Rice Growth Monitoring Research Articles

Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China).

The successful development of an optimal canopy vegetation index dynamic model for obtaining higher yield can offer a technical approach for real-time and nondestructive diagnosis of rice (Oryza sativa L) growth and nitrogen (N) nutrition status. In this study, multiple rice cultivars and N treatments of experimental plots were carried out to obtain: normalized difference vegetation index (NDVI), leaf area index (LAI), above-ground dry matter (DM), and grain yield (GY) data. The quantitative relationships between NDVI and these growth indices (e.g., LAI, DM and GY) were analyzed, showing positive correlations. Using the normalized modeling method, an appropriate NDVI simulation model of rice was established based on the normalized NDVI (RNDVI) and relative accumulative growing degree days (RAGDD). The NDVI dynamic model for high-yield production in rice can be expressed by a double logistic model: (R2 = 0.8577**), which can be used to accurately predict canopy NDVI dynamic changes during the entire growth period. Considering variation among rice cultivars, we constructed two relative NDVI (RNDVI) dynamic models for Japonica and Indica rice types, with R2 reaching 0.8764** and 0.8874**, respectively. Furthermore, independent experimental data were used to validate the RNDVI dynamic models. The results showed that during the entire growth period, the accuracy (k), precision (R2), and standard deviation of RNDVI dynamic models for the Japonica and Indica cultivars were 0.9991, 1.0170; 0.9084**, 0.8030**; and 0.0232, 0.0170, respectively. These results indicated that RNDVI dynamic models could accurately reflect crop growth and predict dynamic changes in high-yield crop populations, providing a rapid approach for monitoring rice growth status.

Monitoring canopy growth and grain yield of paddy rice in South Korea by using the GRAMI model and high spatial resolution imagery

Monitoring crop conditions and forecasting crop yields are both important for assessing crop production and for determining appropriate agricultural management practices; however, remote sensing is limited by the resolution, timing, and coverage of satellite images, and crop modeling is limited in its application at regional scales. To resolve these issues, the Gramineae (GRAMI)-rice model, which utilizes remote sensing data, was used in an effort to combine the complementary techniques of remote sensing and crop modeling. The model was then investigated for its capability to monitor canopy growth and estimate the grain yield of rice (Oryza sativa), at both the field and the regional scales, by using remote sensing images with high spatial resolution. The field scale investigation was performed using unmanned aerial vehicle (UAV) images, and the regional-scale investigation was performed using RapidEye satellite images. Simulated grain yields at the field scale were not significantly different (p = 0.45, p = 0.27, and p = 0.52) from the corresponding measured grain yields according to paired t-tests (α = 0.05). The model’s projections of grain yield at the regional scale represented the spatial grain yield variation of the corresponding field conditions to within ±1 standard deviation. Therefore, based on mapping the growth and grain yield of rice at both field and regional scales of interest within coverages of a UAV or the RapidEye satellite, our results demonstrate the applicability of the GRAMI-rice model to the monitoring and prediction of rice growth and grain yield at different spatial scales. In addition, the GRAMI-rice model is capable of reproducing seasonal variations in rice growth and grain yield at different spatial scales.

Effect of fluorescence characteristics and different algorithms on the estimation of leaf nitrogen content based on laser-induced fluorescence lidar in paddy rice.

Paddy rice is one of the most significant food sources and an important part of the ecosystem. Thus, accurate monitoring of paddy rice growth is highly necessary. Leaf nitrogen content (LNC) serves as a crucial indicator of growth status of paddy rice and determines the dose of nitrogen (N) fertilizer to be used. This study aims to compare the predictive ability of the fluorescence spectra excited by different excitation wavelengths (EWs) combined with traditional multivariate analysis algorithms, such as principal component analysis (PCA), back-propagation neural network (BPNN), and support vector machine (SVM), for estimating paddy rice LNC from the leaf level with three different fluorescence characteristics as input variables. Then, six estimation models were proposed. Compared with the five other models, PCA-BPNN was the most suitable model for the estimation of LNC by improving R2 and reducing RMSE and RE. For 355, 460 and 556 nm EWs, R2 was 0.89, 0.80 and 0.88, respectively. Experimental results demonstrated that the fluorescence spectra excited by 355 and 556 nm EWs were superior to those excited by 460 nm for the estimation of LNC with different models. BPNN algorithm combined with PCA may provide a helpful exploratory and predictive tool for fluorescence spectra excited by appropriate EW based on practical application requirements for monitoring the N status of crops.

