Jiali Shang

Agriculture and Agri-Food Canada | AAFC · Ottawa Research and Development Centre

Leaf area index (LAI) and fractional vegetation cover (FVC) are two essential vegetation parameters for ecological and climate studies. The Chinese Gaofen-1(GF-1) wide field view (WFV) satellite data is a valuable data source for LAI and FVC retrieval at high spatio-temporal resolution. Like its name, GF-1 WFV has very large view angle ranging from...

The direct biophysical effects of fine-scale tree cover changes on temperature are not well understood. Here, we show how land surface temperature responds to subgrid gross tree cover changes. We find that in many forests, the biophysical cooling induced by enhanced evapotranspiration due to tree cover gain is greater in magnitude than the warming...

The retrieval of continuous leaf area index (LAI) in space and time from remote sensing is beneficial for cropland monitoring and management. Synthetic Aperture Radar (SAR) with the advantages of all-weather operation and fine spatial resolutions has been utilized in various agricultural applications. Although the water cloud model (WCM) has been e...

Height is a key factor in monitoring the growth status and rate of crops. Compared with large-scale satellite remote sensing images and high-cost LiDAR point cloud, the point cloud generated by the Structure from Motion (SfM) algorithm based on UAV images can quickly estimate crop height in the target area at a lower cost. However, crop leaves grad...

Understanding the spatial distribution of soil organic matter (SOM) is important for land use management, but conventional sampling methods require significant human and financial resources. How to map SOM and monitor its changes using a limited number of sample points combined with remote sensing techniques that provide long-time series data is cr...

Leaf area index (LAI) is a widely used plant biophysical parameter required for modelling plant photosynthesis and crop yield estimation. UAV remote sensing plays an increasingly important role in providing the data source needed for LAI extraction. This study proposed a UAV-derived 3-D point cloud-based method to automatically calculate crop-effec...

To take full advantage of cloud-free optical remote sensing data for crop leaf area index (LAI) retrieval throughout the growing season, integration of multi-sensor satellite data is increasingly resorted. However, the consistencies of LAI products derived from different satellites using different retrieval approaches should be assessed. This study...

Genetic variation among populations within plant species can have huge impact on canopy biochemistry and structure across broad spatial scales. Since canopy spectral reflectance is determined largely by canopy biochemistry and structure, spectral reflectance can be used as a means to capture the variability of th genetic characteristics of plant sp...

Near real-time (NRT) crop phenology detection and forecasting at the sub-field level are important for crop growth monitoring and management in precision agriculture. Previous studies focused mainly on extracting phenological metrics (e.g., the start of season, and end of season) from time-series remote sensing data for a complete growing season. T...

Early or in-season crop type mapping using remote sensing data is important for crop managements to maximize crop yield. Existing remote sensing-based approaches in the literature rely mainly on the timing and temporal frequency of satellite data acquisition during a growing season to obtain optimal identification feature (OIF) to complete crop map...

Drought is considered one of the key barriers influencing wheat production, and various adaptation schemes are practiced globally to mitigate drought impacts. However, it is difficult to precisely assess the performances of drought mitigation measures, especially when multiple measures are implemented simultaneously. Here, a remote sensing-based ag...

Model-based polarimetric decomposition can separate to some extent the backscattered radar signals from the vegetation canopy and the underlying ground, hence enabling a strategy for soil moisture retrieval in vegetated agricultural fields. However, the volume scattering models used in previous studies are only applicable to specific cases, making...

The use of unmanned aerial vehicle (UAV) provide a timely and low-cost means of accessing high spatial resolution imagery for crop disease detection. In this study, convolutional neural networks (CNNs) and RGB-based high spatial resolution images from UAVs were explored to detect wheat stripe rust transmission centers (Infected area accounted less...

Soil moisture content (SMC) is an indispensable basic element for crop growth and development in agricultural production. Obtaining accurate information on SMC in real time over large agricultural areas has important guiding significance for crop yield estimation and production management. In this study, the paper reports on the retrieval of SMC fr...

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The application of remote sensing technology in grassland monitoring and management has been ongoing for decades. Compared with traditional ground measurements, remote sensing technology has the overall advantage of convenience, efficiency, and cost effectiveness, especially over large areas. This paper provides a comprehensive review of the latest...

