Rice Growth Monitoring Research Articles

Simultaneously predicting SPAD and water content in rice leaves using hyperspectral imaging with deep multi-task regression and transfer component analysis.

Water content and chlorophyll content are important indicators for monitoring rice growth status. Simultaneous detection of water content and chlorophyll content is of significance. Different varieties of rice show differences in phenotype, resulting in the difficulties of establishing a universal model. In this study, hyperspectral imaging was used to detect the Soil and Plant Analyzer Development (SPAD) values and water content of fresh rice leaves of three rice varieties (Jiahua 1, Xiushui 121 and Xiushui 134). Both partial least squares regression and convolutional neural networks were used to establish single-task and multi-task models. Transfer component analysis (TCA) was used as transfer learning to learn the common features to achieve an approximate identical distribution between any two varieties. Single-task and multi-task models were also built using the features of the source domain, and these models were applied to the target domain. These results indicated that for models of each rice variety the prediction accuracy of most multi-task models was close to that of single-task models. As for TCA, the results showed that the single-task model achieved good performance for all transfer learning tasks. Compared with the original model, good and differentiated results were obtained for the models using features learned by TCA for both the source domain and target domain. The multi-task models could be constructed to predict SPAD values and water content simultaneously and then transferred to another rice variety, which could improve the efficiency of model construction and realize rapid detection of rice growth indicators. © 2024 Society of Chemical Industry.

Estimation of the rice aboveground biomass based on the first derivative spectrum and Boruta algorithm.

Aboveground biomass (AGB) is regarded as a critical variable in monitoring crop growth and yield. The use of hyperspectral remote sensing has emerged as a viable method for the rapid and precise monitoring of AGB. Due to the extensive dimensionality and volume of hyperspectral data, it is crucial to effectively reduce data dimensionality and select sensitive spectral features to enhance the accuracy of rice AGB estimation models. At present, derivative transform and feature selection algorithms have become important means to solve this problem. However, few studies have systematically evaluated the impact of derivative spectrum combined with feature selection algorithm on rice AGB estimation. To this end, at the Xiaogang Village (Chuzhou City, China) Experimental Base in 2020, this study used an ASD FieldSpec handheld 2 ground spectrometer (Analytical Spectroscopy Devices, Boulder, Colorado, USA) to obtain canopy spectral data at the critical growth stage (tillering, jointing, booting, heading, and maturity stages) of rice, and evaluated the performance of the recursive feature elimination (RFE) and Boruta feature selection algorithm through partial least squares regression (PLSR), principal component regression (PCR), support vector machine (SVM) and ridge regression (RR). Moreover, we analyzed the importance of the optimal derivative spectrum. The findings indicate that (1) as the growth stage progresses, the correlation between rice canopy spectrum and AGB shows a trend from high to low, among which the first derivative spectrum (FD) has the strongest correlation with AGB. (2) The number of feature bands selected by the Boruta algorithm is 19~35, which has a good dimensionality reduction effect. (3) The combination of FD-Boruta-PCR (FB-PCR) demonstrated the best performance in estimating rice AGB, with an increase in R² of approximately 10% ~ 20% and a decrease in RMSE of approximately 0.08% ~ 14%. (4) The best estimation stage is the booting stage, with R2 values between 0.60 and 0.74 and RMSE values between 1288.23 and 1554.82 kg/hm2. This study confirms the accuracy of hyperspectral remote sensing in estimating vegetation biomass and further explores the theoretical foundation and future direction for monitoring rice growth dynamics.

Remote sensing monitoring of rice growth under Cnaphalocrocis medinalis (Guenée) damage by integrating satellite and UAV remote sensing data

ABSTRACT Satellite remote sensing is commonly used for large-scale agricultural monitoring, but the low spatial resolution of its imagery does not allow it to present details of crop growth. Combining satellite remote sensing and unmanned aerial vehicle (UAV) remote sensing to complement each other’s advantages may be a way to realize large-scale and precision agricultural monitoring. Therefore, to explore the effect of the fusion method of UAV remote sensing data and satellite remote sensing data on improving satellite monitoring of the growth of damaged rice and to achieve large-scale accurate monitoring of the growth characteristics of damaged rice, in this study, the research object was rice infested by Cnaphalocrocis medinalis Guenee. We selected SuperDove satellite imagery on 14 August 2022 and UAV multispectral imagery on 15 August 2022 for vegetation index calculation and fused the two by a scale transformation method. We constructed LAI monitoring models using multiple linear regression (MLR), back-propagation neural network (BPNN) and support vector machine regression (SVR) to evaluate the effect of satellite data on LAI inversion for affected rice before and after fusion. The results showed that (1) the learning ability of the machine learning model represented by BPNN and SVR was better than that of the traditional regression model represented by MLR. The SVR model had the best inversion effect on the LAI. (2) The satellite data after fusion with UAV data significantly improved the inversion accuracy of the LAI of damaged rice. Compared with the original satellite data, with the combined data, R2 increased by approximately 0.3, RMSE and MAE decreased by approximately 0.1. Moreover, the spatial details of remote sensing images were clearer, and the spatial matching degree with the UAV image inversion results was higher. This study can provide theoretical and technical support for large-scale and high-precision monitoring of affected rice.

