Soybean, a pivotal global source of oil and protein, exhibits heightened sensitivity to soil moisture conditions throughout its growth cycle. Accurate monitoring of soil moisture (SM) in soybean fields during the growing season is indispensable for optimizing yields and forecasting sustainable agricultural practices. Leveraging advancements in remote sensing technology, passive microwave soil moisture retrieval has emerged as a crucial tool for large-scale precision agriculture and enduring environmental monitoring. However, challenges in the L-band Microwave Emission of the Biosphere (L-MEB) model, particularly in the computation of vegetation transmissivity, may compromise the accuracy of soil moisture retrieval. In this study, we improved the Beer-Lambert law to more accurately quantify the attenuation effect of the vegetation layer on microwave signals, aiming to ameliorate the inherent limitations in the L-MEB model. The proposed soil moisture retrieval method, primarily validated in soybean fields, was also subjected to supplementary experiments in canola and wheat fields to further assess its effectiveness and generalizability. The proposed method integrates passive microwave and optical data, demonstrating a substantial improvement in accuracy. Experimental results reveal that our enhanced method significantly outperforms the L-MEB model in soybean fields: Pearson correlation coefficients of soil moisture, derived using vegetation water content and leaf area index, are 0.712 and 0.692 respectively. Furthermore, root mean square errors have decreased to 0.056m3/m3and 0.050 m3/m3, a reduction of 39.78% and 19.35%, respectively. In canola and wheat fields, the method exhibited an approximate 10% enhancement in retrieval accuracy. This advancement not only furnishes novel technical support for water management in soybean cultivation but also contributes theoretical and technical insights to the domain of passive microwave soil moisture retrieval. Index Terms-L-MEB model, passive microwave, soil moisture retrieval, vegetation transmissivity.

As a crucial crop for oil and protein, soybean is highly sensitive to soil moisture during growth. Therefore, it is crucial to accurately monitor soil moisture over soybean fields during the growth period. In the L-band Microwave Emission of the Biosphere (L-MEB) model, the calculation of vegetation transmissivity may have some limitations, potentially affecting soil moisture retrieval accuracy. To improve the effectiveness of the retrieval, we introduced the improved Beer-Lambert law to describe the attenuation effect of the vegetation layer on the microwave signals more accurately, thus improving the L-MEB model. The experimental results indicate that our improved method significantly enhances the accuracy of soil moisture retrieval over soybean fields: the correlation coefficients of soil moisture results retrieved using vegetation water content and leaf area index improved by 0.9% and 9.5%, respectively, and the root mean square errors decreased by 39.78% and 19.35%, respectively.

Enhanced sensor technology and advanced control algorithms have expanded the use of autonomous Unmanned Aerial Vehicles (UAVs) in critical sectors such as power inspection. However, the limitations in positioning accuracy and onboard computational power constrain the robustness and versatility of UAV motion planning algorithms in outdoor environments. Therefore, we enhanced the existing generalized UAV obstacle avoidance pathfinding methods for application in electric power inspection, ensuring effective performance despite limited GPS signal quality and computational power. Initially, we delineate impassable areas by marking the non-collision space beneath obstacles at a specific height based on actual requirements. Next, environmental factors are integrated into the assessment of local target points, mitigating risks of UAVs encountering obstacles during trajectory planning. Finally, by discretizing the output B-spline trajectory with node fitness, we tightly couple the UAV's real-time position with the initiation of trajectory replanning. Experimental results confirm the method's robustness and efficiency.

Soil moisture is of great importance for regional hydrological studies such as agricultural management and drought prediction, but those applications usually require a high spatial resolution of 1-10 kilometers. To improve the spatial resolution of satellite soil moisture products, this paper constructs a soil moisture downscaling method in Apparent Thermal Inertia- Leaf Area Index (ATI-LAI) space using an improved automatic edge determination algorithm. The effectiveness and robustness of the method are validated by the Australian Soil Moisture Monitoring Network and ESA Climate Change Initiative (ESA CCI) soil moisture data. By comparing with in situ SM measurements and the spatial patterns of the CCI SM, the R of the downscaled 1km SM reaches 0.694 with a bias of 0.027. The results show that the downscaled 1km SM significantly improves the spatial details of the CCI SM while replicating the accuracy of the CCI SM, and demonstrating fine-scale spatial variability.

