The accurate and high-throughput extraction of phenotypic traits is of great significance for crop breeding and growth monitoring. The segmentation of structural components (e.g. leaves and stems) is a prerequisite for extracting phenotypic traits. In the past decade, there has been an increase in methods attempting to separate leaves and stems in point clouds. However, previous researches mainly focus on plants at the individual level due to the interlocked and overlapped nature of leaves and the bottleneck existing for field plants to extract phenotypic traits. To address this issue, a novel two-stage leaf-stem separation model encompassing the initial separation of leaves and stems and optimization is presented in this paper. The model is based on the different geometric features of leaves and stems of maize plants defined by neighborhood points, and a cylinder is used to find the neighborhood points by considering the elongated characteristic of maize stems. After that, another elongated cylinder (0.5m high and 0.02m diameter) is used to traverse the stem points to optimize the initially separated results. Maize plants with the planting density of 45,000 plants/ha in the filling stage (Exp. 2019) were used to train and test the model in the initial separation step (Experiment 1), showing that the separation accuracy could be up to 91.3%. It was concluded that a 0.11m high and 0.07m diameter cylinder was the optimal searching parameter for the initial separation, and 0.25m was the optimal threshold for optimization. We also tested the transferability of the model (Experiment 2) for maize plants with different planting densities (45,000, 67,500, 90,000, and 105,000 plants/ha), different growth stages (jointing, silking and filling), and point clouds collected using multiple platforms (Terrestrial Laser Scanning (TLS), LiDAR Backpack (LiBackpack), and Unmanned Aerial Vehicle-Light Detection and Ranging (UAV-LiDAR)), suggesting that the model performed well for all the datasets. In addition, the simulated datasets of maize with different planting densities were used to assess the model performance at the point level, showing the separation accuracy were 0.92, 0.91, 0.91, and 0.90 for maize with the planting densities of 45,000, 67,500, 90,000, and 105,000 plants/ha, respectively. The proposed model in this study is innovative, and it has promising prospects for the high-throughput extraction of the phenotypic traits in field maize plants and could facilitate genotype selection in crop breeding and three-dimensional (3D) plant modeling.

Distributions of leaf base and inclination angles are important crop phenotypic traits, influencing light interception and productivity. Light detection and ranging (LiDAR), and especially Terrestrial laser scanning (TLS), provides unprecedented detail of the three-dimensional (3-D) structure of the crop canopy. Recent research mainly focuses on the leaf base and inclination angles of maize at the individual level or at lower planting density. It is difficult to extract the distributions of leaf base and inclination angles of maize in the field due to the interlocked and overlapped nature of leaves. In this study, we have proposed a high-throughput method to extract the distributions of leaf base and inclination angles of maize in the field. Following the separation of the leaf and stem of maize, hollow cylinders with different thicknesses were used to extract the local leaf points from the separated leaf points based on each stem fitted line, and the Density-based spatial clustering of applications with noise (DBSCAN) algorithm and singular value decomposition were used to calculate the leaf base and inclination angles. The distributions of leaf base and inclination angles of maize in the field with different cultivars (Jingjiuqingchu 16 (A1), Tianci 19 (A2), Jingnuo 2008 (A3), Nongkenuo 336 (A4), and Zhengdan 958 (A5)), planting densities (3.32 plants/m2, 4.65 plants/m2, 6.64 plants/m2, and 8.63 plants/m2), and growth stages (jointing, silking, and filling stages) were extracted and analyzed, and these performed well against the validation data. In addition to TLS data, the extraction of the distributions of leaf base and inclination angles based on LiBackpack and UAV-LiDAR data was also discussed. This further validated the potential of the method proposed in this study for the extraction of the distributions of leaf base and inclination angles of maize in the field. Furthermore, the relationship between maize with different leaf base angle distributions and the daily cumulative APAR (Absorbed Photosynthetically Active Radiation) was analyzed, which demonstrated that compact maize cultivars exhibited higher light interception capabilities than scattered ones under high planting densities. The distributions of leaf base and inclination angles exert a substantial influence on the light interception capacity of maize, thereby exerting a consequential effect on maize yield. The high-throughput extraction of these distributions in maize fields holds significant importance for studying the optimal maize cultivar in conjunction with radiative transfer models.

