Low-altitude Unmanned Aerial Vehicle Research Articles

Droplet deposition density of organic liquid fertilizer at low altitude UAV aerial spraying in rice cultivation

Currently, unmanned aerial vehicles (UAVs) are widely used to ease agricultural activities and has been progressed with recent technology development to perform aerial spraying in the rice cultivation. In fact, UAV aerial spraying ability is not just limited to crop protection but it can be extended for crop fertilization activities. However, off-target and insufficient amount of droplet deposition density during the spraying operation has always been a major concern in research area for UAV aerial spraying. The study aimed to evaluate the optimal operating parameters such as UAV flying speeds, and spraying rate on the droplet deposition density of the targeted surface for a standard UAV spraying system available in the market. Field trials were performed by using UAV that flew at the different flying speeds (2, 4 & 6 m/s), at the constant height of 2 m above the rice canopy to spray the organic liquid fertilizer at the different spraying rate (0.75, 1.5, 2.25 & 3.00 L/min). The water-sensitive paper was used as a sampler to collect droplets and droplet deposition density was statistically analysed. The experimental results showed that the droplet uniformity and droplet deposition density was higher when the UAV flying speed was maintained at the lower speed (2 m/s) compared to the higher flying speed (4 and 6 m/s). Hence, the droplet uniformity and droplet deposition density show the highest count with better droplet pattern when spraying the organic liquid fertilizer at 3.00 L/min spray-rate compared to other sprayer rates. Results from the study could provide technical support for UAV aerial spraying within the rice cultivation field.

Estimating biomass of winter oilseed rape using vegetation indices and texture metrics derived from UAV multispectral images

Monitoring the above ground biomass (AGB) in winter oilseed rape is important for improving the agronomic management efficiency and predicting yield to ensure edible oil and biofuel supplies. The Yangtze River Basin in China accounts for one-fifth of the rapeseed yield in the world. However, the fragmented farming lands in the Yangtze River Basin make it difficult to accurately monitor winter oilseed rape growth at large scale using the medium-resolution satellite data. The low-altitude unmanned aerial vehicle (UAV) provides a feasible way to accurately and non-destructively estimate AGB in winter oilseed rape at the plot level. In this study, we evaluated the contributions of vegetation indices (VIs) and texture metrics, derived from multispectral images captured by the camera mounted on a UAV, to predict the AGB in winter oilseed rape. The AGB was estimated with (1) multiple VIs and (2) multiple VIs and texture metrics. The partial least square regression (PLSR) and random forest (RF) regression models were trained with datasets of the experiment in 2016–2017 and 2017–2018, and then applied to predict AGB of the 2018–2019 growth season. Results demonstrated that the incorporation of texture metrics to both PLSR and RF models provided more accurate estimations of AGB in winter oilseed rape than the models based solely on VIs. The accuracy of the AGB predicted by the RF regression model using VIs and texture metrics (RMSE = 274.18 kg/ha for the validation dataset) was slightly higher than the results of the PLSR model (RMSE = 284.09 kg/ha for the validation dataset). According to the evaluation of the important variables, the red edge chlorophyll index (CIred edge) and ratio vegetation index (RVI) were selected as the most important input features by PLSR and RF regression models. The normalized difference vegetation index (NDVI) contrast was selected as an important texture metrics for the AGB prediction, indicating that NDVI contrast could be a sensitive indicator of the spatial distribution of shadow caused by the different amount of biomass. This study suggested the great potential of UAV in estimating the plot-level AGB by combining VIs and texture metrics.

Environmental condition in British moorlands: quantifying the life cycle of Calluna vulgaris using UAV aerial imagery

ABSTRACT As the dominant vegetation species of most British moorlands, Calluna vulgarisis the focus of much scientific interest. While multiple approaches for quantifying the life cycle of the species have been tested, few, if any, have been successful. Given that British moorland environments are internationally valuable in terms of biogeography and carbon storage, but also inherently vulnerable to change, robust long-term monitoring is urgently needed. This study aimed to develop an original methodology, using UAV (Unmanned Aerial Vehicle) aerial imagery to quantify both flowering and vegetation height at different C. vulgaris growth phases. The former has been previously investigated, although not using low-altitude UAV imagery. The latter has not been tested before. Flower coverage was successfully quantified using supervised pixel-based classification, with statistically significant differences found between percentage flower coverage at each growth phase. Findings from height analysis, using structure-from-motion point clouds, suggest that modelled vegetation height can also be used to distinguish between growth phases. These findings demonstrate that UAV imagery can improve data acquisition in field ecology. The novel approach developed here, based on flower coverage and vegetation height, has potential for use beyond the moorland environment, to improve understadning of spatial characteristics of ecological systems and processes.