Dynamic Mapping of Rice Growth Parameters Using HJ-1 CCD Time Series Data

The high temporal resolution (4-day) charge-coupled device (CCD) cameras onboard small environment and disaster monitoring and forecasting satellites (HJ-1A/B) with 30 m spatial resolution and large swath (700 km) have substantially increased the availability of regional clear sky optical remote sensing data. For the application of dynamic mapping of rice growth parameters, leaf area index (LAI) and aboveground biomass (AGB) were considered as plant growth indicators. The HJ-1 CCD-derived vegetation indices (VIs) showed robust relationships with rice growth parameters. Cumulative VIs showed strong performance for the estimation of total dry AGB. The cross-validation coefficient of determination ( R C V 2 ) was increased by using two machine learning methods, i.e., a back propagation neural network (BPNN) and a support vector machine (SVM) compared with traditional regression equations of LAI retrieval. The LAI inversion accuracy was further improved by dividing the rice growth period into before and after heading stages. This study demonstrated that continuous rice growth monitoring over time and space at field level can be implemented effectively with HJ-1 CCD 10-day composite data using a combination of proper VIs and regression models.

Estimation of Paddy Rice Variables with a Modified Water Cloud Model and Improved Polarimetric Decomposition Using Multi-Temporal RADARSAT-2 Images

Rice growth monitoring is very important as rice is one of the staple crops of the world. Rice variables as quantitative indicators of rice growth are critical for farming management and yield estimation, and synthetic aperture radar (SAR) has great advantages for monitoring rice variables due to its all-weather observation capability. In this study, eight temporal RADARSAT-2 full-polarimetric SAR images were acquired during rice growth cycle and a modified water cloud model (MWCM) was proposed, in which the heterogeneity of the rice canopy in the horizontal direction and its phenological changes were considered when the double-bounce scattering between the rice canopy and the underlying surface was firstly considered as well. Then, three scattering components from an improved polarimetric decomposition were coupled with the MWCM, instead of the backscattering coefficients. Using a genetic algorithm, eight rice variables were estimated, such as the leaf area index (LAI), rice height (h), and the fresh and dry biomass of ears (Fe and De). The accuracy validation showed the MWCM was suitable for the estimation of rice variables during the whole growth season. The validation results showed that the MWCM could predict the temporal behaviors of the rice variables well during the growth cycle (R2 > 0.8). Compared with the original water cloud model (WCM), the relative errors of rice variables with the MWCM were much smaller, especially in the vegetation phase (approximately 15% smaller). Finally, it was discussed that the MWCM could be used, theoretically, for extensive applications since the empirical coefficients in the MWCM were determined in general cases, but more applications of the MWCM are necessary in future work.

Models for estimating the leaf NDVI of japonica rice on a canopy scale by combining canopy NDVI and multisource environmental data in Northeast China

Remote sensing of rice traits has advanced significantly with regard to the capacity to retrieve useful plant biochemical, physiological and structural quantities across spatial scales. The rice leaf NDVI (normalized difference vegetation index) has been developed and applied in monitoring rice growth, yield prediction and disease status to guide agricultural management practices. This study combined rice canopy NDVI and environmental data to estimate rice leaf NDVI. The test site was a japonica rice experiment located in the eastern city of Shenyang, Liaoning Province, China. This paper describes (1) the use of multiple linear regression to establish four periods of rice leaf NDVI models with good accuracy (R2=0.782–0.903), and (2) how the key point of the rice growth period based on these models was determined. The techniques for modeling leaf NDVI at the point of remote canopy sensing were also presented. The results indicate that the rice leaf NDVI has a high correlation with the canopy NDVI and multisource environmental data. This research can provide an efficient method to detect rice leaf growth at the canopy scale in the future. Keywords: japonica rice, NDVI, leaf models, canopy scale, environmental data DOI: 10.3965/j.ijabe.20160905.2266 Citation: Yu F H, Xu T Y, Cao Y L, Yang G J, Du W, Wang S. Models for estimating the leaf NDVI of japonica rice on a canopy scale by combining canopy NDVI and multisource environmental data in Northeast China. Int J Agric & Biol Eng, 2016; 9(5): 132－142.