The accurate large-scale measurement of peach crowns is vital in horticultural science and the optimization of orchard management. Nowadays, numerous crown parameters (e.g., crown area, height, and volume) can be obtained via the analysis of point clouds or photographs. Current laser-based sensors provide the required reliable and accurate information; however, they are costly and time-consuming. Therefore, a simpler approach for crown measurement is required. For this purpose, this study presents a pipeline for the monitoring and clustering of 259 peach tree crowns based on unmanned aerial vehicle (UAV) images of a peach orchard in Southeast China. Considering the limitation that the original aerial image dataset contains little information, a data augmentation process is adopted, and an efficient deep learning architecture based on conditional generative adversarial networks (cGANs) was designed to extract the crown area. Then, the shape of the crown area was clustered using an edge detection process and a$k$-means algorithm. Finally, an ellipsoid volume method (EVM) was applied to estimate the crown volume. Five indicators—namely,$Q_{\mathrm {seg}}$,$S_{\mathrm {r}}$, Precision, Recall, and F-measure—were employed to evaluate the crown extraction effects, and the average results for testing samples were 0.832, 0.847, 0.851, 0.828, and 0.846, respectively. Compared with other approaches—namely, fully convolutional network (FCN), U-Net, SegNet21, the excess green index (ExG), and the color index of vegetation extraction (CIVE)—the proposed cGAN model performs better, achieving an accuracy improvement of 5%–25%. For the estimation of crown volume, using measurements from a light detection and ranging (LIDAR) scanner as a reference, the correlation coefficient and relative-root-mean-square error (R-RMSE) were found to be 0.836% and 14.93%, respectively. Overall, the results demonstrate that the proposed method is feasible for measuring peach tree crowns. The wide application of such technology would facilitate applied research in plant phenotyping and precision horticulture.

Leaf base and inclination angles are two critical 3-D structural parameters in agronomy and remote sensing for breeding and modeling. Terrestrial laser scanning (TLS) has been proven to be a promising tool to quantify leaf base and inclination angles. However, previous TLS studies often focused on leaf base and inclination angles of certain trees or plants with flat leaves, such as European beech. Few studies have worked on leaf base and inclination angles of maize plants due to their curved and elongated characteristics. In this study, a machine learning-based [support vector machine (SVM)] method and a structure-based [skeleton extraction (SE)] method were presented to extract the leaf base and inclination angles of maize plants. After separating individual leaf points from the complete point cloud and skeleton points of maize plants and then extracting geometric features, the machine learning- and structure-based methods were used to calculate leaf base and inclination angles. Our results show that the leaf base and inclination angles extracted using these two methods agreed well with ground truth, and the estimation accuracy of the machine learning-based method was obviously higher than that of the structure-based method. The mean absolute error (MAE), root-mean-squared error (RMSE), and relative RMSE (rRMSE) of the leaf base and inclination angles using the machine learning-based method were 4.56°, 6.17°, and 19.04% and 7.95°, 10.00°, and 20.24%, respectively; and those from the structure-based method were 6.22°, 7.47°, and 23.30% and 8.99°, 12.57°, and 25.85%, respectively. The machine learning-based method was also applied to a field with dense mature maize, and their MAE, RMSE, and rRMSE were 6.04°, 8.12°, and 25.90% and 11.30°, 13.52°, and 26.5%, respectively. It is demonstrated that both the machine learning- and structure-based methods are effective to estimate the leaf base and inclination angles of maize plants, although the machine learning-based method appears to outperform the structure-based method.