Crop identification and classification are of great significance to agricultural land use management. The physically constrained general model-based decomposition (PCGMD) has proven to be a promising method in comparison with the typical four-component decomposition methods in scattering mechanism interpretation and identifying vegetation types. Ho...

Accurate and timely information on soil moisture (SM) is crucial for better understanding the hydrological and ecological processes in humid saline regions. This study investigated the potential of using Sentinel-1A Synthetic Aperture Radar (SAR) imagery and Machine Learning Algorithms for SM mapping in China's east coast. This study used the recur...

Soil water deficit and high atmospheric dryness (vapor pressure deficit, VPD) are major environmental limitations on carbon uptake of terrestrial ecosystems. However, it is still unclear how climate seasonality influences seasonal soil water supply and atmospheric water demand, and consequently limits plant photosynthesis. Here, we analyzed the imp...

This study aimed to exploit the use of deep learning networks in the retrieval of the biophysical and biochemical parameters of vegetation canopies. Convolutional Neural Network (CNN), network with only fully connected layers, referred as dense network (DNN), and Autoencoder (AE) were investigated to retrieve leaf area index (LAI) and leaf chloroph...

Synthetic aperture radars (SARs) propagate and measure the scattering of energy at microwave frequencies. These wavelengths are sensitive to the dielectric properties and structural characteristics of targets, and less affected by weather conditions than sensors that operate in optical wavelengths. Given these advantages, SARs are appealing for use...

Detecting winter canola (Brassica napus) phenological stages using an improved shape-model method based on time-series UAV spectral data, The Crop Journal (2022), doi: https://doi. Abstract: Accurate information about phenological stages is essential for canola field management practices such as irrigation, fertilization, and harvesting. Previous s...

The climatic drivers of leaf phenology and water stress regulation strategies in tropical/subtropical forest biomes are poorly understood on the continental scale. Widespread field observations and remotely sensed plant phenology and physiology data across tropical forest ecosystems at various scales provide new insight into the response of tropica...

Cropland classification can be used to monitor cropland distribution and its change over time. In this letter, a new superpixel-based cropland classification method is proposed for synthetic aperture radar (SAR) imagery through the integration of statistical texture, polarization, and spatial information. First, the method combines random forest al...

Citation: Wang, J.; Zhou, Q.; Shang, J.; Liu, C.; Zhuang, T.; Ding, J.; Xian, Y.; Zhao, L.; Wang, W.; Zhou, G.; et al. UAV-and Machine Learning-Based Retrieval of Wheat SPAD Values at the Overwintering Stage for Variety Screening. Remote Sens. 2021, 13, 5166. https:// Abstract: In recent years, the delay in sowing has become a major obstacle to hig...

Measurements of leaf area index (LAI) are important for modeling microclimate in vegetation research. Among the instruments for measuring the LAI, smartphone cameras are becoming an attractive alternative to special LAI instruments. However, the narrow full field of view (FOV) of the common smartphones offer only an effective viewing zenith angle (...

Delineation of agricultural fields is desirable for operational monitoring of agricultural production and is essential to support food security. Due to large within-class variance of pixel values and small inter-class difference, automated field delineation remains to be a challenging task. In this study, a strategy is proposed to effectively addre...

Our limited understanding of the impacts of drought on tropical forests significantly impedes our ability in accurately predicting the impacts of climate change on this biome. Here, we investigated the impact of drought on the dynamics of forest canopies with different heights using time-series records of remotely sensed Ku-band Vegetation Optical...

Abstract Assessing the spatial distribution of soil heavy metals in urban areas in relation to land use, lithology and landform may provide insights for soil quality monitoring. This study evaluated the spatial distribution, the sources and the extent of heavy metal(loid)s in the topsoil of Fuzhou city, China. A combination of GIS and multivariate...

It is generally accepted that land use and land management practices impact climate change through sequestration of carbon in soils, but modulation of surface energy budget can also be important. Using Landsat data to characterize cropland albedos in Canada’s three prairie soil zones, this study estimates the atmospheric carbon equivalent drawdown...

Timely and accurate mapping crops is essential for agricultural management, policy making and food security. The smallholder agricultural systems lead to large number of fragmented and heterogeneous landscape, making fine crop mapping a huge challenge. Feature and classifier selection are two important influencing factors in crop classification. Ho...

