Assessing the sensitive spectral bands for soybean water status monitoring and soil moisture prediction using leaf-based hyperspectral reflectance

文献类型： 外文期刊

第一作者： Teixeira Crusiol, Luis Guilherme

作者： Teixeira Crusiol, Luis Guilherme;Ribeiro Sibaldelli, Rubson Natal;Goncalves, Sergio Luiz;Simonetto Foloni, Jose Salvador;Mertz-Henning, Liliane Marcia;Nepomuceno, Alexandre Lima;Neumaier, Norman;Boucas Farias, Jose Renato;Nanni, Marcos Rafael;Furlanetto, Renato Herrig;Sun, Liang

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关键词： Drought; Leaf water content; Yield; Principal component analysis; Partial least squares regression

期刊名称：AGRICULTURAL WATER MANAGEMENT （ 影响因子：6.611； 五年影响因子：6.574 ）

ISSN： 0378-3774

年卷期： 2023 年 277 卷

页码：

收录情况： SCI

摘要： The stability of soybean yields in Brazil is regularly affected by drought periods, and soil management practices are crucial to expanding the water holding capacity of the soil and providing higher levels of moisture during critical periods, which contribute to increasing crop yields, relieving the need for non-agricultural areas to be converted into croplands. The research reported herein aimed to quantitatively monitor the soil moisture of a soybean crop through leaf-based hyperspectral reflectance and suggest a remote sensing-based approach that might assist in identifying soil management zones. A field experiment at the Brazilian Agricultural Research Corporation during 2016/2017, 2017/2018, and 2018/2019 cropping seasons had ten soybean genotypes sub-jected to four water conditions: irrigated, non-irrigated, and water deficit induced at the vegetative or repro-ductive stages. The soil of the experimental site is characterized as Udox Oxisol. Leaf reflectance (400-2500 nm) was collected by the spectroradiometer FieldSpec 3 Jr simultaneously with soil moisture (0-20 and 20-40 cm depths) at eleven dates. Data submitted to Principal Component Analysis (PCA) evaluated the clustering of water conditions and which are the most critical spectral wavelengths to characterize the plant water status. The Partial Least Squares Regression (PLSR) was applied to develop a quantitative spectral model to predict soil moisture. The PCA explained over 93% of the spectral variance within each assessment day, and shortwave infrared wavelengths presented a higher contribution to water status clustering. At the cross-validation step, the PLSR presented R2 up to 0.860 and 0.906 (0-20 and 20-40 cm) underperforming when soil moisture showed no significant differences between water conditions. Using samples from all assessment days, PLSR presented R2 = 0.609 and 0.722 (0-20 and 20-40 cm) at the external validation step (RMSE = 2.7 and 1.9, respectively), with a soil moisture range equal to 16-35% and 20-35% at both depths, remarkably outperforming the traditional univariate spectral models. Our results contribute to soil moisture assessment in extensive soybean areas regardless of the stage of crop development and provide a significant contribution since the Brazilian soybean crop calendar might present differences of over 30 days within the same production region. Due to that, soybean plants are rarely at the same phenological stage on a given date in the season.

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