Fast and accurate counting of wheat ears in field conditions is a key element for determining wheat yield. To obtain the number of wheat ears in a field, we propose a new counting algorithm based on computer vision. This algorithm counts wheat ears in remote images through semantic segmentation regression network (SSRNet). SSRNet is a multistage convolutional neural network that we propose to achieve counting problems through regression. In SSRNet, first, the original image is cropped to increase the amount of data. This method effectively solves the small sample dataset. Next, based on the cropping results, we build a fully convolutional neural network (FCNN) to segment wheat ears in field conditions. FCNN increases the accuracy of wheat ears counting by accurately segmenting wheat ears in a complex background. Then, we build a regression convolutional neural network (RCNN) to count wheat ears based on the segmentation results of FCNN. In RCNN, we propose a new activation function positive rectification linear unit (PrLU) to process the last layer of the fully connected layer, so that RCNN can effectively count the number of wheat ears in the image. Finally, a counting strategy is proposed to count the number of wheat ears in the original image. To verify the counting performance of SSRNet, we compare the counting result of SSRNet with the real value of manual statistics. The results show that the average accuracy (Acc),$R^{2}$, and root mean squared error (RMSE) of the SSRNet count results on the test set in this article are 0.980, 0.996, and 9.437, respectively. It can be seen from the results that our proposed method can accurately count wheat ears in field conditions. At the same time, the counting time (0.11 s) shows that SSRNet can quickly estimate the number of wheat ears in field conditions. We concluded that this study can provide important technical support for the high-throughput field wheat ears counting task in large-scale phenotyping work.

Rapid and accurate estimation of the winter wheat leaf area index (LAI) is important for evaluating its growth and estimating yield. In this paper, Optimal Index (OI) was used to screen the best combination of hyperspectral bands in the flag stage and flowering period of wheat, and the LAI estimation model was constructed by Partial Least Square (PLS). The main results are as follows: The LAI estimation model based on the 614-774-794nm band combination is the best model for winter wheat flag stage (R2= 0.485, RMSE = 1.192, RV2= 0.682, RMSEV= 1.210); The LAI estimation model constructed by the 454-754-834nm band combination is the best model for winter wheat flowering (R2= 0.702, RMSE = 0.665, RV2= 0.810, RMSEV=0.468). The results show that it is feasible to use the optimal band combination as an independent variable to estimate the leaf area index of winter wheat, which can be used as a new method to monitor the growth of wheat.

Monitoring spatial variance of crop growth is very important for precise fertilization and water management to reduce the difference of grain quality. The study obtained the CHRIS hyperspectral image in the jointing stage of winter wheat. Then the Beer-Lambert law was used to develop the inversion model of leaf area index (LAI) of winter wheat in the study area. Taken the winter wheat parcels as primary units, the CVs of LAI in each parcel were used to evaluate the spatial variance of winter wheat growth at parcel scale. Results showed that the parameters of LAI among all winter wheat parcels were obviously variable. The CVs of LAI fluctuated from 10% to 40%. There were 13 parcels of mid-variance, while 191 parcels of lower-variance. These indicated that the winter wheat growth in the study area was relatively homogeneous. The spatial resolution and spectral resolution of CHRIS image were suitable for evaluating the spatial variance of crop growth in parcel scale.

The study aimed to monitor maize lodging in a large scale by using multi-temporal HJ-1B CCD images. The variation of vegetation indexes before and after lodging was analyzed. The sensitive vegetation index of maize lodging was selected by correlation analysis method. The remote sensing monitoring model of maize lodging disaster was constructed, which used to map maize lodging distribution and disaster grade at large scale. The model was validated by field measured samples at last. Results showed that correlation between ΔRVI and lodging ratio was highest. The ΔRVI can be used as the best vegetation index for quantitative inversion of maize lodging by remote sensing. The overall accuracy of disaster grade classification was 87.5%, and Kappa coefficient was 0.817. It indicated that the model developed in the study could be used to map maize lodging coverage and spatial distribution of disaster grade.

Apple nitrogen status is a key indicator for evaluating quality of apple fruits. In order to study and estimate the nitrogen content of apple leaves, it provides a basis for making a reasonable estimate of the yield of fruit trees. In this paper, the continuous wavelet transform method is used to screen out the sensitive bands. Among them, in the range of 500-650 nm, the correlation coefficient between the nitrogen content of the leaves and the original spectrum is significantly higher. The most relevant band is 621 nm with a correlation coefficient of 0.71. Modeling and verification by partial least squares method, the modeling accuracy is R2is 0.62, RMSE is 0.26g/(100g), NRMSE is 0.1, verification accuracy R2is 0.79, RMSE is 0.31 g/(100g) , NRMSE is 0.1. It can be seen that the model has good stability, high prediction ability and good fitting effect, and can be used as an estimation model for nitrogen content in apple leaves.