Fine classification and phenological analysis of rice paddy based on multi-temporal general compact polarimetric SAR data.

Fine classification and phenological information of rice paddy are of great significance for precision agricultural management. General compact polarimetric (CP) synthetic aperture radar (SAR) offers the advantage of providing rich polarimetric information, making it an important means of monitoring rice growth. Therefore, in response to the current challenges of difficulty in rice type classification and the small differences in phenological polarimetric characteristics, a novel strategy for fine classification and phenological analysis of rice paddy is proposed. This strategy thoroughly explores the polarimetric information of general CP SAR data and the target scattering characterization capabilities under different imaging modes. Firstly, the general CP SAR data is formalized using the standard CP descriptors, followed by the extraction of general CP features through the ΔαB /αB target decomposition method. optimal CP features are generated to achieve fine classification of rice paddy. Finally, 6 phenological stages of rice are analyzed based on the general CP features. The experiment results of rice classification show that the classification accuracy based on this strategy exceeds 90%, with a Kappa coefficient above 0.88. The highest classification accuracies were observed for transplanting hybrid rice paddy (T-H) and direct-sown japonica rice paddy (D-J), at 80.9% and 89.9%, respectively. The phenological evolution rule of the two rice types indicate that from early June (seedling stage) to late July (elongation stage), the CP feature variation trends of T-H and D-J are generally consistent. However, from October (mature stage) to November (harvest stage), the variation trends of the CP features for T-H and D-J are significantly different. The study found that from the booting-heading stage to the harvest stage, the linear π/4 mode outperforms circular and elliptical polarimetric modes in distinguishing different types of rice. Throughout the entire phenological period of rice growth, CP SAR of linear π/4 mode is surpasses that of other linear modes in discriminating different type of rice. The proposed strategy enables high-precision fine classification rice paddy, and the extracted general CP αB parameter effectively reflects the phenological change trends in rice growth.

Kharif rice growth and area monitoring in Gosaba CD block of Indian Sundarbans region using multi-temporal dual-pol SAR data

Kharif rice growth and area monitoring in Gosaba CD block of Indian Sundarbans region using multi-temporal dual-pol SAR data

Dual-polarization SAR rice growth model: A modeling approach for monitoring plant height by combining crop growth patterns with spatiotemporal SAR data

Plant height (PH) is a crucial indicator for monitoring rice growth. Synthetic aperture radar (SAR) has emerged as a valuable tool for monitoring rice growth due to its sensitivity to changes in vegetation. This paper introduces a dual-polarization SAR rice growth model for plant height (DPSRG-PH) that effectively combines crop growth patterns with PH models. To enhance the generalization ability of growth monitoring, we employ an unsupervised domain adaptive (DA) method. The experimental results demonstrate that the DPSRG-PH model considers the correlation between crop growth changes in the time dimension and the differences in crop growth at different phenological stages. Moreover, it can be effectively applied throughout the entire growth period, with the RMSE of most growth stages remaining below 10 cm. Furthermore, the modeling method proposed in this study effectively addresses the issues of overfitting caused by the number of sample points. This enhances the reasonability of the model parameters in evaluating the growth trend and holds practical implications for production.

Monitoring the Rice Panicle Blast Control Period Based on UAV Multispectral Remote Sensing and Machine Learning

The heading stage of rice is a critical period for disease control, such as for panicle blast. The rapid and accurate monitoring of rice growth is of great significance for plant protection operations in large areas for mobilizing resources. For this paper, the canopy multispectral information acquired continuously by an unmanned aerial vehicle (UAV) was used to obtain the heading rate by inversion. The results indicated that the multi-vegetation index inversion model is more accurate than the single-band and single-vegetation index inversion models. Compared with traditional inversion algorithms such as neural network (NN) and support vector regression (SVR), the adaptive boosting algorithm based on ensemble learning has a higher inversion accuracy, with a correlation coefficient (R2) of 0.94 and root mean square error (RMSE) of 0.12 for the model. The study suggests that a more effective inversion model of UAV multispectral remote sensing and heading rate can be built using the AdaBoost algorithm based on the multi-vegetation index, which provides a crop growth information acquisition and processing method for determining the timing of rice tassel control.