Spatial pattern analysis of Haloxylon ammodendron using UAV imagery - A case study in the Gurbantunggut Desert

Spatial patterns are not only the foundation for the understanding of plant interactions, but also reflect the spatial processes among plant populations. The primary requirement of spatial pattern analysis is the collections of location information of individual plants. In this study, we used low-altitude Unmanned Aerial Vehicle (UAV) remote sensing technology to obtain regional high-precision remote sensing images for Haloxylon ammodendron (H. ammodendron) forest in the southwestern Gurbantunggut Desert, and extracted spatial position information to analyze the spatial patterns using Ripley's L(r) function. Applying and comparing seven spatial position extraction methods, this study showed that the index RGRI (Red-Green Ratio Index) made 83.46% accuracy in the spatial position extraction, and an accuracy of 79.48% was obtained using NGBDI (Normalized Green-Blue Difference Index), while other five location information extraction methods resulted relatively lower accuracy. Results from spatial pattern analysis indicated that the extraction by UAV remote sensing were consistent with those obtained by field measurements. The H. ammodendron population showed a random distribution within the scale of 0–15 m, which suggested that the dependence of mutual asylum between individuals was low and not important. This distribution may be caused by the intense competition of individual vegetation for soil moisture, nutrients and other resources in desert areas. This study with low-altitude UAV imagery index analysis provided an efficient approach to rapid monitoring of plant population distribution characteristics in desert areas.

Seed and Seedling Detection Using Unmanned Aerial Vehicles and Automated Image Classification in the Monitoring of Ecological Recovery

Monitoring is a crucial component of ecological recovery projects, yet it can be challenging to achieve at scale and during the formative stages of plant establishment. The monitoring of seeds and seedlings, which represent extremely vulnerable stages in the plant life cycle, is particularly challenging due to their diminutive size and lack of distinctive morphological characteristics. Counting and classifying seedlings to species level can be time-consuming and extremely difficult, and there is a need for technological approaches offering restoration practitioners with fine-resolution, rapid and scalable plant-based monitoring solutions. Unmanned aerial vehicles (UAVs) offer a novel approach to seed and seedling monitoring, as the combination of high-resolution sensors and low flight altitudes allow for the detection and monitoring of small objects, even in challenging terrain and in remote areas. This study utilized low-altitude UAV imagery and an automated object-based image analysis software to detect and count target seeds and seedlings from a matrix of non-target grasses across a variety of substrates reflective of local restoration substrates. Automated classification of target seeds and target seedlings was achieved at accuracies exceeding 90% and 80%, respectively, although the classification accuracy decreased with increasing flight altitude (i.e., decreasing image resolution) and increasing background surface complexity (increasing percentage cover of non-target grasses and substrate surface texture). Results represent the first empirical evidence that small objects such as seeds and seedlings can be classified from complex ecological backgrounds using automated processes from UAV-imagery with high levels of accuracy. We suggest that this novel application of UAV use in ecological monitoring offers restoration practitioners an excellent tool for rapid, reliable and non-destructive early restoration trajectory assessment.

UAV/SATELLITE MULTISCALE DATA FUSION FOR CROP MONITORING AND EARLY STRESS DETECTION

Abstract. Early stress detection is critical for proactive field management and terminal yield prediction, and can aid policy making for improved food security in the context of climate change and population growth. Field surveys for crop monitoring are destructive, labor-intensive, time-consuming and not ideal for large-scale spatial and temporal monitoring. Recent technological advances in Unmanned Aerial Vehicle (UAV) and high-resolution satellite imaging with frequent revisit time have proliferated the applications of this emerging new technology in precision agriculture to address food security challenges from regional to global scales. In this paper, we present a concept of UAV and satellite virtual constellation to demonstrate the power of multi-scale imaging for crop monitoring. Low-cost sensors integrated on a UAV were used to collect RGB, multispectral, and thermal images during the growing season in a test site established near Columbia, Missouri, USA. WorldView-3 multispectral data were pan-sharpened, atmospherically corrected to reflectance and combined with UAV data for temporal monitoring of early stress. UAV thermal and multispectral data were calibrated to canopy temperature and reflectance following a rigorous georeferencing and ortho-correction. The results show that early stress can be effectively detected using multi-temporal and multi-scale UAV and satellite observation; the limitations of satellite remote sensing data in field-level crop monitoring can be overcome by using low altitude UAV observations addressing not just mixed pixel issues but also filling the temporal gap in satellite data availability enabling capture of early stress. The concept developed in this paper also provides a framework for accurate and robust estimation of plant traits and grain yield and delivers valuable insight for high spatial precision in high-throughput phenotyping and farm field management.