Leaf area index (LAI) is a critical plant biophysical parameter required for modelling plant photosynthesis and crop yield estimation. UAV remote sensing plays an increasingly significant role in providing the data source needed for LAI extraction. This study proposed a method that automatically calculate crop effective LAI (LAIe) using UAV-based 3-D point cloud. The porosity and projection function of crops from different zenith perspectives was estimated using three-dimensional perspective. Then the LAIe was calculated using the Beer Lambert law. The result shows a good linear correlation between the calculated LAIe and the field LAI measured by digital hemispherical photography method, and R2is 0.64. The method presented in this paper performs well in LAIe estimation of main leaf development stages of winter wheat growth period. It offers an effective means for mapping crop LAIe without reference data and saves time and cost.

Leaf area index (LAI) serves as a key ecophysiological parameter for assessing plant growth and is particularly vital for crop monitoring. Using unmanned aerial vehicle (UAV)-based point cloud data generated through photogrammetry techniques offers valuable structural insights into crops, facilitating LAI retrieval. This study introduces a method for estimating LAI from 3-D point clouds. By employing spherical voxel partitioning, the vegetation gap fraction is computed based on the spatial distribution of point clouds. Furthermore, the leaf inclination angle is determined through triangular patch collections reconstructed from 3-D point clouds. Projection functions, accounting for varying zenith perspectives, are developed considering the leaf inclination angle. Subsequently, the combination of vegetation gap fraction and projection functions is used within the Beer–Lambert law framework to calculate LAI. Validation against ground measurements demonstrates a strong correlation between measured and retrieved LAI ($R^{2} = 0.64$, RMSE = 0.43), affirming the effectiveness of the proposed method in estimating LAI using UAV-based structure from motion (SfM) point cloud data.

Rice false smut (RFS) is a late fungal disease mainly occurring on rice panicle in recent years. This research was based on the unmanned aerial vehicle (UAV) hyperspectral remote sensing data. On the basis of genetic algorithm combined with partial least squares to select the feature bands, the correlation coefficient method and Instability Index between Classes method were used to further select the feature bands, which further eliminated 27.78% of the feature bands when the model monitoring accuracy was improved overall. The prediction accuracy of Gradient Boosting Decision Tree model and Random Forest model was the best, which were 85.62% and 84.10% respectively, and the monitoring accuracy was improved by 2.22% and 2.4% compared with that before optimization. Then, based on the UAV hyperspectral data and the characteristic bands, the sensitive band ranges of rice false smut monitoring were determined, which were 698nm-750nm and 974nm-984nm.

Automatic extraction of crop row centerline is an important technology for agricultural automation, and it has a wide range of applications in automated operations, such as automatic agricultural navigation, automatic harvesting, automatic weeding and automatic seedling replenishment. In this study, the method of row centerline detection is proposed by combining image segmentation and the technique of feature point extraction, and it is applied to the extraction of corn missing seedling locations. Firstly, image segmentation is performed by combining the improved vegetation index ExGG and a double-threshold algorithm (the OTSU method combined with the Particle Swarm Optimization algorithm), and most of the pseudo-feature points are removed using median filtering to initially separate corn seedlings from weeds and soil. Then, the number of crop rows is obtained using the vertical projection method; the micro-region of interest(micro-ROI) is used to find the center of mass and extract the feature points. Finally, the remaining pseudo-feature points are removed by the location clustering method, and the crop row centerline is fitted using the linear regression method of least squares. This study extracts the location and number of missing seedlings of corn based on the information from the row centerline, providing technical support for the subsequent seedling replenishment operation. The experimental results show that the accuracy of the proposed method for detecting the centerline of corn seedling rows is 0.016°, which is better than the Hough transform.

Soil moisture content (SMC) is a significant factor affecting crop growth and development. However, SMC estimation based on synthetic aperture radar (SAR) will be influenced by a variety of surface parameters, such as vegetation cover and surface roughness, which are usually difficult to measure. In order to retrieve the SMC across agricultural areas (such as wheat fields) without ground measurement. In this study, a model-based polarization decomposition method was used to decompose the original SAR signal into different scattering components representing different scattering mechanisms. Then different volume scattering models were used and compared to remove the scattering contribution from vegetation canopy, so as to extract the surface scattering component related to the soil moisture. Finally, combined with the extensively used surface scattering model (CIEM) and the method of roughness parameters optimization, the look-up table method was used to estimate the soil moisture during wheat growth period. The achieved$\mathrm{R}^{2}$and RMSE of the SWC are 0.534, 5.62 vol. %, which indicates that this approach has a good estimation performance on the soil moisture under wheat during its growing period.