The aboveground biomass of crops is an important index reflecting the growth status of crops. It is an important reference for achieving precise water and fertilizer management, yield monitoring and prediction. In this paper, the true height of the vegetation relative to the ground is obtained from the data preprocessing, the point cloud filtering and the normalization of the UAV radar data. The accuracy of biomass and height estimated by the average canopy height (Hcanopy) derived from canopy height model (CHM) is analyzed in a variety of resolution(5cm, 10cm, 15cm,20cm,25, 30cm,50cm, 100cm)respectively. The results suggest that the Hcanopyderived from CHM in 5cm resolution is highest correlated with biomass(R2=0.93, RMSE=698.90kg/ha), the Hcanopy derived from CHM in 50cm resolution is highest correlated with height (R2=0.96, RMSE=4cm). The crop growth parameters could be estimated based on UAV LiDAR across broad spatial scales.

Crop biomass is the basis of crop yield formation, and accurate biomass information is of great significance to ensure national food security. Taking winter wheat as the research object, the airborne radar data and wheat biomass information were used to study the difference of biomass inversion in the vertical distribution of laser point cloud at different scanning angles. The vertical distribution of point clouds is of great difference at 30 m above ground and a scan angle of ±60o. There is no correlation between the vertical distribution of point clouds and the scan angle of ±10oas a result of partial correlation analysis. Two lidar metrics (plot-level Hmea\mathfrakn and Dbelow) is strongly related to field-measured biomass (R2= 0.94,RMSE = 572.32kg/ha) at a scan angle of±10o.

Methods to obtain accurate phenotypic data of the seedling stage of maize are receiving ever-increasing research attention because such data are very important for crop growth and for estimating crop yield. To obtain such data, we propose herein an algorithm that uses computer vision to accurately recognize maize seedlings from a digital image. First, the red-green-blue (RGB) images acquired by a manned ground vehicle (MGV) and an unmanned aerial vehicle (UAV) are transformed into grayscale image to clarify the details of the images, and the Otsu threshold-segmentation method, which is based on threshold optimization, is used to separate the maize seedlings from the soil background. Next, the external pressure method is used to segment the results of skeleton extraction. After removing the skeleton burr by using the skeleton-deburring method based on saliency theory, the principal component analysis is used to determine the direction of the principal axis of the maize-seedling skeleton. The principal axis is then introduced as a reference to identify the direction angle of the principle axis in binary images. This process allows us to obtain from RGB images the multiple plant parameters that characterize the maize seedling stage. To verify these statistical computer-generated results, we compare them with field measurements of the maize plots. The result of applying the proposed algorithm to the MGV and UAV data sets correlate strongly (R = 0.77-0.86) with the manually collected data. An average of 0.014 s is required to calculate the number of maize seedlings in a plot from two images showing different vegetation coverage, which shows that the proposed algorithm is computationally efficient. These results indicate that the proposed method provides accurate phenotypic data on maize seedlings.

Nitrogen is one of the important indices for evaluation of crop growth and output quality. Spectral reflectance of leaves and concurrent leaf nitrogen content parameters of samples were acquired in maize test. The top 10 different vegetation indices were chosen after ranking with VIP as the independent variable for estimating nitrogen content of leaf in maize. The leaf nitrogen content (LNC) estimation model with different vegetation indices can be built using the integrated model of variable importance projection (VIP) — partial least squares (PLS). The optimal model was selected by using Akaike's Information Criterion (AIC). The optimal model was validated by leave one out cross-validation (LOOCV) method. The decision coefficient (R2), root-mean-square error (RMSE) and relative error (RE) of the optimal model respectively were 0.73, 0.16 and 0; R2, RMSE and RE of maize by validating were 0.73, 0.19 and 0, respectively.

Gross primary production (GPP), defined as the overall amount of carbon fixed through the process of vegetation photosynthesis, is an important characteristic for climate change and carbon cycle research to reveal the carbon sequestration capacity of a certain ecosystem. The aim of the research is to test a simplified way to remote estimation of gross primary production in maize with remote sensing and carbon flux measurements, through discarding nearly all kinds of complex concepts and focusing on the operation convenience, according to the main principle is based on the concept of LUE-based (light use efficiency) GPP estimation models. And validate whether it is right that “the more complex the method is, the more accurate it will achieve”. Results shows that for single and multi-sites estimations, the two forms of estimation concepts of GPP ∝VI∗SOL and GPP ∝LAI∗VI∗SOL performs different. And the former one seems to be better than the other. This also reveals that to establish the remotely-sensed models, the numbers of input parameters is not the key influence factors. The accuracy of single site estimation is higher than that of multi-sites because of the heterogeneity in different sites.