Low‐altitude UAV air‐ground propagation channel measurement and analysis in a suburban environment at 3.9 GHz

Unmanned aerial vehicles (UAVs) have been applied to various promising applications because of their features of high-mobility, portability, rapid deployment, and modest power consumption. Small UAVs fly at low altitudes and typically short distances from the ground controller. It is of interest and value to model the propagation channel between low-altitude UAVs and ground stations. This article presents results of channel measurements at 3.9 GHz carried out in a suburban environment with a small-size UAV flying at low altitudes, up to 40 m. The authors also present comparative results from ray-tracing simulations. Height-dependent models for path loss, root mean square (RMS) delay spread, and the number of multipath components are provided. The results illustrate that although multipath effects exist at low altitudes in this environment, the two-ray path loss model works reasonably well in describing the channel behaviour. Initial ray tracing simulations show reasonable agreement with measurements. In addition, RMS delay spread results indicate that distant scatterers have an appreciable effect on the UAV air-ground (AG) propagation channels, which can be different from some terrestrial channels. Authors' results should be of use in the modelling of low-altitude AG propagation channels and in the performance analysis of UAV-enabled AG communication systems.

Wind Field Distribution of Multi-rotor UAV and Its Influence on Spectral Information Acquisition of Rice Canopies

Unmanned aerial vehicles (UAV) are widely used as remote sensing platforms to effectively monitor agricultural conditions. The wind field generated by the rotors in low-altitude operations will cause the deformation of rice crops, and may affect the acquisition of the true spectral information. In this study, a low-altitude UAV remote sensing simulation platform and a triple-direction wind field wireless sensor network system were built to explore the wind field distribution law. Combined with the multi-spectral images of the rice canopy, the influence of wind field on the spectral information acquisition was analyzed through variance and regression analysis. The results showed that the Z-direction wind field of UAV rotors dominated along three directions (X, Y, and Z). The coefficient of determination (R2) of three linear regression models for Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), and Canopy Coverage Rate (CCR) was 0.782, 0.749, and 0.527, respectively. Therefore, the multi-rotor UAV wind field had an impact on the spectral information acquisition of rice canopy, and this influence could eventually affect the assessment of rice growth status. The models established in this study could provide a reference for the revised model of spectral indices, and offer guidance for the actual operations of low-altitude multi-rotor UAV.

Effects of the ephemeral stream on plant species diversity and distribution in an alluvial fan of arid desert region: An application of a low altitude UAV.

Biodiversity conservation, plant growth and spatial distribution of plant species are the central issues in contemporary community ecology. Ephemeral stream may influence soil properties, which in turn may determine biodiversity and function of an ecosystem in alluvial fan of arid desert region. Ephemeral stream is one of the most common natural disturbances, yet the effects of the ephemeral stream on plant communities in terms of species diversity and plant species distribution remain poorly studied. In this study, the information of species distribution, ephemeral stream beds (‘washes’), and the characteristics of plant growth, i.e. height, crown area, were interpreted at different heights using the images of low altitude unmanned aerial vehicle (UAV). After that, soil properties such as soil texture (sand, silt and clay), soil water content, pH, soil organic matter, soil electric conductivity, soil bulk density and the percentage of gravel content, and their relationships with UAV data were assessed in order to explore the influences of ephemeral stream on species diversity, plant growth characteristics and species distribution in an alluvial fan of arid desert region. The results showed that deep-rooted plants were only distributed in washes whereas shallow-rooted plants were distributed in both washes and the outside of washes (‘non-washes’). Species richness was significantly higher in washes than that in non-washes whereas the opposite pattern was true for abundance. Soil properties, plant height and crown area were higher in washes than that in non-washes. Plant height, crown area and the total number of individual plants increased with increasing wash width and per unit length of stream flow. This study highlights that the coupling factors of ephemeral stream, such as soil erosion, particle transport and sedimentation, can dramatically cause changes in soil properties and total number of individual plants, and hence, can influence species diversity, plant growth characteristics and spatial distribution of plant species in an alluvial fan of arid desert regions.