Height is a key factor in monitoring the growth status and rate of crops. The point cloud generated by the Structure from Motion (SfM) algorithm based on Unmanned Aerial Vehicle (UAV) images can quickly estimate the crop height in the target area at a lower cost. However, crop leaves started to cover the ground gradually from the beginning of the stem elongation stage, making more and more ground points below the canopy disappear in the data. Therefore, the terrain undulations in the target area and outliers in the point cloud will seriously affect the height estimation accuracy. This paper proposed a new method to estimate the height of winter wheat based on UAV point cloud. Random Sample Consensus (RANSAC) was applied to obtain the ground points from the point cloud. Then, the missing ground points were fitted according to the known ground points. Our approach achieved crop height monitoring with an R2 of 0.76. Fitting the missing ground points simulated the terrain undulations effectively and improved the accuracy of estimated crop height.

Crop yield is a key factor in agricultural production management and agricultural policy formulation, which affects the stability of the country and society. Therefore, accessing the spatial distribution of crops in real time is very important. In this study, multi-temporal RADARSAT-2 fine beam quad-polarized SAR data was obtained, and a crop classification method based on similarity analysis of phenological features and image segmentation technology was proposed. The main idea of this method is to construct a standard phenological feature sequence (SPFS) based on backscatter coefficient and Cloude decomposition parameters for each crop by using the training data, and then the similarity coefficient between the segmented test data and the phenological sequence of each crop is calculated to judge the type of ground objects. The results show that the overall classification accuracy based on block scale reached to 76.06%, which indicates that the phenological features and image segmentation is beneficial for accuracy improving in crop classification. And the multi-temporal SAR data own great potential in agricultural monitoring.

Chlorophyll is an essential pigment for photosynthesis of crops, which indicates the growth status of crops. Accurate and robust information on the spatial dynamics of chlorophyll content is of critical importance for crop growth status assessment and corresponding response activities. However, previous studies mainly focus on the methodologies based on in situ hyperspectral data, which are not applicative for regional chlorophyll content mapping. In this content, Unmanned Aerial Vehicle (UAV) based hyperspectral imagery, with high spatial and spectral resolution, may provide the spatial distribution of chlorophyll content accurately over crop fields. In this study, an empirical model between wavelet features, derived from UAV based hyperspectral imagery by continuous wavelet transform (CWT), and chlorophyll content (measured by a portable soil-plant analysis development meter) is proposed by support vector regression (SVR). The results suggest that the UAV based hyperspectral imagery combined with CWT demonstrates good performance in rice chlorophyll content estimation with R and RMSE of 0.81 and 3.51, respectively. Moreover, the wavelet coefficients corresponding to two bands at red (640nm, 628nm) and one band at green (548nm) are the most effective wavelet features to estimate chlorophyll content of rice.

In recent years, the method of obtaining ground object properties by processing and analyzing a 3D point cloud is prevalent. In this paper, a tree height estimation algorithm based on a 3D point cloud is proposed. This algorithm can calculate the height of trees in sparse scenes in batches, and it is also efficient. The key of the algorithm is to obtain the accurate point cloud cluster of the monomer tree. We achieve this by using a refined Density-based spatial clustering of applications with noise (DBSCAN). Then we estimate the ground height and treetop height, the height of the tree is their difference. In order to reduce the number of point clouds and maintain the overall structure, Voxel Grid filtering is also used. We apply this method to the 3D obtained point cloud, and good results prove our excellent work. Our code and point cloud data are available at https://github.com/yzfly/SimpleTreeHeight.

Paddy rice is one of the most important crops in the world. Information of the area and spatial distribution of paddy rice is significant for food security, water resources management, and greenhouse gas (methane) emissions. Paddy rice field is characterized by an initial period of flooding and transplanting, during which period open canopy (a mixture of rice crops and surface water) exists. The present algorithms for paddy rice recognition are mainly based on the unique physical features of paddy rice and the vegetation indices, which are sensitive to dynamics of the canopy and surface water content. In this study, the rice growth calendar data and the cropping pattern (e.g., single rice or double rice) information were used to improve present phenology-based rice recognition approach by determining the potential temporal window of flooding and transplanting periods over a year. Other land cover types (e.g., snow, evergreen vegetation and permanent water bodies) with potential influences on paddy rice identification were removed (masked out) due to different temporal profiles. The results showed that the estimated rice area is in line with the provincial agricultural statistics with R2and slope are 0.951, 1.049, respectively, and the relative errors of the different province are between -15.81% to 25.75%.