With the increasing food demand for agricultural products and climate change pressures, the production of agricultural ecosystem performs more and more important to achieve the goal of sustainable development. The aim of this paper is to evaluate cropland production potential integrating cultivation patterns and types discrimination based on long-term crop dynamics using remote sensing approaches. First, crop cultivation patterns and types are identified using long-term vegetation dynamics information. Then the results are introduced into the models of cropland production potential estimation. Considering the influence of radiation, temperature, precipitation and soil conditions on crop growth for various crop types in different regions, the cropland potential production is estimated step by step. And finally show the results of four kinds of potential productivity: light, light-temperature, climatic and cropland potential productivity.

Structure-From-Motion (SFM) method is based on the same scene and different angles of the captured sequence of image, then calculate the feature points in the photogrammetric coordinate system of three-dimensional coordinates and camera parameters. SFM can directly generated orthophoto map just by captured overlapping images, but mosaicking accuracy has not to be verified. The purpose of this study is that verify the feasibility and accuracy of SFM method in UAV image mosaic. The process of UAV imagery mosaicking based on SFM method was elaborated, and the test image was mosaicked with UAV imagery processing software which based on SFM. The result: (1) UAV imagery mosaicking based on SFM algorithm has low accuracy on geographic positioning because of the low precision POS. But the distance\area measurement with high accuracy, the perimeter accuracy is above 96.6% and the area accuracy is above 93.2%. (2) The image had high accuracy after geometric correction using ground points. When 5 ground points were used, the mean value of absolute error was 0.60 m. The study showed: (1) the accuracy of perimeter and area can basically meet the accuracy requirements of distance\area measurement in agricultural applications. (2) the orthophoto map was rectified by ground control point can significantly improve the geo-location precision of the image.

Vertical heterogeneity of the canopy is being increasingly recognized in remote estimates of vegetative properties. Given the current limited knowledge of this issue, this paper investigated the effects of different vertical distributions of crop structure [e.g., leaf angle (LA)] and leaf area index (LAI)] and biochemical parameters [e.g., chlorophyll a and b content (Chla+b) and water content (Wc)] on canopy reflectance and vegetation indices (VIs). A recently developed multiple-layer canopy reflectance model (MRTM) was tested for winter wheat and used to run a simulation analysis of different canopy scenarios. The results showed that the MRTM performed well to model winter wheat canopy reflectance with regard to spikes and vertical distributions of leaf properties. The vertical profiles of LA and LAI influenced canopy reflectance at almost all wavelengths, whereas the vertical profile of Chla+bmainly affected reflectance in the visible region, and that of Wconly affected reflectance in the near-infrared region. Changes in vertical distribution of the LA resulted in clear variations in VIs related to the LA, LAI, and Chla+bestimates. The vertical LAI and Chla+bprofiles mainly influenced the VIs related to the LAI and Chla+bestimates. The Wcvertical profile primarily affected the VIs used to estimate crop water properties. The sensitivities of the VIs were mainly associated with the spectral responses and penetration characteristics of the bands they used. These findings suggest that the sensitivity of VIs to the vertical distributions of crop parameters should be considered when establishing models for remote crop monitoring.

The assimilation of biophysical crop canopy variables retrieved from remotely sensed data into two crop models of differing degree of complexity is assessed in this study, in the context of the development of tools suitable for the estimation of yield losses due to drought. The more complex AQUACROP model, developed by FAO and the simpler SAFY model were employed to estimate wheat grain yield for an area in the Shaanxi Province in China through the assimilation of biophysical variables retrieved from Landsat and HJ1A and HJ1B satellites for three growing seasons (2013 to 2015). Results were validated with ground yield data.