Estimation of rice vegetation coverage from DVI of Landsat 7 and 8 data

Monitoring of rice growth is a requirement for high quality rice production. In addtion to plant height, number stem and rice leaf color, vegetation coverage (VC) which represents for percentage of ground covered by rice plant is also considered as an important index to validate rice growth. Thus, the study is to estimate rice vegetation coverage from difference vegetation index (DVI) calculated from reflectance of near-infrared and red band of Landsat 7 and 8 images. The field observations of the reflectance and the VC were carried out in two paddy rice varieties in 2013. Paddy field reflectance was observed by spectrometer Ocean Optics SD2000. The photos of paddies were taken from the height of 1 m by a digital camera in order to calculate the VC. The reflectances of paddy field corresponding to red and near-infrared bands of Landsat 7 and 8 were calculated from the field observation data. Satellite reflectance was also converted from pixel value of Landsat images. According to the data analysis, VC rapidly increased in two fields and got saturation status (VC>90%) at 65 days after transplanting (DAT) in the early July. DVI was approximately 25% when VC saturated. Additionally, DVI had strong correlation with VC with high determination coefficient (r2 =0.9) when VC was less than 90%. Thus, VC were computed from DVI, calculated from reflectances of Landsat images, using a regression model of VC and DVI. From the result of comparison between the estimated and computed VC, the possibility of estimating VC from DVI calculated from Landsat reflectance is confirmed.

Monitoring spatial and temporal variations of the rice backscatter coefficient (σ0) at different phenological stages in Sungai Burong and Sawah Sempadan, Kuala Selangor.

Monitoring rice growth and yield estimation using optical remote sensing data constitutes a big challenge largely due to cloud conditions that are typical of tropical regions. Using Radar remote sensing data helps because it overcomes the cloud issue and distinguishes the behaviour of the radar backscattering of rice crops specifically. This study indicated the temporal change of rice backscatter (σ°) at two different growth stages using HH polarimetric Radarsat-2. The aims of this study are: (1) to identify crop with different life spans based on the backscatter coefficient's values from a single polarisation for understanding the backscatter characteristic of rice over the entire growth cycle, and (2) to understand the advantages and limitations using the RADARSAT-2, C band with HH polarisation. The values of backscattering coefficients have been related to the Malaysia rice crop calendar to get the information of the growth status. The result shows strong backscatter coefficient values on the 21st of May that referred to the reproductive-maturity of rice in the Sawah Sempadan area, and out of season for the Sungai Burong area. While for the August 1st imagery, the result shows weak backscatter values which refers to early vegetative and vegetative-reproductive. The values of backscattering coefficient are found to be much less for early vegetation compare to mature rice crop. In this paper, we have also performed a classification of a rice field using Landsat 8 OLI.

Estimation of rice biophysical parameters using multitemporal RADARSAT-2 images

Compared with optical sensors, synthetic aperture radar (SAR) has the capability of acquiring images in all-weather conditions. Thus, SAR images are suitable for using in rice growth regions that are characterized by frequent cloud cover and rain. The objective of this paper was to evaluate the probability of rice biophysical parameters estimation using multitemporal RADARSAT-2 images, and to develop the estimation models. Three RADARSTA-2 images were acquired during the rice critical growth stages in 2014 near Meishan, Sichuan province, Southwest China. Leaf area index (LAI), the fraction of photosynthetically active radiation (FPAR), height, biomass and canopy water content (WC) were observed at 30 experimental plots over 5 periods. The relationship between RADARSAT-2 backscattering coefficients (σ0) or their ratios and rice biophysical parameters were analysed. These biophysical parameters were significantly and consistently correlated with the VV and VH σ0 ratio (σ0VV/ σ0VH) throughout all growth stages. The regression model were developed between biophysical parameters and σ0VV/ σ0VH. The results suggest that the RADARSAT-2 data has great potential capability for the rice biophysical parameters estimation and the timely rice growth monitoring.

A study of the use of COSMO-SkyMed SAR PingPong polarimetric mode for rice growth monitoring

ABSTRACTThe sensitivity of COSMO-SkyMed (CSK) incoherent dual-polarimetric synthetic aperture radar (SAR) data to the rice growth cycle is investigated here. State-of-the-art scattering models are used, together with a time series of 24 CSK SAR images collected in Mekong Delta, Vietnam in 2014, to interpret the behaviour of multi-polarization features with respect to the different phenological stages that characterize rice growth. Experimental results show the multi-polarization features sensitivity with respect to rice growth cycle and witness that a joint use of the co-polarized channels (i.e. co-polarized ratio or correlation between co-polarized channels) allows identifying scattering behaviours that are compatible with four stages of the rice growth cycle.

