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.

中文翻译：

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通过地面和 VENµS 卫星估算鹰嘴豆叶水势

摘要

鹰嘴豆 ( Cicer arietinum L.) 是世界各地种植的主要谷物豆类，作为主要蛋白质来源。传统上，它是一种靠雨水浇灌的作物，但补充灌溉可以提高产量并应对全球气候变化带来的挑战。快速且非破坏性的植物水分状况评估方法可以简化灌溉管理。本研究的主要目的是远程评估田间种植鹰嘴豆的叶水势（LWP）和叶面积指数（LAI）。在两个农场试验和两个商业田地中应用了五种灌溉处理。在整个研究过程中获取的新型微型卫星 (VENμS) 图像上进行地面高光谱冠层反射率以及植被和环境监测。同时，在现场捕获了 LWP 和 LAI 测量结果。基于所有光谱带的植被指数（VI）和机器学习（ML）用于校准和验证光谱估计模型。当前研究中选择的使用 1600 和 1730 nm 波段的归一化差谱指数 (NDSI) (NDSI _(1600,1730) ) 在使用线性回归的独立验证 (RMSE = 0.19 [MPa]) 上产生了 LWP 最低估计误差。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 导致 LWP 估计精度相对较低（RMSE = 0.23–0.29 [MPa]）。基于地面和空间光谱数据的 LWP 人工神经网络 (ANN) 模型表现出相似的性能 (RMSE = 0.15–0.17 [MPa])，并且都比 VI 更准确。LWP 对灌溉处理的响应比 LAI 响应更快，并被 NDSI _(1600,1730)捕获。LWP 和 LAI 之间的低相关性（r  = 0.08-0.44）支持这样的结论：鹰嘴豆冠层的光谱反射率可用于估计 LWP 本身，并且仅部分受到灌溉处理和冠层发育引起的形态变化的影响。快速估计鹰嘴豆 LWP 的能力可能会改善未来的灌溉安排。