Plant biomass is an important parameter for crop management and yield estimation. The potential of compact polarimetric (CP) synthetic aperture radar (SAR) data in estimating biomass of oilseed rape crop (Brassica napus L.) is investigated in this study. Five CP SAR imagery was simulated using five fully polarimetric Radarsat-2 data, and the dynamic evolution of polarimetric features, relying on different polarimetric decomposition methods (m-χ, m-δ, and Freeman-Durden), with the crop growth, was compared. It was found that the Dbl indicator, by the m-χ decomposition method, can reflect well the dynamic growth of canola. Therefore, a method of monitoring fresh and dry biomass of canola was put forward. The result showed that the root mean square error (RMSE) was 56.5g/m2, 448.2g/m2, and the relative error (RE) was 23.9%, 25.0% for fresh and dry biomass, respectively. In addition, the precision of the model will be affected when the crop becomes mature since its vegetation water content declines. The results were also compared with those of the fully polarization SAR. It revealed that the performance of CP SAR on rapeseed monitoring can achieve the level of fully polarization SAR, considering the advantages of CP SAR, such as wider coverage and less data volume etc. It revealed that the polarization information was necessary in quantitatively monitoring of broad leaf crops, such as rapeseed, and CP SAR has a great potential in crop monitoring.

Crop drought is a terrible agricultural disaster across the globe, which has been widely studied with remote sensing optical data. However, soil moisture, a key parameter in crop drought monitoring which was hard for optical remote sensing data to estimate. SAR (Synthetic Aperture Radar) observation system is very sensitive to moisture in the soil, more importantly, the microwave which SAR systems used could penetrate the crop canopy into the soil. Water Cloud Model (WCM) is a common method of estimating soil moisture, which needs descriptor of the canopy. In order to reduce descriptor of the canopy error in the WCM, crop parameters are instead by Radar Vegetation Index (RVI). A new method was proposed to soil moisture estimation and application based on WCM and bare soil model. In the new model, crop parameter input ware replaced by RVI, which was calculated by Radarsat-2 SAR data. The result shows a good performance with no crop parameter was used.

The paper describes the preliminary results of the January-August 2015 multi-frequency EO data acquisition campaign conducted over the Maccarese (Central Italy) farm. From January to May radar Cosmo SkyMed Ping-Pong (HH-VV), RapidEye and ZY-3 multispectral VHR optical images, as well as in situ data, have been acquired to retrieve biophysical and/or bio-chemical characteristics of soil and crops. LAI trend has been analyzed and compared by using both polarimetric and optical retrieval algorithms while soil moisture measurements have been compared with the radar backscattering.

The aim of this work is the development of methods for the assimilation of biophysical variables, estimated from multi-source remote sensing data, into crop growth models, in order to estimate the yield losses due to drought both at the farm and at the regional scale. A methodology to obtain maps of leaf area index (LAI), and fractional canopy cover (CC), from HJ1A and HJ1B Chinese satellite optical data was established, using an algorithm based on the training of artificial neural networks (ANN) on PROSAIL model simulations. Retrieved values of biophysical variables, such as LAI or CC, will be assimilated into crop growth models in order to estimate wheat yield. The present work focused on testing two different approaches using a common dataset gathered in Xiaotangshan (China) with two crop models of different complexity, in order to compare the procedures and analyse the responses of the models, before the subsequent application at a regional scale in Yangling, Shaanxi, Central China.

Monitoring crop leaf area index (LAI) timely and accurately by remote sensing is crucial to assess crop growth, manage field water-fertilizer and predict yield. The Huaihe River Basin was chose as study area to carry out field survey. By using decision tree classification and HJ-1/2 CCD image, the spatial distribution of paddy was identified. The extinction coefficient of paddy surface was confirmed with in-situ samples. The Beer-Lambert law was introduced to develop the inversion model of paddy LAI. The accuracy of inversion model was evaluated with in-situ samples, including coefficient of determination (R2), RMSE and overall accuracy, while contrasting with the model of single-variable and multi-variables. Results showed that the inversion model based on Beer-Lambert law reached highest accuracy with the average R2of 0.684 and the average RMSE of 0.592. The average R2of multi-variables was 0.636, while the average RMSE was 0.661. The model of single-variable has lowest accuracy with average R2of 0.595 and average RMSE of 0.732. It indicated that the retrieval accuracy of LAI was improved with more variables inputted. The model based on Beer-Lambert law simulated the physical process of radiative transfer of paddy that differed from the two other models. The overall accuracy of Beer-Lambert law model exceeded 95 percent, while those of the two other models were 91.0 percent and 88.2 percent respectively. So the inversion model of paddy LAI based on Beer-Lambert law could eliminate the influence of water background and improve the accuracy of paddy LAI by remote sensing.

