Article
Environmental Sciences
Chao Chen, Huixin Chen, Jintao Liang, Wenlang Huang, Wenxue Xu, Bin Li, Jianqiang Wang
Summary: This article introduces a method for extracting water body information from remote sensing images, which takes into account the greenness and wetness of ground-based objects. The method has been proven to have high accuracy and consistency and can be applied to updating geographical databases of water bodies and water resource management.
Article
Horticulture
Giovanni Caruso, Giacomo Palai, Francesco Paolo Marra, Tiziano Caruso
Summary: Remote sensing techniques using images captured by UAVs are effective in highlighting differences in geometric and spectral characteristics of olive trees. The study shows that UAVs can linearly estimate canopy features and accurately estimate pruning material volume.
Article
Engineering, Environmental
Abdoulaye Mahamat Malabad, Fabienne Tatin-Froux, Gilles Gallinet, Jean-Michel Colin, Michel Chalot, Julien Parelle
Summary: The study aimed to evaluate the canopy conductance of tree species and the content of elements in leaves, identifying Ostrya carpinifolia as a potentially useful species for managing landfill leachates.
JOURNAL OF HAZARDOUS MATERIALS
(2022)
Article
Environmental Sciences
Hongfang Chang, Jiabing Cai, Baozhong Zhang, Zheng Wei, Di Xu
Summary: Incorporating remote-sensing-derived land surface temperature (LST) and in-season data into logistic models, accurate forecasting of maize yield can be achieved. Experimental results in Changchun, Jilin Province and Jiefangzha sub-irrigation district, Inner Mongolia, China validate the effectiveness of this approach.
Article
Remote Sensing
Jun Zhou, Xiangyu Lu, Rui Yang, Huizhe Chen, Yaliang Wang, Yuping Zhang, Jing Huang, Fei Liu
Summary: This study develops a novel yield index by fusing multiple indices based on unmanned aerial vehicle imagery, which shows great potential in crop yield monitoring.
Article
Environmental Sciences
Binglong Wu, Yuan Shen, Shanxin Guo, Jinsong Chen, Luyi Sun, Hongzhong Li, Yong Ao
Summary: Deep-learning-based object detectors have made significant improvements in object detection in remote sensing images. However, challenges still exist due to the variation in object scales and the imbalance between positive and negative samples. This paper proposes a Cascade R-CNN++ structure to address these challenges and demonstrates its effectiveness through experiments.
Article
Plant Sciences
Shagor Sarkar, Jing Zhou, Andrew Scaboo, Jianfeng Zhou, Noel Aloysius, Teng Teeh Lim
Summary: This study investigated the potential of using UAV-based imagery and machine learning to assess soybean lodging conditions for breeding programs. The results showed that a classification model based on imagery can effectively differentiate lodging phenotypes and classify lodging scores. The preprocessing method SMOTE-ENN consistently performed well for all classifiers, improving classification accuracy and demonstrating its applicability for unbalanced datasets.
Article
Geochemistry & Geophysics
Cheng Su, Zeyu Xu, Fan Shen, Xiaocan Zhang
Summary: This letter presents an automatic detection method for identifying blurred areas within remote sensing images, and explores the relationship between the level of visual blurring and the difficulty of automatic detection.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2023)
Article
Engineering, Electrical & Electronic
Manish Sharma, Mayur Dhanaraj, Srivallabha Karnam, Dimitris G. Chachlakis, Raymond Ptucha, Panos P. Markopoulos, Eli Saber
Summary: Deep-learning object detection methods designed for computer vision applications perform poorly on remote sensing data due to difficulties in collecting training data, small target sizes, and arbitrary perspective transformations. Fusion of data from multiple remote sensing modalities can improve detection performance. YOLOrs is a new convolutional neural network specifically designed for real-time object detection in multimodal remote sensing imagery, capable of detecting objects at multiple scales and predicting target orientations.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Agronomy
J. M. Ramirez-Cuesta, M. F. Ortuno, V Gonzalez-Dugo, P. J. Zarco-Tejada, M. Parra, J. S. Rubio-Asensio, D. S. Intrigliolo
Summary: The thermal region of the electromagnetic spectrum can provide valuable information for assessing plant water status. This study compares different measurement methods and finds that aerial measurements are better for predicting stem water potential, while ground measurements are preferred for estimating stomatal conductance and net photosynthesis.
AGRICULTURAL WATER MANAGEMENT
(2022)
Article
Agronomy
Rebecca Dunkleberger, David J. Sauchyn, Mark C. Vanderwel
Summary: With a warming climate and greater evaporative demand, forest ecosystems are increasingly affected by water limitation. This study evaluated the use of airborne thermal imagery from unmanned aerial vehicles as a tool for assessing tree-level water stress. The researchers found that leaf temperature and daily change in tree water deficit were both correlated with soil moisture, vapor pressure deficit, and wind speed. Different tree species showed some differences in response to drought.
AGRICULTURAL AND FOREST METEOROLOGY
(2023)
Review
Agronomy
Andrew W. W. Herr, Alper Adak, Matthew E. E. Carroll, Dinakaran Elango, Soumyashree Kar, Changying Li, Sarah E. E. Jones, Arron H. H. Carter, Seth C. C. Murray, Andrew Paterson, Sindhuja Sankaran, Arti Singh, Asheesh K. K. Singh
Summary: High-throughput phenotyping (HTP) using unmanned aerial systems (UAS) is a promising tool for plant breeding and research. This review focuses on the application of UAS-collected data in cotton, maize, soybean, and wheat, illustrating how it can automate and improve estimation of phenotypic traits. The potential applications include measuring abiotic and biotic stress, crop growth and development, and productivity.
Article
Environmental Sciences
Sergii Skakun, Natacha I. Kalecinski, Meredith G. L. Brown, David M. Johnson, Eric F. Vermote, Jean-Claude Roger, Belen Franch
Summary: Crop yield monitoring is crucial for agricultural assessment, with satellite remote sensing instruments playing a key role in providing timely and comprehensive information on crop growth. Advances in satellite imagery technology enable estimation of corn and soybean yields at sub-field scales, with assessment of different satellite sensors' efficiency.
Article
Environmental Sciences
Shreya Pare, Himanshu Mittal, Mohammad Sajid, Jagdish Chand Bansal, Amit Saxena, Tony Jan, Witold Pedrycz, Mukesh Prasad
Summary: Segmentation techniques in remote sensing imagery face challenges such as dense features, low illumination, uncertainties, and noise. Existing multilevel thresholding methods lack spatial information, leading to low segmentation accuracy.
Article
Agronomy
Peder K. Schmitz, Hans J. Kandel
Summary: Planting date, seeding rate, relative maturity, and row spacing are key factors affecting soybean yield. Maximizing canopy cover before flowering improves seed yield. Combining early planting, optimum cultivars, high seeding rate, and narrow row spacing can significantly increase yield and profit compared to conventional practices.
Article
Agronomy
Jiaxin Sun, Yanli Yang, Peng Qi, Guangxin Zhang, Yao Wu
Summary: The optimal allocation of agricultural water and land resources is crucial for farmers' economic benefits, carbon sequestration, and water resource conservation. This study developed a novel water-carbon-economy coupling model and applied it to a real farm, demonstrating its effectiveness in achieving the optimal allocation of water and land resources. The model balances economic, environmental, and social benefits.
AGRICULTURAL WATER MANAGEMENT
(2024)