The objective of this study is to investigate the potential of multitemporal remote sensing images for crop classification. Multi-temporal Landsat 8 OLI/TIRS C1 Level-1 images were acquired. The surface reflectance of visible and near infrared bands was used to represent the characteristics of crops. A time series model of surface reflectance was constructed for crop classification. Cloud cover is critical for the accuracy of classification. In order to remove the influence of clouds, the cloud pixels were neglected by setting a constant. Pearson correlation coefficient was used in the time series model of surface reflectance to classify the crop type. Finally, the overall accuracy reaches 78.26% and Kappa reaches 71.33%. Therefore, the method has the operational potential for crop classification even in the special area with cloudy or foggy weather.

Fuel moisture content (FMC) is a critical variable in assessing wildfire risk and its behavior. Previous studies normally focused on the methodologies based on optical remote sensing data for FMC retrieval. However, active microwave technique, which processes the advantage of high sensitivity to surface moisture, all-weather and all-time work capability and strong penetrability, attracted more attention in surface parameter monitoring, particularly for the polarimetric SAR which provides more sufficient object scatter characteristic. In this paper, we retrieved the FMC for a grassland based on the multiple linear regression analysis of polarimetric decomposition parameters from Radarsat-2 data. The results show that the correlation coefficient (R) and root mean square error (RMSE) reached to 0.658 and 30.319% when compared to the measured FMC. Finally, the presented method was used for spatial and temporal mapping of FMC in the target study area.

Spatiotemporal monitoring of fuel moisture content (FMC) is vital to assessing the wildfire risk and its behavior. Optical remote sensing data-based FMC estimation have been wildly explored in previous studies. However, limited studies focused on FMC retrieval from the active microwave technique represented by synthetic aperture radar (SAR) data, which processes the advantage of higher sensitivity to surface moisture and better all-weather and all-time work capability than optical data. This is the first study to assess the performance of time series dual-polarization Sentinel-1A data for FMC estimation from coupled the bare soil backscatter Linear Model and the vegetation backscatter Water Cloud Model. The results show that the simulated backscattering coefficients and FMC are in line with the measured Sentinel-1A data and FMC with R2and RMSE are 0.549, 0.354 dB, and 0.543, 13.579 %, respectively.

Spatiotemporal simulation of Fractional Vegetation Coverage (FVC) is of great significance for the protection and management of the ecological environment. In this study, the growing season FVCs of the South Qilian Mountains from 2000 to 2017 were extracted from the MODIS vegetation indices product (MOD13Q1), and then that were used to train the Back Propagation (BP) artificial neural network to estimate the annual FVCs of the South Qilian Mountains in the next 7 years (2019-2025). The results show that the established model has a good performance through verification FVC data in 2018, with the coefficient of determination (R2) is 0.9462 and the root mean square error (RMSE) is 0.0118. The simulation results of the model indicate the FVC will present a trend of growth in the following years. This study indicates that the combination of BP neural network and remote sensing data can effectively simulate the spatiotemporal pattern of fractional vegetation coverage, which can further contribute to the environmental protection.

This paper studies the applicability of multi-temporal synthetic aperture radar (SAR) images in detecting urban land use types change. In this paper, the study area is 128×358 pixels covers Shuangliu International Airport, Chengdu, China. Nine scenes of ALOS-PALSAR HV images from July 2007 to October 2010 were collected. the logarithmic ratio operator was used to generate the intensity and texture feature difference images. Texture features were extracted by the gray level co-occurrence matrix (GLCM). Then the redundant difference information was compressed by PCA transformation. The index of dynamic change image was generated to represent the change in land use type in Chengdu Shuangliu International Airport.

Tianjin city, China has seen rapid urban expansion and economy development especially since the Chinese reform and opening up in 1978. Understanding their internal relationship is important for urban management. Considering of the long-history records with medium spatial resolution (30 meters), Landsat time series (LTS) has become a key remote sensing dataset for monitoring urban changes. As the impervious surface is an important indicator for assessing urban environment, in this study, we extracted the impervious surface maps in Tianjin between 1990 and 2017 based on LTS using a Continue Change Detection and Classification (CCDC) algorithm. On the other hand, we quantitatively explored the relationships between the impervious surface and Gross National Product (GDP). Results show that the impervious surface expansion in Tianjin experienced two stages between 1990 and 2017 with the area increased by 521.95 km2, and the correlation coefficient between the area of impervious surface and GDP value was as high as 0.9718.