Mapping high spatial-temporal resolution evapotranspiration (ET) over large areas is important for water resources planning, precision irrigation and monitoring water use efficiency. However, both the traditional field measurement and aerodynamic estimation mainly focus on obtaining local ET. Remote sensing data often can be used to retrieve large area instantaneous ET at low spatial resolution over region or global scale. Therefore, using traditional measurements and high resolution image data to generate high spatial-temporal resolution ET is becoming an important research direction. In this paper, the complementary relationship model (CR) was employed together with meteorological data to estimate actual ET, and the results were validated by lysimeter observation. Furthermore, CR model was combined with high resolution image, IKONOS data, to estimate instantaneous field scale ET and they also were transferred into daily ET. The cumulative evapotranspiration (ET) of winter wheat during the reproductive phase from March to June of 2011 was 469.12 mm, essentially corresponding to the annual precipitation in the Beijing area. The most high accuracy of estimated ET by CR model is also on May(R2=0.863, RMSE=0.103 mm). The transferred daily ET by self-preservation of evaporative fraction(EF) method were consistent with lysimeter measurements for all four months(R2=0.937, RMSE=0.668 mm). It was proved in this study that CR model can be used to estimate precision field scale ET with meteorological data and high resolution remote sensing data together in a region with limited ground data availability.

Leaf area index (LAI) of crop canopies is important for crop growth monitoring and yield estimation. Considering the practical need of achieving distribution properties of LAI at a special spatial scale, and the difficult acquisition of corresponding observations at the same scale, a method integrating multi-resolution data at larger scales based on continuous wavelet theory is proposed to provide a more effective LAI dataset. For this method, firstly multi-scale wavelet theory is selected for multi-resolution data decomposition, and then decomposed signals and statistics of observations are coupled for wavelet reconstruction. Finally, the new constructed data is used for LAI estimation through multiple linear regression method. Barley is selected as experimental object. The performance of this method is quantitatively analyzed by testing indicators, i.e. Number of effective bands, R2, and MRA. Theory analysis and numerical practices fully confirm the feasibility and validity of the proposed method in crop LAI estimation.

Based on the theory of radiation transfer model, this paper modified the Simultaneous Heat and Water model to calculate FPAR vertical distribution in maize canopy and analyzed the relationships between FPAR and some parameters like maize canopy structure, solar zenith, soil reflectance, etc. The validation results using field measurements prove the model to be accurate.

Land surface temperature (LST) is a key parameter in numerous environmental studies. In order to lower the subpixel temperature estimation error caused by re-sampling of remote sensing data, a disaggregation method for subpixel temperature using the remote sensing endmember index based technique (DisEMI) was established in this study. To take an advantage of simultaneous, multi-resolution observations at coincident nadirs by the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and the MODerate-resolution Imaging Spectroradiometer (MODIS), LST products from the two sensors were examined for a portion of suburb area in Beijing, China. The verified results indicate that the estimated temperature distribution was basically consistent with that of ASTER LST product(R2= 0.709 and RMSE = 2.702 K).

This paper presents a new forest leaf area index (LAI) inversion method from multisource and multiangle data combined with a radiative transfer model and the strategy of -means clustering and artificial neural network (ANN). Four scenes of Landsat-5 Thematic Mapper (L5TM) and Beijing-1 small satellite multispectral sensors (BJ1) images, acquired at different times, were selected to construct multisource and multiangle image data in this study. Considering a vertical distribution of forest LAI from both overstory and understory, a hybrid model of the invertible forest reflectance model (INFORM) was used to support the retrieval of forest LAI to eliminate the dependence of understory vegetation. The simulated data from INFORM outputs, added with a random noise, were first clustered by -means method, and were then trained by ANN to obtain the inversion model for each group (cluster). Next, the inversion model was applied to the different combinations of multiangle data to retrieve the forest LAI. Finally, a validation of inverted results with Moderate Resolution Imaging Spectroradiometer LAI product and field measurements was conducted. The experimental results indicate that the accuracy of the inverted forest LAI can be improved through the addition of observation angle data, if the quality of the image data is ensured. The inversion accuracy of LAI with the multiangle image data is improved by 30% compared to the average accuracy of the inverted LAI with the single angle data after considering the addition of random noise to the ANN training data.

