSE = 0.15–0.17 [MPa]), and were both more accurate than VIs. LWP response to the irrigation treatments was faster than the LAI response and was captured by the NDSI_(1600,1730). The low correlation found between LWP and LAI (r = 0.08–0.44) supports the conclusion that spectral reflectance of chickpea canopy can be used to estimate LWP per se and is only partially affected by morphological changes induced by irrigation treatments and canopy development. The ability to rapidly estimate chickpea LWP may improve irrigation scheduling in the future.

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从地面和 VENµS 卫星估算鹰嘴豆叶片水势

摘要鹰嘴豆（Cicer arietinum L.）是一种主要的谷物豆类，作为主食蛋白质来源在世界各地种植。传统上，鹰嘴豆是雨水灌溉作物，但补充灌溉可以提高产量，应对全球气候变化带来的挑战。快速、无损的植物水分状况评估方法可简化灌溉管理。本研究的主要目的是远程评估田间种植鹰嘴豆的叶片水势（LWP）和叶面积指数（LAI）。在两块农场试验田和两块商业田中采用了五种灌溉处理方法。在整个研究过程中采集了地面高光谱冠层反射率和新型微卫星植被与环境监测（VENµS）图像。同时，还在田间采集了 LWP 和 LAI 测量值。基于所有光谱波段的植被指数（VIs）和机器学习（ML）被用于校准和验证光谱估算模型。本研究选择了使用 1600 和 1730 纳米波段的归一化差异光谱指数（NDSI）（NDSI(1600,1730)），使用线性回归法进行独立验证，其 LWP 估算误差最小（RMSE = 0.19 [MPa]）。与根据地面高光谱数据计算的VI（RMSE = 0.19-0.21 [MPa]）相比，基于VENµS的VI的LWP估算精度相对较低（RMSE = 0.23-0.29 [MPa]）。根据地面和空间光谱数据建立的 LWP 人工神经网络（ANN）模型显示出相似的性能（RMSE = 0.15-0.17 [MPa]），并且都比 VIs 更准确。LWP 对灌溉处理的响应速度快于 LAI 响应速度，并被 NDSI（1600,1730）所捕捉。LWP 与 LAI 之间的相关性较低（r = 0.08-0.44），这证明鹰嘴豆冠层的光谱反射率本身可用于估算 LWP，仅部分受到灌溉处理和冠层发育引起的形态变化的影响。快速估算鹰嘴豆 LWP 的能力可改善未来的灌溉调度。

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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.