Spectral pattern analysis of rice varieties with proximal sensing method and sentinel-2 imagery

Rice growth monitoring through remote sensing devices, such as Sentinel-2 imagery has been widely used, yet the accuracy level is different due to variations in altitude and object reflectance. Proximal sensing provides site (ground-level) monitoring via high-resolution sensors. Spectroradiometer is a proximal sensing device that can continuously record the spectral pattern of an object. This research aims to examine the spectral pattern characteristic of Grendel sticky and Ciherang rice varieties based on proximal sensing and to investigate the relationship between the reflectance value of spectroradiometer measurements and Sentinel-2 image extraction. Based on the spectral pattern analysis, the Ciherang variety reached the maximum reflectance value for red-edge and NIR wavelength at 56 DAP (reproductive stage) and then decreased at 81 DAP (ripening stage). In comparison, the Grendel variety reached 81 DAP (reproductive stage) and dropped to 86 DAP (ripening stage). Based on linear regression, at 56, 81, 86, and 90, DAP indicated the reflectance values of spectroradiometer measurement are equivalent to the value of sentinel-2 image extraction with an average of R2 above 86%.

Research progress on radar remote sensing for rice growth monitoring

Research progress on radar remote sensing for rice growth monitoring

Improved Mask R-CNN Combined with Otsu Preprocessing for Rice Panicle Detection and Segmentation

Rice yield is closely related to the number and proportional area of rice panicles. Currently, rice panicle information is acquired with manual observation, which is inefficient and subjective. To solve this problem, we propose an improved Mask R-CNN combined with Otsu preprocessing for rice detection and segmentation. This method first constructs a rice dataset for rice images in a large field environment, expands the dataset using data augmentation, and then uses LabelMe to label the rice panicles. The optimized Mask R-CNN is used as a rice detection and segmentation model. Actual rice panicle images are preprocessed by the Otsu algorithm and input into the model, which yields accurate rice panicle detection and segmentation results using the structural similarity and perceptual hash value as the measurement criteria. The results show that the proposed method has the highest detection and segmentation accuracy for rice panicles among the compared algorithms. When further calculating the number and relative proportional area of the rice panicles, the average error of the number of rice panicles is 16.73% with a minimum error of 5.39%, and the error of the relative proportional of rice panicles does not exceed 5%, with a minimum error of 1.97% and an average error of 3.90%. The improved Mask R-CNN combined with Otsu preprocessing for rice panicle detection and segmentation proposed in this paper can operate well in a large field environment, making it highly suitable for rice growth monitoring and yield estimation.

Multispectral remote sensing for accurate acquisition of rice phenotypes: Impacts of radiometric calibration and unmanned aerial vehicle flying altitudes.

As a promising method, unmanned aerial vehicle (UAV) multispectral remote sensing (RS) has been extensively studied in precision agriculture. However, there are numerous problems to be solved in the data acquisition and processing, which limit its application. In this study, the Micro-MCA12 camera was used to obtain images at different altitudes. The piecewise empirical line (PEL) method suitable for predicting the reflectance of different ground objects was proposed to accurately acquire the reflectance of multi-altitude images by comparing the performance of the conventional methods. Several commonly utilized vegetation indices (VIs) were computed to estimate the rice growth parameters and yield. Then the rice growth monitoring and yield prediction were implemented to verify and evaluate the effects of radiometric calibration methods (RCMs) and UAV flying altitudes (UAV-FAs). The results show that the variation trends of reflectance and VIs are significantly different due to the change in component proportion observed at different altitudes. Except for the milking stage, the reflectance and VIs in other periods fluctuated greatly in the first 100 m and remained stable thereafter. This phenomenon was determined by the field of view of the sensor and the characteristic of the ground object. The selection of an appropriate calibration method was essential as a result of the marked differences in the rice phenotypes estimation accuracy based on different RCMs. There were pronounced differences in the accuracy of rice growth monitoring and yield estimation based on the 50 and 100 m-based variables, and the altitudes above 100 m had no notable effect on the results. This study can provide a reference for the application of UAV RS technology in precision agriculture and the accurate acquisition of crop phenotypes.