Comparison of Satellite and UAV-Based Multispectral Imagery for Vineyard Variability Assessment

In agriculture, remotely sensed data play a crucial role in providing valuable information on crop and soil status to perform effective management. Several spectral indices have proven to be valuable tools in describing crop spatial and temporal variability. In this paper, a detailed analysis and comparison of vineyard multispectral imagery, provided by decametric resolution satellite and low altitude Unmanned Aerial Vehicle (UAV) platforms, is presented. The effectiveness of Sentinel-2 imagery and of high-resolution UAV aerial images was evaluated by considering the well-known relation between the Normalised Difference Vegetation Index (NDVI) and crop vigour. After being pre-processed, the data from UAV was compared with the satellite imagery by computing three different NDVI indices to properly analyse the unbundled spectral contribution of the different elements in the vineyard environment considering: (i) the whole cropland surface; (ii) only the vine canopies; and (iii) only the inter-row terrain. The results show that the raw s resolution satellite imagery could not be directly used to reliably describe vineyard variability. Indeed, the contribution of inter-row surfaces to the remotely sensed dataset may affect the NDVI computation, leading to biased crop descriptors. On the contrary, vigour maps computed from the UAV imagery, considering only the pixels representing crop canopies, resulted to be more related to the in-field assessment compared to the satellite imagery. The proposed method may be extended to other crop typologies grown in rows or without intensive layout, where crop canopies do not extend to the whole surface or where the presence of weeds is significant.

A low-altitude unmanned aerial vehicle (UAV) created using 3D-printed bioplastic

In this work, a four-person student team was given the challenge of designing, analysing, constructing, and testing a low-altitude unmanned aerial vehicle (UAV) prototype, which could meet or exceed a set of predefined performance requirements including range, altitude, time of flight, and load-carrying capability. In addition, the team was tasked with having their final design be composed of at least 70% sustainable material by volume. The final prototype took the form of a quadcopter with an airframe 3D printed from a plant-based bioplastic. This prototype was able to meet or exceed three of the four project performance targets, with time of flight being the lone failure. Besides serving as a proof-of-concept prototype of a functioning bioplastic-based UAV, this project is also a demonstration of 3D printing as an enabling technology that can allow even small design teams to realize complex geometries, enjoy enhanced design flexibility, and achieve high levels of UAV functionality with relatively limited resources. Finally, a discussion of important material parameters of 3D printed UAVs is presented.

RETRACTED: Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification

Asphalt pavement ages and incurs various distresses due to natural and human factors. Thus, it is crucial to rapidly and accurately extract different types of pavement distress to effectively monitor road health status. In this study, we explored the feasibility of pavement distress identification using low-altitude unmanned aerial vehicle light detection and ranging (UAV LiDAR) and random forest classification (RFC) for a section of an asphalt road that is located in the suburb of Shihezi City in Xinjiang Province of China. After a spectral and spatial feature analysis of pavement distress, a total of 48 multidimensional and multiscale features were extracted based on the strength of the point cloud elevations and reflection intensities. Subsequently, we extracted the pavement distresses from the multifeature dataset by utilizing the RFC method. The overall accuracy of the distress identification was 92.3%, and the kappa coefficient was 0.902. When compared with the maximum likelihood classification (MLC) and support vector machine (SVM), the RFC had a higher accuracy, which confirms its robustness and applicability to multisample and high-dimensional data classification. Furthermore, the method achieved an overall accuracy of 95.86% with a validation dataset. This result indicates the validity and stability of our method, which highway maintenance agencies can use to evaluate road health conditions and implement maintenance.

