Jiale Jiang

Jiale Jiang
King Abdullah University of Science and Technology | KAUST · Hydrology, Agriculture and Land Observation (HALO) Lab

PhD

About

25
Publications
9,731
Reads
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265
Citations
Additional affiliations
September 2019 - August 2020
King Abdullah University of Science and Technology
Position
  • PostDoc Position
July 2017 - July 2019
Nanjing Agricultural University
Position
  • PostDoc Position
Education
September 2012 - July 2017
China University of Geosciences (Beijing)
Field of study
  • Surveying and Mapping
September 2008 - July 2012
China University of Geosciences (Beijing)
Field of study
  • Geographic Information System

Publications

Publications (25)
Article
Full-text available
Given its high nutritional value and capacity to grow in harsh environments, quinoa has significant potential to address a range of food security concerns. Monitoring the development of phenotypic traits during field trials can provide insights into the varieties best suited to specific environmental conditions and management strategies. Unmanned a...
Article
Full-text available
Real-time and accurate monitoring of nitrogen content in crops is crucial for precision agriculture. Proximal sensing is the most common technique for monitoring crop traits, but it is often influenced by soil background and shadow effects. However, few studies have investigated the classification of different components of crop canopy, and the per...
Article
Full-text available
Developments in computer vision, such as structure from motion and multiview stereo reconstruction, have enabled a range of photogrammetric applications using unmanned aerial vehicles (UAV)-based imagery. However, some specific cases still present reconstruction challenges, including survey areas composed of steep, overhanging, or vertical rock for...
Data
The package contains 1) HISTIF code, 2) user's guide, and 3) test data.
Data
The package contains 1) HISTIF installer, 2) user's guide, and 3) test data.
Article
Full-text available
Satellite-based time-series crop monitoring at the subfield level is essential to the efficient implementation of precision crop management. Existing spatiotemporal image fusion techniques can be helpful, but they were often proposed to generate medium-resolution images. This study proposed a HIgh-resolution SpatioTemporal Image Fusion method (HIST...
Article
Full-text available
Commercially available digital cameras can be mounted on an unmanned aerial vehicle (UAV) for crop growth monitoring in open-air fields as a low-cost, highly effective observation system. However, few studies have investigated their potential for nitrogen (N) status monitoring, and the performance of camera-derived vegetation indices (VIs) under di...
Article
Full-text available
High-resolution satellite images can be used to some extent to mitigate the mixed-pixel problem caused by the lack of intensive production, farmland fragmentation, and the uneven growth of field crops in developing countries. Specifically, red-edge (RE) satellite images can be used in this context to reduce the influence of soil background at early...
Article
Full-text available
Unmanned aerial vehicle (UAV)-based multispectral sensors have great potential in crop monitoring due to their high flexibility, high spatial resolution, and ease of operation. Image preprocessing, however, is a prerequisite to make full use of the acquired high-quality data in practical applications. Most crop monitoring studies have focused on sp...
Article
Full-text available
Unmanned aerial vehicle (UAV)-based remote sensing (RS) possesses the significant advantage of being able to efficiently collect images for precision agricultural applications. Although numerous methods have been proposed to monitor crop nitrogen (N) status in recent decades, just how to utilize an appropriate modeling algorithm to estimate crop le...
Conference Paper
Full-text available
Rice blast is one of the most devastating crop diseases around the world. Although previous remote sensing studies have examined the spectral variation at leaf and canopy levels in response to disease severity levels, the non-imaging nature of their data makes it difficult to examine the spectral variation related to the disease within a leaf. This...
Article
Full-text available
Identification of paddy fields is essential for monitoring the rice cultivated area and predicting rice productivity. Timely and accurate extraction of rice distribution can bring vital information for national food security, agricultural policy formulation, and regional environmental sustainability. Conventional classification methods usually suff...
Article
Full-text available
The correction of spatial scaling bias on the estimate of leaf area index (LAI) retrieved from remotely sensed data is an essential issue in quantitative remote sensing for vegetation monitoring. We analyzed three techniques, including Taylor's theorem (TT), Wavelet-Fractal technique (WF), and Fractal theory (FT), for correcting the scaling bias of...
Article
Full-text available
Accurate monitoring of heavy metal stress in crops is of great importance to assure agricultural productivity and food security, and remote sensing is an effective tool to address this problem. However, given that Earth observation instruments provide data at multiple scales, the choice of scale for use in such monitoring is challenging. This study...
Conference Paper
Full-text available
Monitoring heavy metal stress in rice is significant for agricultural production management and food security. Remote sensing offers an undamaged and efficient approach to detect the crop and soil contamination. In this study, an improved BP neural network for predicting the accumulation of the total cadmium (Cd) in rice was proposed by using the g...
Article
Full-text available
Leaf area index (LAI) is a basic quantity indicating crop growth situation and plays a significant role in ecological model and interaction model between earth surface and atmosphere. However, nonlinear estimation processes of LAI from heterogeneous remote sensing data would induce a scaling bias. The purpose of this study is to provide a method to...
Article
Full-text available
Monitoring of heavy metal stress in crops is vital for food security and agricultural production management. Traditional remote sensing methods focus on the stress-induced changes to the aerial organs of plants, whereas roots are considered to be more directly and severely stressed. In this study, the dry weight of rice roots (WRT) was used as an i...
Article
Full-text available
Ocean salinity is a key parameter in oceanic and climate studies, and the accurate estimation of sea surface salinity (SSS) of coastal water is of great scientific interest. This paper reports on a modeling study of SSS using artificial neural network (ANN) and random forest (RF) algorithm. Hong Kong Sea, China was used as case study. Sea biochemis...

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