Development of Prediction Models for Estimating Key Rice Growth Variables Using Visible and NIR Images from Unmanned Aerial Systems

The rapid and accurate acquisition of rice growth variables using unmanned aerial system (UAS) is useful for assessing rice growth and variable fertilization in precision agriculture. In this study, rice plant height (PH), leaf area index (LAI), aboveground biomass (AGB), and nitrogen nutrient index (NNI) were obtained for different growth periods in field experiments with different nitrogen (N) treatments from 2019–2020. Known spectral indices derived from the visible and NIR images and key rice growth variables measured in the field at different growth periods were used to build a prediction model using the random forest (RF) algorithm. The results showed that the different N fertilizer applications resulted in significant differences in rice growth variables; the correlation coefficients of PH and LAI with visible-near infrared (V-NIR) images at different growth periods were larger than those with visible (V) images while the reverse was true for AGB and NNI. RF models for estimating key rice growth variables were established using V-NIR images and V images, and the results were validated with an R2 value greater than 0.8 for all growth stages. The accuracy of the RF model established from V images was slightly higher than that established from V-NIR images. The RF models were further tested using V images from 2019: R2 values of 0.75, 0.75, 0.72, and 0.68 and RMSE values of 11.68, 1.58, 3.74, and 0.13 were achieved for PH, LAI, AGB, and NNI, respectively, demonstrating that RGB UAS achieved the same performance as multispectral UAS for monitoring rice growth.

NESEA-Rice10: high-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019

Abstract. An accurate paddy rice map is crucial for ensuring food security, particularly for Southeast and Northeast Asia. MODIS satellite data are useful for mapping paddy rice at continental scales but have a mixed-pixel problem caused by the coarse spatial resolution. To reduce the mixed pixels, we designed a rule-based method for mapping paddy rice by integrating time series Sentinel-1 and MODIS data. We demonstrated the method by generating annual paddy rice maps for Southeast and Northeast Asia in 2017–2019 (NESEA-Rice10). We compared the resultant paddy rice maps with available agricultural statistics at subnational levels and existing rice maps for some countries. The results demonstrated that the linear coefficient of determination (R2) between our paddy rice maps and agricultural statistics ranged from 0.80 to 0.97. The paddy rice planting areas in 2017 were spatially consistent with the existing maps in Vietnam (R2=0.93) and Northeast China (R2=0.99). The spatial distribution of the 2017–2019 composite paddy rice map was consistent with that of the rice map from the International Rice Research Institute. The paddy rice planting area may have been underestimated in the region in which the flooding signal was not strong. The dataset is useful for water resource management, rice growth, and yield monitoring. The full product is publicly available at https://doi.org/10.5281/zenodo.5645344 (Han et al., 2021a). Small examples can be found from the following DOI: https://doi.org/10.17632/cnc3tkbwcm.1 (Han et al., 2021b).

STICS crop model and Sentinel-2 images for monitoring rice growth and yield in the Camargue region

The assessment of rice yield at territory level is important for strategic economic decisions. Assessing spatial and temporal yield variability at regional scale is difficult because of the numerous factors involved, including agricultural practices, phenological calendars, and environmental contexts. New remote sensing data acquired at decametric resolution (Sentinel missions) can provide information on this spatial variability. The study objective was thus to evaluate the potential of Sentinel-2 images for monitoring rice cropping systems and yield from farm to region scales. The approach considered both observations and modeling. In-depth farmers surveys were carried out in the Camargue region, Southeastern France. The novelty was to use operational tools (BVNET and PHENOTB) to compute leaf area index, to daily interpolate this biophysical variable from 44 images acquired in 2016 and 2017 for each rice field, and to derive key phenological parameters from the analysis of the temporal profiles. The STICS crop model was spatially used, considering the biophysical variables derived from remote sensing. We tested four simulation strategies, differing in the integration intensity of remote sensing information into the model. Results have shown that (1) Sentinel-2 data allowed distinguishing early and late rice varieties. (2) The phenological stages mapped at the regional level allowed to better understand the agricultural practices of farmers. (3) The assimilation of remote sensing data to the STICS crop model significantly improved yield estimation and provided useful information on the spatial variability observed at regional scale. It was the first time that Sentinel-2 data are used with STICS crop model to assess rice yield at both farm and regional scale in the Camargue area. The proposed method is based on free open data and free access model, easily reproducible in other environmental contexts.

Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands

Accurate rice mapping and growth monitoring are of great significance for ensuring food security and agricultural sustainable development. Remote sensing (RS), as an efficient observation technology, is expected to be useful for rice mapping and growth monitoring. Due to the fragmented distribution of paddy fields and the undulating terrain in Southern China, it is very difficult in rice mapping. Moreover, there are many crops with the same growth period as rice, resulting in low accuracy of rice mapping. We proposed a red-edge decision tree (REDT) method based on the combination of time series GF-6 images and red-edge bands to solve this problem. The red-edge integral and red-edge vegetation index integral were computed by using two red-edge bands derived from GF-6 images to construct the REDT. Meanwhile, the conventional method based on time series normalized difference vegetation index (NDVI), normalized difference water index (NDWI), enhanced vegetation index (EVI) (NNE) was employed to compare the effectiveness of rice mapping. The results indicated that the overall accuracy and Kappa coefficient of REDT ranged from 91%–94% and 0.82–0.87, improving about 7% and 0.15 compared with the NNE method. This proved that the proposed technology was able to efficiently solve the problem of rice mapping on a large scale and regions with fragmented landscapes. Additionally, two red-edge bands of GF-6 images were applied to monitor rice growth. It concluded that the two red-edge bands played different roles in rice growth monitoring. The red-edge bands of GF-6 images were superior in rice mapping and growth monitoring. Further study needs to develop more vegetation indices (VIs) related to the red-edge to make the best use of red-edge characteristics in precision agriculture.

Strategy Planning for Rice Seed Producer’s Information System Using Anita Cassidy Method

Strategic planning of information systems on seed producers is an action that needs to be taken in order to maintain and make the business grow faster. However, such information system usually been created through the system development life cycle (SDLC) or other methods that appropriate for business. This study used Anita Cassidy method for strategic planning of rice seed producer’s information systems in XYZ Ltd., as case study. The following stages has been done, i.e. visioning, analysis, direction, and recommendations. The final results of the stages carried out recommend business applications that can be implemented, namely: Rice seed certification information systems, E-Commerce of Rice Seeds, Rice Type Determination Applications, Irrigation Control Applications, Paddy Pest Resolution Applications, Rice Growth Monitoring Applications, Personnel Information Systems, Payroll Information System, Procurement Information System, Financial Information System.
 Keywords: Information Systems, Information Systems Strategic Planning, Anita Cassidy Method

Paddy Monitoring in Seberang Perak, Malaysia Using Multi-Temporal Sentinel-1 Data

Rice is considered the main food source for over 40% of the world population and plays a crucial role in countries’ food security, food management, and economic aspects. The value of SAR remote sensing in agricultural studies has its source of illumination and not limited to cloud cover. This makes it highly preferable over optical sensors in cloud-shrouded countries. The objective of the study is to assess the capability of Sentinel-1 data for determining paddy planting methods, identifying unhealthy paddy and an attempt made to differentiate rice varieties through correlation of in situ measurements and temporal variation of SAR backscattered signals. Six Sentinel-1 images are stacked to cover the entire paddy lifecycle. The correlated field data and plant backscatter showed that transplanted paddy has backscatter higher than broadcasted paddy. Two drops of paddy backscatter coefficient occurred, the first one, at the reproductive stage when paddy was attacked by bacteria and the second drop was at the ripening stage due to the attack of pests. The five rice varieties planted in Seberang Perak, Malaysia had the backscatter with insignificant differences that cannot confirm the Sentinel-1 capability to differentiate planted rice varieties. According to the obtained results, the time series of Sentinel-1 data has the capability for paddy rice growth monitoring.

Assessment of rice growth conditions in a semi-arid region of India using the Generalized Radar Vegetation Index derived from RADARSAT-2 polarimetric SAR data