An important prerequisite for improving the classification accuracy is to fully extract the characteristics that reflect physical properties of the objects. The objective of this study is to investigate the capability of quad polarized Synthetic Aperture Radar (SAR) images for crop classification in Ontario, Canada. Multi-temporal RADARSAT-2 fine beam quad-polarized SAR data were acquired. A support vector machine (SVM) classifier was selected for the classification using combinations of the polarization characteristics and texture features. The polarimetric features, including odd scattering, double scattering and volume scattering, were extracted from classic Pauli decomposition. Eight texture features were extracted from grey level co-occurrence matrix (GLCM). Principal Component Analysis (PCA) method was applied to reduce the redundancy of texture features. The results indicated that multi-temporal SAR data achieved satisfactory classification accuracy. Texture features of SAR data were useful for improving classification accuracy. SAR data have considerable potential for agricultural monitoring.

This reported study was intended to advance the retrieval of leaf area index (LAI) using synthetic aperture radar (SAR) data. A novel method was proposed by introducing the vegetation coverage into the Water Cloud Model (WCM) to improve the retrieval accuracy of the LAI. LAI is a strong indicator of crop productivity, and vegetation coverage has a strong relationship with the LAI (R2=0.9733), a function can be created to express their relation. Finally, the accuracy in this innovative LAI retrieval method were evaluated. The results showed that the accuracy of estimation was improved greatly (R2was increased to 0.6055 and 0.6422 from 0.3491 and 0.3561 in VH and HH polarization). Thus, the method has operational potential for the LAI retrieval of crop in agriculture regions.

This paper examines the potential of Radarsat-2 C-band synthetic aperture radar (SAR) data for quantifying the spatial variability of soil moisture during the agriculture growth period. To remove the effect of crop within total backscattering, a method that adequately represents the scattering behavior of vegetation-covered area by defining the scattering of the vegetation and underlying soil was developed. The Dubois model was employed to determine the backscattering from the underlying soil. The modified Water Cloud Model was used to reduce the effect of backscattering caused by the vegetation. Soil moisture was derived by the inversion scheme which uses of the dual polarizations (HH and VV) available from the quad polarization Radarsat-2 data.

The retrieval of parameters through a physical mechanism model is promising for its generality but is challenged by the ill-posed inversion problem. This study focused on the use of multiple priori information to alleviate the ill-posed inversion problem. The priori information included the products of satellite images, the correlations among model free parameters, field survey, and the achievements of previous studies. However, the priori information was of uncertainty, which was described by multi-variables probability distribution in this study. A Bayesian network algorithm was used to retrieve the canopy water content (CWC) by calculating the posterior probability distribution of CWC based on the priori information, the HJ-1B product, and the PROSAIL model. The retrieval results showed that the R2 = 0.83 and RMSE = 0.18 compared to the field measured CWC, which confirmed the feasibility to alleviate the ill-posed inversion problem by the multiple priori information.

A synergistic method of SAR and optical remote sensing data for retrieval of soil moisture was developed in this paper. Vegetation coverage, which can be easily estimated from optical data, was combined in the backscattering model. The total backscattering was divided into the amount attributed to areas covered with vegetation and that attributed to areas of bare soil. Backscattering coefficients were simulated using the established backscattering model. Then, soil moisture was estimated using the inverted model. The results showed that the predicted soil moisture correlated with the measured soil moisture (R2= 0.7075, RMSE = 3.3219 m2/m2).

Rocky desertification is a type of land desertification. It comes from the fragile ecological and geological environment, where the human activity is very strong and the land productivity is degraded severely. As a natural disaster, rocky desertification is very destructive, and it is very difficult to be recovered. The karst region of China in southwest is the world's concentrated karsts region. It is also one of the largest contiguous karsts regions. The karst region is also the most typical ecological fragile regions in China. We utilized the ETM+ images in 2000 to study the rocky desertification of the north regions in Guangxi province in the past ten years. Firstly, the rocky exponential model was established to extract rocky desertification information of the region. Then, the RGB image was composited to interpret and obtain the rocky desertification. Our experiment showed that rocky desertification of the karst region can be classified into no rocky desertification, moderate desertification, and severe rocky desertification.

Leaf area index (LAI) is one of the key vegetation indices for many biological and physical processes in plant canopies. In this study, an assimilation technique was used to simulate the LAI's varying in time series in an alpine wetland located in western China. The Terra MODIS 16 day composite surface reflectance products at 250 m resolution in 2010 with high quality were used. LAI was retrieved based on the ACRM canopy reflectance model and LUT algorithm. An experiential LOGISTIC model was fitted using the retrieved LAI, and the ensemble Kalman filter algorithm was introduced to assimilate the estimated LAI into the LOGISTIC model to update the model state.