The microclimate dynamics under forest crown fundamentally drive plant community responses to global warming. The understory air and soil temperatures are two of the most important components of forest understory microclimate. However, there is rare method to reasonably evaluate the joint effects of forest cover on the understory air and soil tempe...

Data assimilation, a state-of-the-art method that merges remote sensing data with a dynamic model to improve model performance, has been widely used in land surface process modeling. Application of data assimilation under various water conditions can provide insight of crop response to different water supply rates, which is useful for agricultural...

Rice false smut (RFS), caused by Ustilaginoidea virens, is a significant grain disease in rice that can lead to reduced yield and quality. In order to obtain spatiotemporal change information, multitemporal hyperspectral UAV data were used in this study to determine the sensitive wavebands for RFS identification, 665–685 and 705–880 nm. Then, two m...

Assessing the spatial dynamics of soil organic carbon (SOC) is essential for carbon monitoring. Since variability of SOC is mainly attributed to biophysical land surface variables, integrating a compressive set of such indices may support the pursuit for optimum set of predictor variables. Therefore, this study was aimed at predicting the spatial d...

Multitemporal polarimetric synthetic aperture radar (PolSAR) has proven as a very effective technique in agricultural monitoring and crop classification. This study presents a comprehensive evaluation of crop monitoring and classification over an agricultural area in southwestern Ontario, Canada. The time-series RADARSAT-2 C-Band PolSAR images thro...

Soil moisture is vital for the crop growth and directly affects the crop yield. Conventional SAR based soil moisture monitoring is often influenced by vegetation cover and surface roughness. Machine-learning methods are not constrained by physical parameters and have high nonlinear fitting capabilities. In this study, machine-learning methods were...

We evaluate the potential of using a process-based ecosystem model (BEPS) for crop biomass mapping at 20 m resolution over the research site in Manitoba, western Canada driven by spatially explicit leaf area index (LAI) retrieved from Sentinel-2 spectral reflectance throughout the entire growing season. We find that overall, the BEPS-simulated crop...

This study presents a demonstration of the applicability of machine learning techniques for the retrieval of crop height in corn fields using space-borne PolSAR (Polarimetric Synthetic Aperture Radar) data. Multi-year RADARSAT-2 C-band data acquired over agricultural areas in Canada, covering the whole corn growing period, are exploited. Two popula...

Accurate, efficient, timely, and affordable measurements of crop structural parameters such as leaf area index (LAI) and mean tilt angle (MTA) are needed for crop growth modeling and precision field management. In this paper, a novel method was proposed to simultaneously measure corn (Zea mays L.) LAI and MTA using a low-cost indirect approach. The...

Soil moisture (Mv) estimation and monitoring over agricultural areas using Synthetic Aperture Radar (SAR) are often affected by vegetation cover during the growing season. Volume scattering and vegetation attenuation can complicate the received SAR backscatter signal when microwave interacts with the vegetation canopy. To address the existing probl...

Canada is one of the top wheat grain exporters, with a share of more than 10% in the world wheat market. The majority of Canadian wheat production takes place in the Prairies where 6.2 million ha of the area is seeded to spring wheat. The climate is semiarid with an estimated precipitation deficit of about 300 mm during the crop growing season, ind...

Green leaf area index (LAI) is an important variable related to crop growth. Accurate and timely information on LAI is essential for developing suitable field management strategies to mitigate risk and boost yield. Several remote sensing (RS) based methods have been recently developed to estimate LAI at the regional scale. However, the performance...

Accurate and continuous monitoring of leaf area index (LAI), a widely used vegetation structural parameter, is crucial to characterize crop growth conditions and forecast crop yield. Meanwhile, advancements in collecting field LAI measurements have provided strong support for validating remote sensing derived LAI. This paper evaluates the performan...

The availability of Landsat 8 and Sentinel-2 has led to a steady increase in both temporal and spatial resolution of satellite data, offering new opportunities for large-scale crop condition monitoring and crop yield mapping. This study investigated the potential of using Landsat 8 and Sentinel-2 data from the harmonized Landsat 8 and Sentinel-2 (H...

Chlorophyll is an essential pigment for photosynthesis in crops, and leaf chlorophyll content can be used as an indicator for crop growth status and help guide nitrogen fertilizer applications. Estimating crop chlorophyll content plays an important role in precision agriculture. In this study, a variable, rate of change in reflectance between wavel...