Rice growth monitoring using Synthetic Aperture Radar (SAR) is recognized as a promising approach for tracking the development of this important crop. Accurate spatio-temporal information of rice inventories is required for water resource management, production risk occurrence, and yield forecasting. This research investigates the potential of the proposed Generalized volume scattering model based Radar Vegetation Index (GRVI) for monitoring rice growth at different phenological stages. The GRVI is derived using the concept of a geodesic distance (GD) between Kennaugh matrices projected on a unit sphere. We utilized this concept of GD to quantify a similarity measure between the observed Kennaugh matrix (representation of observed Polarimetric SAR information) and the Kennaugh matrix of a generalized volume scattering model (a realization of scattering media). The similarity measure is then modulated with a factor estimated from the ratio of the minimum to the maximum GD between the observed Kennaugh matrix and the set of elementary targets: trihedral, cylinder, dihedral, and narrow dihedral. In this work, we utilize a time series of C-band quad-pol RADARSAT-2 observations over a semi-arid region in Vijayawada, India. Among the several rice cultivation practices adopted in this region, we analyze the growth stages of direct seeded rice (DSR) and conventional tansplanted rice (TR) with the GRVI and crop biophysical parameters viz., Plant Area Index – PAI. The GRVI is compared for both rice types against the Radar Vegetation Index (RVI) proposed by Kim and van Zyl. A temporal analysis of the GRVI with crop biophysical parameters at different phenological stages confirms its trend with the plant growth stages. Also, the linear regression analysis confirms that the GRVI outperforms RVI with significant correlations with PAI (r ≥ 0.83 for both DSR and TR). In addition, PAI estimations from GRVI show promising retrieval accuracy with Root Mean Square Error (RMSE) <1.05m2m−2 and Mean Absolute Error (MAE) <0.85m2m−2.

Evaluation of RGB, Color-Infrared and Multispectral Images Acquired from Unmanned Aerial Systems for the Estimation of Nitrogen Accumulation in Rice

Unmanned aerial system (UAS)-based remote sensing is one promising technique for precision crop management, but few studies have reported the applications of such systems on nitrogen (N) estimation with multiple sensors in rice (Oryza sativa L.). This study aims to evaluate three sensors (RGB, color-infrared (CIR) and multispectral (MS) cameras) onboard UAS for the estimation of N status at individual stages and their combination with the field data collected from a two-year rice experiment. The experiments were conducted in 2015 and 2016, involving different N rates, planting densities and rice cultivars, with three replicates. An Oktokopter UAS was used to acquire aerial photography at early growth stages (from tillering to booting) and field samplings were taken at a near date. Two color indices (normalized excess green index (NExG), and normalized green red difference index (NGRDI)), two near infrared vegetation indices (green normalized difference vegetation index (GNDVI), and enhanced NDVI (ENDVI)) and two red edge vegetation indices (red edge chlorophyll index (CIred edge), and DATT) were used to evaluate the capability of these three sensors in estimating leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA) in rice. The results demonstrated that the red edge vegetation indices derived from MS images produced the highest estimation accuracy for LNA (R2: 0.79–0.81, root mean squared error (RMSE): 1.43–1.45 g m−2) and PNA (R2: 0.81–0.84, RMSE: 2.27–2.38 g m−2). The GNDVI from CIR images yielded a moderate estimation accuracy with an all-stage model. Color indices from RGB images exhibited satisfactory performance for the pooled dataset of the tillering and jointing stages. Compared with the counterpart indices from the RGB and CIR images, the indices from the MS images performed better in most cases. These results may set strong foundations for the development of UAS-based rice growth monitoring systems, providing useful information for the real-time decision making on crop N management.

An improved scheme for rice phenology estimation based on time-series multispectral HJ-1A/B and polarimetric RADARSAT-2 data

Rice phenology information is critical for farm management and productivity evaluation. Synthetic aperture radar (SAR) and optical remote sensing data are very useful for monitoring rice growth. Therefore, this paper focuses on building a robust, accurate and operational scheme for phenology estimation based on SAR and multispectral remote sensing data. The proposed scheme has three main improvements. First, two main types of rice field - transplanted indica rice field (TRF) and direct-sown japonica rice field (DRF) - were considered individually. Second, 149 SAR signatures and optical Vegetation Indices (VIs) were extracted from time-series of optical and full/compact-pol SAR data and a novel feature selection method based on Monte Carlo experiments and the correlation limitation (MCCL) was proposed. Third, six different phenological labeling cases were considered, with the multiclass relevant vector machine (mRVM) being used to identify different stages. Based on the improvements above, we generated the optimal feature matrix based on the MCCL; this matrix consisted of the optimal feature subset (OFS) used for the identification of each stage. The overall phenological estimation accuracy (OPEA) based on the optical VIs and SAR signatures together (86.59%), was shown to be better than that based on either the optical VIs or the SAR signatures only. In addition, it was significant to consider the differences between the DRF and TRF for rice phenology estimation. The OPEA was higher than 85% when the DRF and TRF were considered separately, much higher than the accuracy obtained (68.89%) when the differences between the DRF and TRF were ignored. Lastly, the availability of the proposed feature selection method MCCL was discussed. It was found that the best OPEA was generated using the OFS selected by the proposed MCCL when the dimensions of the OFS were kept small.