Among the multisource data fusing methods, the potential advantages of remote sensing of solar-reflective visible and near-Infrared [(VNIR); 400-900 nm] data and thermal-infrared (TIR) data have not been fully mined. Usually, a linear unmixed method is used for the purpose, which results in low estimation accuracy of subpixel land-surface temperature (LST). In this paper, we propose a novel method to estimate subpixel LST. This approach uses the characteristics of high spatial-resolution advanced spaceborne thermal emission and reflection radiometer (ASTER) VNIR data and the low spatial-resolution TIR data simulated from ASTER temperature product to generate the high spatial-resolution temperature data at a subpixel scale. First, the land-surface parameters (e.g., leaf area index, normalized difference vegetation index (NDVI), soil water content index, and reflectance) were extracted from VNIR data and field measurements. Then, the extracted high resolution of land-surface parameters and the LST were simulated into coarse resolutions. Second, the genetic algorithm and self-organizing feature map artificial neural network (ANN) was utilized to create relationships between land-surface parameters and the corresponding LSTs separately for different land-cover types at coarse spatial-resolution scales. Finally, the ANN-trained relationships were applied in the estimation of subpixel temperatures (at high spatial resolution) from high spatial-resolution land-surface parameters. The two sets of data with different spatial resolutions were simulated using an aggregate resampling algorithm. Experimental results indicate that the accuracy with our method to estimate land-surface subpixel temperature is significantly higher than that with a traditional method that uses the NDVI as an input parameter, and the average error of subpixel temperature is decreased by 2-3 K with our method. This method is a simple and convenient approach to estimate subpixel LST from high spatial-temporal resolution data quickly and effectively.

Satellite image simulation is one of the key methods to check the expected performance of the satellites before they launched or when satellites can not provide images in other time. In order to provide a useful tool to analyze whether the payload of HJ-1B (a small satellite of the environment-monitoring constellation) is enough, we develop a simulation system for the infrared cameras, which consists of four bands including NIR band (0.75-1.10¿m), SWIR (1.55-1.75¿m), MIR (3.50-3.90¿m) and TIR (10.5-12.5¿m). The spatial resolution of NIR and SWIR band is 150 meter, while 300 meter for the MIR and TIR band. The sensor is an optical-mechanics multi-scanning system with maximum scanning degree of 29 degree.

Some typical topographic correction methods, such as cosine model, C correction model, SCS model, SCS+C model and Minnaert model, have been assessed in detail in this paper using GOMS model. A BRF model also presented for topographic effects eliminating and its application in Jiangxi rugged area. The result shows that the BRF model has the topographic correction ability.

In rugged area, the solar radiance is accepted by the sensor after a complicated interactive process between solar incidence, atmosphere and earth surface target. In this paper radiance received by one earth target is analyzed. Solar direct radiance, sky diffuse radiance and background terrain reflective radiance were obtained using a fit model. Combined with radiative transfer code and bi-directinal reflectance factor, atmospheric and topographic effects of Landsat/TM that covers Jiangxi rugged area had been eliminated. Several criterions were taken as the correction result validation. This paper shows that the method has robust atmospheric and topographic correction ability.

The consistent end-to-end simulation of them is an important task, sometimes the only way for the adaptation and optimisation of a sensor and its observation conditions, the choice and test of algorithms for data processing, error estimation and the evaluation of the capabilities of the whole sensor system. It is essential to accomplish simulation of atmospheric radiative transfer, if a complete imaging simulating system is to be expected. Based on given resolution and directional capabilities of the instrument, and combination with land surface temperature and emissivity data obtained from airborne imagery, TOA (top of atmosphere) radiance images have been simulated pixel by pixel coupling the atmospheric radiative transfer analytic model extended from MODTRAN4 and the atmospheric adjacency effect model derived from point spread function (for atmospheric directional and adjacency effect). In this way, all major scattering and emission contribution of atmosphere were considered. Through analysing results, it indicates that analytic model and adjacency effect model is more adequate for thermal infrared imaging simulation than others existing models.