Article
Remote Sensing
Sihem Chaabouni, Abdelaziz Kallel, Rasmus Houborg
Summary: Optical radiative transfer models (RTM) are inverted using a Bayesian approach to estimate vegetation properties such as LAI and Cab, presenting a new multi-scale variational inversion approach for practical optimization. The approach is tested in a dryland agricultural system showing good agreement with in-situ measurements, demonstrating improvement over traditional variational techniques.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Agriculture, Multidisciplinary
Liang Wan, Yufei Liu, Yong He, Haiyan Cen
Summary: This study explores the use of prior knowledge and active learning to assess leaf chlorophyll content from multi-scale canopy reflectance. The results show that the hybrid method performs better when prior knowledge for model parameters is available, and the active learning method can select representative samples for model update. The proposed method obtains satisfactory estimations across different datasets, contributing to the improvement of hybrid modeling for multi-scale monitoring of leaf biochemical traits.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Environmental Sciences
Wenchao Tang, Rongxin Tang, Tao Guo, Jingbo Wei
Summary: Fast and accurate prediction of crop yield at the regional scale is significant for food policies or trade. This study develops a new model to predict the yield of oilseed rape using high-resolution remote sensing images. The model is derived through ground experiments and remote sensing data analysis. The proposed model combines ground leaf area index (LAI) measurements and remote sensing LAI predictions to accurately estimate oilseed rape yield using high-resolution remote sensing images.
Article
Environmental Sciences
Erekle Chakhvashvili, Bastian Siegmann, Onno Muller, Jochem Verrelst, Juliane Bendig, Thorsten Kraska, Uwe Rascher
Summary: This study explored the capabilities of using high-spatial-resolution multispectral unmanned aerial vehicle (UAV) data and a leaf-canopy radiative transfer model (RTM) to retrieve crop variables such as leaf chlorophyll content, leaf area index, and canopy chlorophyll content. The results showed promising accuracy in retrieving leaf chlorophyll content, but more challenges were encountered in retrieving leaf area index, especially for maize varieties with contrasting canopy geometry.
Article
Environmental Sciences
Qi Sun, Quanjun Jiao, Xidong Chen, Huimin Xing, Wenjiang Huang, Bing Zhang
Summary: This study investigates the use of the PROSAIL-D model in combination with machine learning algorithms to estimate the chlorophyll content and leaf area index of crops using simulated ALA(adj) data. The results show that the machine learning methods using ALA(adj) data can greatly enhance estimation accuracy, particularly for mixed crops. Therefore, this method has the potential to estimate canopy chlorophyll content and leaf area index of crops at the satellite scale.
Article
Environmental Sciences
Cihan Karaca, Rodney B. Thompson, M. Teresa Pena-Fleitas, Marisa Gallardo, Francisco M. Padilla
Summary: Using normalized indices to measure the nitrogen status of plant leaves does not improve yield estimation, while absolute measurements on lower leaves slightly improve yield estimation performance.
Article
Horticulture
Renan Tosin, Isabel Pocas, Helena Novo, Jorge Teixeira, Natacha Fontes, Antonio Graca, Mario Cunha
Summary: This study developed two models to estimate Psi(pd) in a commercial vineyard, utilizing spectral data and machine learning algorithms. The first model estimated Psi(pd) based on vine canopy reflectance and selected suitable vegetation indices, while the second model optimized variables for Psi(pd) estimation based on pigments' concentrations assessed through hyperspectral reflectance. The B-MARS algorithm produced the best results with a RRMSE between 13-14% in validation.
SCIENTIA HORTICULTURAE
(2021)
Article
Multidisciplinary Sciences
Sayantan Sarkar, Alexandre-Brice Cazenave, Joseph Oakes, David McCall, Wade Thomason, Lynn Abbott, Maria Balota
Summary: This study introduces novel models for rapid estimation of peanut leaf area index (LAI) and lateral growth (LG) using UAV-collected leaf reflectance data, and for predicting pod yield. The models are effective in identifying phenotypic differences among genotypes and can be used for selecting peanut varieties with desirable LAI, LG, and yield.
SCIENTIFIC REPORTS
(2021)
Article
Plant Sciences
Qiaomin Chen, Bangyou Zheng, Tong Chen, Scott C. Chapman
Summary: A conceptual framework integrating crop growth model and radiative transfer model was proposed to introduce biological constraints in a synthetic training dataset for improving estimation accuracy of crop traits. The results demonstrated the potential advantages of adding biological constraints and utilizing deep learning for simultaneously predicting multiple crop traits from synthetic datasets. The predictive models were further validated on real unmanned aerial vehicle-based multispectral images, confirming the effectiveness of the proposed framework.
JOURNAL OF EXPERIMENTAL BOTANY
(2022)
Article
Remote Sensing
Achraf Makhloufi, Abdelaziz Kallel, Rayda Chaker, Jean-Philippe Gastellu-Etchegorry
Summary: This study aims to estimate and monitor the biophysical properties of olive trees in super-intensive groves using innovative forward/backward radiative transfer modeling. The model utilizes the DART model to simulate realistic olive tree mock-ups with high accuracy and neural networks for property estimation. The dataset covers various biophysical and structural properties of olive trees to ensure accurate retrieval and validation is done using in-situ measurements.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2021)
Article
Environmental Sciences
Qi Sun, Quanjun Jiao, Xiaojin Qian, Liangyun Liu, Xinjie Liu, Huayang Dai
Summary: The study proposed a method for estimating crop canopy chlorophyll content (CCC) by combining MERIS and LAI-VIs, resulting in more accurate estimates using random forest regression.
Article
Ecology
Alexander J. Turner, Philipp Kohler, Troy S. Magney, Christian Frankenberg, Inez Fung, Ronald C. Cohen
Summary: A study developed a parsimonious relationship between TROPOMI's SIF and GPP in the US, using a Gaussian mixture model. The research found that extreme precipitation events cause regional GPP anomalies, but overall, there is less than 4% variation in CONUS GPP between 2018 and 2019.
Article
Agriculture, Multidisciplinary
Haiying Jiang, Xiangqin Wei, Zhulin Chen, Mengxun Zhu, Yunjun Yao, Xiaotong Zhang, Kun Jia
Summary: This study investigated the influence of different soil reflectance schemes on the accuracy of retrieving leaf area index (LAI) and fractional vegetation cover (FVC) using the PROSAIL model in an agriculture region. The results showed that soil reflectance had a greater impact on LAI retrieval and was more important for canopies with low LAI and FVC.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Environmental Sciences
Kun Zhou, Donghui Xie, Jianbo Qi, Zhixiang Zhang, Xinyu Bo, Guangjian Yan, Xihan Mu
Summary: This study reconstructs vegetation scenarios and applies them to a radiation transfer model to simulate remote sensing observations and radiative budget. The simulation results explain angle effects and the variation and robustness of the normalized difference vegetation index, and validate energy conservation. The study also provides guidance on simplifying geometries and tuning illumination resolution to balance simulation accuracy and efficiency.
JOURNAL OF REMOTE SENSING
(2023)
Article
Multidisciplinary Sciences
Yanli Lu, Xiaoyu Zhang, Yuezhi Cui, Yaru Chao, Guipei Song, Caie Nie, Lei Wang
Summary: Spectral technology is effective in diagnosing N stress in maize, but it is affected by varietal differences. The responses to N stress, leaf N spectral diagnostic models, and varietal differences were analyzed in this study. The diagnostic stages for Jiyu 5817 and Zhengdan 958 were identified, and considering varietal effect in the diagnostic model improved its fit and RMSE.
SCIENTIFIC REPORTS
(2023)