小型UAVとSfM-MVSを使用した近接画像からの水稲生育モニタリング

小型UAVとSfM-MVSを使用した近接画像からの水稲生育モニタリング

Rice Growth Monitoring by Means of X-Band Co-polar SAR: Feature Clustering and BBCH Scale

Precision agriculture research, which aims to monitor agricultural fields and to manage agricultural practice by considering overall environmental impacts, has gained momentum with the recent improvements in the remote sensing area. The objective of this letter, as a part of precision farming, is to implement Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) scale assignment in plant growth monitoring by means of SAR. The proposed approach copes with structural heterogeneity in agricultural fields by grouping together similar morphologies. For this, densely cultivated paddy rice fields are analyzed using TerraSAR-X (TSX) co-polar SAR data. For generating structurally similar groups, K-means clustering is used in a polarimetric feature vector space, which is composed of backscattering intensities and polarimetric phase differences. This step is followed by a preliminary classification approach based on the temporal separability of the explanatory parameters. In the last step of the proposed methodology, assigned classes are updated based on the biological principles that are followed in rice cultivation. This letter provides the results of the proposed algorithm and compares them to the standard threshold-based approach in two independent agricultural areas. The results show the superiority of the feature-clustering-based classification compared with the standard approach in handling field heterogeneity.

Rice growth monitoring by RC multi-copter and SfM technique

Rice growth monitoring by RC multi-copter and SfM technique

The acquisition of rice growth monitoring by small UAV

The acquisition of rice growth monitoring by small UAV

Simulation and mapping of rice growth and yield based on remote sensing

The GRAMI crop growth model uses remote sensing data and thus has the potential to produce maps of crop growth and yield. A pixel-based crop information delivery system (CIDS) to simulate and map rice (Oryza sativa) growth and yield was developed using GRAMI. The GRAMI-rice model was parameterized using field data obtained at Chonnam National University, Gwangju, Republic of Korea, in 2011 and 2012. The model was separately validated using field data obtained at the same research site in 2009 and 2010. The model was then integrated into the CIDS to produce two-dimensional (2-D) maps of crop growth and yield. Simulated values of rice growth and yield agreed well with the corresponding measurements in both parameterization and evaluation. The simulated yields were in statistical agreement with the corresponding measured yields according to paired t tests (p=0.415 for parameterization and p=0.939 for validation). The CIDS accurately produced 2-D maps of rice growth and yield. The GRAMI-rice CIDS has simple input requirements and will be useful for regional rice growth monitoring and yield mapping projects.

Rice growth monitoring using simulated compact polarimetric C band SAR

In this study, a set of nine compact polarimetric (CP) images were simulated from polarimetric RADARSAT-2 data acquired over a test site containing two types of rice field in Jiangsu province, China. The types of rice field in the test site were (1) transplanted hybrid rice fields, and (2) direct-sown japonica rice fields. Both types have different yields and phenological stages. As a first step, the two types of rice field were distinguished with 94% and 86% accuracy respectively through analyzing CP synthetic aperture radar (SAR) observations and their behavior in terms of scattering mechanisms during the rice growth season. The focus was then on phenology retrieval for each type of rice field. A decision tree (DT) algorithm was built to fulfill the precise retrieval of rice phenological stages, in which seven phenological stages were discriminated. The key criterion for each phenological stage was composed of 1–4 CP parameters, some of which were first used for rice phenology retrieval and found to be very sensitive to rice phenological changes. The retrieval results were verified at parcel level for a set of 12 stands of rice and up to nine observation dates per stand. This gave an accuracy of 88–95%. Throughout the phenology retrieval process, only simulated CP data were used, without any auxiliary data. These results demonstrate the potential of CP SAR for rice growth monitoring applications.

Integrated sensor system for monitoring rice growth conditions based on unmanned ground vehicle system.

Integrated sensor system for monitoring rice growth conditions based on unmanned ground vehicle system.

Retrieving canopy height and density of paddy rice from Radarsat-2 images with a canopy scattering model

Quantification of rice biophysical properties is important not only for rice growth monitoring and cropping management, but for understanding carbon cycle in agricultural ecosystems. In this study, a rice canopy scattering model (RCSM) was firstly utilized to simulate rice backscatter with a root mean square error (RMSE) <1 dB in comparison with the C-band, HH-polarization Radarsat-2 images. And then, by integrating the model with a generic algorithm optimization tools (GOAT), canopy height and density were separately retrieved from Radarsat-2 images acquired in three rice growth stages (elongation stage, heading stage and yellow ripening stage). Accuracy analysis showed that the two parameters could be retrieved with the RMSE of 5.4 cm in height, and 26 (#/m2) in density. The study demonstrated the potential of Radarsat-2 SAR data for quantitative mapping of biophysical parameters of paddy rice.