基于双通道的快速低空无人机检测识别方法

基于双通道的快速低空无人机检测识别方法

On the Performance of Low-Altitude UAV-Enabled Secure AF Relaying With Cooperative Jamming and SWIPT

This paper proposes a novel cooperative secure unmanned aerial vehicle (UAV) aided transmission protocol, where a source (Alice) sends confidential information to a destination (Bob) via an energy-constrained UAV-mounted amplify-and-forward (AF) relay in the presence of a ground eavesdropper (Eve). We adopt destination-assisted cooperative jamming (CJ) as well as simultaneous wireless information and power transfer (SWIPT) at the UAV-mounted relay to enhance physical-layer security (PLS) and transmission reliability. Assuming a low altitude UAV, we derive connection probability (CP), secrecy outage probability (SOP), instantaneous secrecy rate, and average secrecy rate (ASR) of the proposed protocol over Air-Ground (AG) channels, which are modeled as Rician fading with elevation-angel dependent parameters. By simulations, we verify our theoretical results and demonstrate significant performance improvement of our protocol, when compared to conventional transmission protocol with ground relaying and UAV-based transmission protocol without destination-assisted jamming. Finally, we evaluate the impacts of different system parameters and different UAV's locations on the proposed protocol in terms of ASR.

Measurement-Based Characterization and Modeling for Low-Altitude UAV Air-to-Air Channels

In this paper, we characterize the unmanned aerial vehicle (UAV) air-to-air (A2A) channel for an 802.11-based low altitude network based on a preliminary measuring result over user datagram protocol (UDP) throughput, latency, received signal strength indicator (RSSI), and pack loss, via various field experiments. Latency and pack loss are strongly related to the stability of flight; while the UDP throughput and RSSI shows a related variation trend in terms of the communication distance and altitude. A modified model based on the classical log-distance path loss (PL) model is introduced to describe propagation path loss, demonstrating the decreasing trend of multi-path effect and shadow fading with an increase in altitude. The simulation work is done to specifically show the characterization of A2A channels based on UAVs in short distance and low altitude environment.

Compensation of range-dependent phase error in low-altitude unmanned aerial vehicles synthetic aperture radar by sparse reconstruction

Unmanned aerial vehicles (UAV) are a useful supplement to traditional synthetic aperture radar (SAR) platforms. In some cases, UAV-based SAR systems have to fly at low altitude. In this case, range-dependent phase errors due to platform motion affect the imaging quality. To solve the problem of motion compensation, an angle-dependent model and a second-order range-dependent model are introduced into autofocusing by previous researchers, but the first one relies too much on the geometric angle while the latter has limited fitting order for solution. We present a higher order range-dependent model, which can approximate analytical solution. Nevertheless, an increase in the fitting order makes the matrix in this model underdetermined. Based on the theoretical proof, this higher order model can be tackled by exploitation of compressive sensing (CS) theory. A CS reconstruction of higher order fitting coefficients is performed in the experiments, and corresponding performance analysis is given. Finally, the range-dependent phase error is compensated under the condition of low altitude.

Rice yield estimation based on K-means clustering with graph-cut segmentation using low-altitude UAV images

Predicting the harvest yield enables farm practices to be modified throughout the growing season, with potential to increase the final yield. Unmanned aerial vehicle (UAV) based remote sensing is a promising way to estimate crop yields. In this study, rice yield was estimated by segmenting grain areas using low altitude RGB images collected using a rotary-wing type UAV. In particular, an image processing method that combines K-means clustering with a graph-cut (KCG) algorithm was proposed to segment the rice grain areas. The graph-cut algorithm was applied to extract the foreground and background of the images. The foreground RGB images were converted to the Lab colour space and then K-means clustering was used to label pixels based on colour information. The area of the rice grains in the images was calculated from the clustered images. Using this grain area information, the rice yield of the field could be estimated. Experiments show that the proposed method can segment the grain areas with a relative error of 6%–33%, and it improved the relative error of the previous method (by 1%–31%). The coefficient of determination between the results of the proposed method and the ground truth was found to be 0.98. Furthermore, the relative error of the yield estimation for four field sections was 21%–31%. The results indicate that the UAV image-based grain segmentation has the potential to estimate rice yield accurately and conveniently.

Opportunistic relaying for low-altitude UAV swarm secure communications with multiple eavesdroppers

In this study, we investigate the secrecy outage performance achieved by opportunistic relaying for a low-altitude unmanned aerial vehicle (UAV) swarm secure communication system in the presence of multiple UAV-eavesdroppers. In the primary channel, multiple UAV-transmitters are connected to a ground station via the ground-to-air (G2A) wireless backhaul and a UAV-transmitter and UAV-relay are selected to transmit the source signal to a far ground destination under air-to-air and air-to-ground Nakagami-m fading links. In the eavesdropping channel, maximum ratio combining is applied across multiple UAV-eavesdroppers for intercepting the legitimate transmissions from both the selected UAV-transmitter and UAV-relay. The backhaul reliability and eavesdropping probability are introduced to reflect the practical constraints on the G2A wireless backhaul and UAV-eavesdropper cooperation, respectively. The closed-form expression for the secrecy outage probability is derived in terms of the UAV cooperation, backhaul reliability, eavesdropping probability, and Nakagami-m fading parameters. In the high signal-to-noise ratio region, the asymptotic secrecy outage probability is also derived. It is shown that the secrecy diversity gain achieved by opportunistic relaying is jointly determined by the UAV cooperation and shape factors of Nakagami-m fading links in the primary channel. The analytical secrecy outage metrics achieved by opportunistic relaying are verified by Monte Carlo simulation results.