Background: Assessing the spatial dynamics of soil organic carbon (SOC) is essential for carbon monitoring. Since, variability of SOC is mainly attributed to biophysical land surface variables, integrating a compressive set of such indices may support the pursuit for optimum set of predictor variables. Therefore, this study was aimed at predicting...

Remote sensing is a useful tool for monitoring spatio-temporal variations of crop morphological and physiological status and supporting practices in precision farming. In comparison with multispectral imaging, hyperspectral imaging is a more advanced technique that is capable of acquiring a detailed spectral response of target features. Due to limi...

Spatial information on crop nutrient status is central for monitoring vegetation health, plant productivity and managing nutrient optimization programs in agricultural systems. This study maps the spatial variability of leaf chlorophyll content within fields with differing quantities of nitrogen fertilizer application, using multispectral Landsat-8...

Knowledge of sub-field yield potential is critical for guiding precision farming. The recently developed simulated observation of point cloud (SOPC) method can generate high spatial resolution winter wheat effective leaf area index (SOPC-LAIe) maps from the unmanned aerial vehicle (UAV)-based point cloud data without ground-based measurements. In t...

Based on experimental results, this chapter describes applications of SAR polarimetry to extract relevant information on agriculture and wetland scenarios by exploiting differences in the polarimetric signature of different scatterers, crop types and their development stage depending on their physical properties. Concerning agriculture, crop type m...

We evaluated the utility of Terra/MODIS derived crop metrics for yield estimation across the Canadian Prairies. This study was undertaken at the Census Agriculture Region (CAR) and the Rural Municipality (RM) of the province of Saskatchewan, in three prairie agro-climate zones. We compared MODIS-derived vegetation indices, gross primary productivit...

Within-field variation of leaf area index (LAI) plays an essential role in field crop monitoring and yield forecasting. Although unmanned aerial vehicle (UAV)-based optical remote sensing method can overcome the spatial and temporal resolution limitations associated with satellite imagery for fine-scale within-field LAI estimation of field crops, i...

Synthetic aperture radar (SAR) is more sensitive to the dielectric properties and structure of the targets and less affected by weather conditions than optical sensors, making it more capable of detecting changes induced by management practices in agricultural fields. In this study, the capability of C-band SAR data for detecting crop seeding and h...

In the areas where the soils are polluted by cadmium (Cd), farmers cannot afford to use their lands for remediation due to high demand for soybean (Glycine max L.) production. In order to provide information for safe soybean production and achieve the phytoremediation using the root at the meantime, four soybean cultivars (mentioned as Cd excluder...

Annual crop inventory information is important for many agriculture applications and government statistics. The synergistic use of multi-temporal polarimetric synthetic aperture radar (SAR) and available multispectral remote sensing data can reduce the temporal gaps and provide the spectral and polarimetric information of the crops, which is effect...

Understanding the urban land dynamics and its causes is critical to manage and predict both urban development and its associated environmental qualities. However, little is known and documented about the historical urban land dynamics in Pingtan. By integrating remote sensing, geographic information systems and statistical analysis, this study aims...

Accurate and timely information on soil salinity is crucial for vegetation growth and agricultural productivity in coastal regions. This study investigates the potential of using Wifi POGO, an in situ electromagnetic sensor, for soil salinity assessment over saline coastal regions in eastern China. The sensor readings, soil moisture, and temperatur...

The articles in this special section focus on the use of digital technology in agricultural research and applications. Sustainability of global agricultural and food systems is one of the prominent factors for peaceful future of the world in next few decades. Although agriculture is the main part of the global food supply chain, it is under rising...

The polarimetric Synthetic Aperture Radar (PolSAR) signal contains more parameters than single or dual polarized SAR when using a scattering matrix to characterize targets. The increased information content of PolSAR provides more potential inputs for machine learning and classification applications; however, polarimetric parameters tend to be simp...

Remote sensing has been recognized as a cost-effective way to detect the spatial and temporal variability of crop growth and productivity. In this study, multispectral RapidEye images were used to delineate homogeneous zones of soil and crop development in two fields in Ontario, Canada, one planted with canola (Brassica napus L.) and the other with...

Accurate measurement of leaf chlorophyll concentration (LChl) in the field using a portable chlorophyll meter (PCM) is crucial to support methodology development for mapping the spatiotemporal variability of crop nitrogen status using remote sensing. Several PCMs have been developed to measure LChl instantaneously and non-destructively in the field...