Capability of C-band backscattering coefficients from high-resolution satellite SAR sensors to assess biophysical variables in paddy rice

High-resolution (ca. 1m) synthetic aperture radar (SAR) sensors have great potential for all-weather monitoring of crop biophysical variables in small and mosaic crop fields in Asia. Rice is the most important staple crop in monsoon Asia, and the timely monitoring of rice growth is critical for precision farming and the assessment of productivity. The objective of this study was to determine the potential capability of backscattering coefficients (σ0) from satellite C-band SAR sensors for the assessment of biophysical variables in rice. SAR images were acquired by a Radarsat-2 sensor in spotlight mode during the critical growth stages over 4years in one of the major rice-producing areas of Japan. Detailed plant biophysical measurements were made concurrently with the SAR observations. The seasonal consistency of C-band σ0 was clearly demonstrated. The baseline σ0 values (minimum σ0 for zero-biomass paddy fields) were determined to be −28.5dB in VH and −21.1dB in HH and VV, respectively. The dynamic change in σ0 during the full range of rice growth was similar (ca. 12dB) in all polarizations. A comprehensive analysis revealed the response of C-band σ0 to biophysical canopy variables. High or moderate sensitivity of σ0 to canopy height, water content, or chlorophyll content was superficial and was attributable to the change in leaf biomass and structure. Both the leaf area index (LAI) and leaf biomass were significantly and consistently correlated with σ0 throughout all growth stages. These relationships were expressed by exponential curves with high coefficients of determination, although σ0 saturates at around a LAI of 3 and a leaf biomass of 180gDWm−2. The response of σ0 to total biomass was expressed by an exponential function with a high coefficient of determination, but the sensitivity was clear only within the lower 20% range of the seasonal maximum biomass. The C-band σ0 had the highest correlation with fAPAR, and the σ0–fAPAR relationship was linear throughout the growth stages. The results suggest the suitability of C-band σ0 for the assessment of LAI or fAPAR and show promise for the timely monitoring of rice growth by C-band SAR and/or through its constellation with optical sensors.

Hyperspectral characteristics and growth monitoring of rice (Oryza sativa) under asymmetric warming

Climate warming shows great diurnal variation, with a higher warming rate at night-time, and consequently causes significant impacts on rice growth and development. Soil and plant analyser development (SPAD) values and leaf area index (LAI) are important parameters for indicating rice growth potential. The non-destructive, quick assessment of SPAD values and LAI is necessary for growth diagnosis in rice production. The objectives of this study were to determine the effects of asymmetric warming on rice growth and development, to define the relationships of SPAD values and LAI to ground-based canopy hyperspectral reflectance and derivative parameters, and to establish quantitative models for real-time monitoring of rice growth using sensitive spectral parameters under asymmetric warming. Two field warming experiments were performed in Nanjing in Jiangsu Province, China, to investigate the effects of asymmetric warming (all-day warming, AW; daytime warming from 7:00 to 19:00, DW; and night-time warming from 19:00 to 7:00, NW) on the growth and development of the Huaidao 5 rice cultivar, using a free air temperature increase (FATI) apparatus. The daily mean rice canopy temperatures in the AW, DW, and NW treatment plots were 2.0°C, 1.1°C, and 1.3°C higher, respectively, than those in the control (CK) plots. Canopy hyperspectral reflectance, SPAD, and LAI were measured under warming treatments during different growth stages. The results showed that rice SPAD and LAI decreased under warming treatments, with significant differences among certain growth stages compared with CK. The SPAD and LAI dynamics during growth stages exhibited single-peak patterns. Sensitive spectral bands occurred mostly within the visible light and near-infrared regions. An integrated linear regression equation relating SPAD to first-order derivative 514 (FD514) and ratio vegetation index (810,560) (RVI (810,560)) accurately described the dynamic pattern of SPAD changes, the coefficients of determination (R2) of 0.53 and 0.59, with the standard errors (SEs) of 0.85 and 1.13. The hyperspectral parameters first-order derivative 506 (FD506) and blue edge area (SDb) exhibited a linear relationship with LAI, with R2 values of 0.79 and 0.80 and SE values of 0.69 and 0.23, respectively. When independent data were used to test the derived equations, the R2 values between the measured and estimated SPAD from FD514 and RVI (810,560) were 0.72 and 0.60, with root mean square errors (RMSEs) of 4.34 and 4.44 and relative errors (REs) of 11.00% and 12.00%, respectively. For spectral parameters FD506 and SDb, the R2 values between the measured and estimated LAI values were 0.75 and 0.75, with RMSEs of 0.57 and 0.48 and REs of 17.00% and 4.00%, respectively. The strong fit between the measured and estimated values indicated that the present models based on hyperspectral reflectance could be used for reliable SPAD and LAI estimation under asymmetric warming.