DEVELOPMENT OF UAV AIR ROADS BY USING 3D GRID SYSTEM

Abstract. With the drastic development of low-altitude UAV (Unmanned Aerial Vehicle) technology, UAV will be used for long-distance logistics in the near future. Many countries begin to develop UTM (UAV Traffic Management) system, and one of the objectives for the system is preparation of UAV-logistics era. In that era, hundreds of drone will simultaneously fly at one area. To prevent UAV collision in the air, UAV air road should be designed. The Korean government have supported research projects related with UAV air roads. This paper deals with development of UAV air roads by using 3D grid system. First, detail 3D spatial information for UAV air roads is constructed. In many cases, 3D digital map does not include transmission towers, utility poles, power lines, or trees, since the interests of 3D digital map are focussed on digital elevation model and digital surface model with buildings. The transmission towers, utility poles, and power lines could be obstacles when UAV perform its logistics mission. Therefore, detail 3D information should be constructed for UAV air roads. We constructed such detail 3D information by using MMS (Mobile Mapping System) and aerial survey with Lidar and digital photograph. Next, 3D grid system is proposed to present such detail 3D information. Usual object based 3D information is huge size and hard to control. To provide 3D information to a flying UAV, data should be light. Therefore, light-weight 3D grid system is effective to provide air road information to UAV. Proposed 3D grid based air roads can be used for UAV flight plan, traffic management etc.

Improved estimation of rice aboveground biomass combining textural and spectral analysis of UAV imagery

Crop aboveground biomass (AGB) is one of the most important indicators in crop breeding and crop management, and can be used for crop yield prediction. A number of vegetation indices (VIs) have been proposed to estimate crop biomass, but they perform poorly at high biomass levels and are easily affected by background materials. Texture analysis has been proved to be an efficient approach in forest biomass estimation, but has never been applied to crops with low-altitude unmanned aerial vehicle (UAV) images. The objective of this study was to improve rice AGB estimation by combining textural and spectral analysis of UAV imagery. A two-year rice experiment was conducted in 2015 and 2016, involving different nitrogen (N) rates, planting densities and rice cultivars with three replicates. A six-band multispectral (MS) camera was mounted on a UAV to acquire rice canopy images at critical stages during the rice growing seasons and concurrent field samplings were taken. Simple regression and stepwise multiple linear regression models were developed between biomass data from the two-year experiment and image parameters derived from four different types of feature sets. These features represented commonly used VIs, texture parameters, normalization of texture measurements (normalized difference texture index, NDTI) and combinations of VIs and NDTIs. Finally, all the regression models were evaluated by cross-validation over pooled data with the coefficient of determination (R2) and the root mean square error (RMSE). Results demonstrated that the optimized soil adjusted vegetation index (OSAVI) exhibited the best relationship with AGB for the whole season (R2 = 0.63) and post-heading stages (R2 = 0.65). Red-edge-based indices yielded best performance (R2 > 0.70) only for the growth stages before heading. The texture measurement mean (MEA) from the NIR band was the best among the eight candidates in AGB estimation. Texture index (NDTI (MEA800, MEA550)) was superior to all the evaluated VIs in estimating AGB for the whole season (R2 = 0.75) and pre-heading stages (R2 = 0.84). Further improvement was obtained across the whole season by combining NDTIs and VIs through a multiple linear regression. This multivariate model produced the highest estimation accuracy for all stages (R2 = 0.78 and RMSE = 1.84 t ha−1) and different stage groups (R2 = 0.84 and RMSE = 1.06 t ha−1 for pre-heading stages and R2 = 0.65 and RMSE = 1.94 t ha−1 for post-heading stages). The findings imply that the integration of textural information with spectral information significantly improves the accuracy for rice biomass estimation compared to the use of spectral information alone.