Shanyu Huang

Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice

Abstract Appropriate field management requires methods of measuring plant height with high precision, accuracy, and resolution. Studies show that terrestrial laser scanning (TLS) is suitable for capturing small objects like crops. In this contribution, the results of multitemporal TLS surveys for monitoring plant height on paddy rice fields in China are presented. Three campaigns were carried out on a field experiment and on a farmer’s conventionally managed field. The high density of measurement points allows us to establish crop surface models with a resolution of 1 cm, which can be used for deriving plant heights. For both sites, strong correlations (each R 2 = 0.91 between TLS-derived and manually measured plant heights confirm the accuracy of the scan data. A biomass regression model was established based on the correlation between plant height and biomass samples from the field experiment ( R 2 = 0.86 ). The transferability to the farmer’s field was supported with a strong correlation between simulated and measured values ( R 2 = 0.90 ). Independent biomass measurements were used for validating the temporal transferability. The study demonstrates the advantages of TLS for deriving plant height, which can be used for modeling biomass. Consequently, laser scanning methods are a promising tool for precision agriculture.

Satellite Remote Sensing-Based In-Season Diagnosis of Rice Nitrogen Status in Northeast China

Rice farming in Northeast China is crucially important for China’s food security and sustainable development. A key challenge is how to optimize nitrogen (N) management to ensure high yield production while improving N use efficiency and protecting the environment. Handheld chlorophyll meter (CM) and active crop canopy sensors have been used to improve rice N management in this region. However, these technologies are still time consuming for large-scale applications. Satellite remote sensing provides a promising technology for large-scale crop growth monitoring and precision management. The objective of this study was to evaluate the potential of using FORMOSAT-2 satellite images to diagnose rice N status for guiding topdressing N application at the stem elongation stage in Northeast China. Five farmers’ fields (three in 2011 and two in 2012) were selected from the Qixing Farm in Heilongjiang Province of Northeast China. FORMOSAT-2 satellite images were collected in late June. Simultaneously, 92 field samples were collected and six agronomic variables, including aboveground biomass, leaf area index (LAI), plant N concentration (PNC), plant N uptake (PNU), CM readings and N nutrition index (NNI) defined as the ratio of actual PNC and critical PNC, were determined. Based on the FORMOSAT-2 imagery, a total of 50 vegetation indices (VIs) were computed and correlated with the field-based agronomic variables. Results indicated that 45% of NNI variability could be explained using Ratio Vegetation Index 3 (RVI3) directly across years. A more practical and promising approach was proposed by using satellite remote sensing to estimate aboveground biomass and PNU at the panicle initiation stage and then using these two variables to estimate NNI indirectly (R2 = 0.52 across years). Further, the difference between the estimated PNU and the critical PNU can be used to guide the topdressing N application rate adjustments.

Potential of RapidEye and WorldView-2 Satellite Data for Improving Rice Nitrogen Status Monitoring at Different Growth Stages

For in-season site-specific nitrogen (N) management of rice to be successful, it is crucially important to diagnose rice N status efficiently across large areas within a short time frame. In recent studies, the FORMOSAT-2 satellite images with traditional blue (B), green (G), red (R), and near-infrared (NIR) wavebands have been used to estimate rice N status due to its high spatial resolution, daily revisit capability, and relatively lower cost. This study aimed to evaluate the potential improvements of RapidEye and WorldView-2 data over FORMOSAT-2 for rice N status monitoring, as the former two sensors provide additional wavelengths besides the traditional four wavebands. Ten site-year N rate experiments were conducted in Jiansanjiang, Heilongjiang Province of Northeast China from 2008 to 2011. Plant samples and field hyperspectral data were collected at three growth stages: panicle initiation (PI), stem elongation (SE), and heading (HE). The canopy-scale hyperspectral data were upscaled to simulate the satellite bands. Vegetation index (VI) analysis, stepwise multiple linear regression (SMLR), and partial least squares regression (PLSR) were performed to derive plant N status indicators. The results indicated that the best-performed VIs calculated from the simulated RapidEye and WorldView-2 bands, especially those based on the red edge (RE) bands, explained significantly more variability for above ground biomass (AGB), plant N uptake (PNU), and nitrogen nutrition index (NNI) estimations than their FORMOSAT-2-based counterparts did, especially at the PI and SE stages. The SMLR and PLSR models based on the WorldView-2 bands generally had the best performance, followed by the ones based on the RapidEye bands. The SMLR results revealed that both the NIR and RE bands were important for N status estimation. In particular, the NIR1 band (760–900 nm from RapidEye or 770–895 nm from WorldView-2) was most important for estimating all the N status indicators. The RE band (690–730 nm or 705–745 nm) improved AGB, PNU, and NNI estimations at all three stages, especially at the PI and SE stages. AGB and PNU were best estimated using data across the stages while plant N concentration (PNC) and NNI were best estimated at the HE stage. The PLSR analysis confirmed the significance of the NIR1 band for AGB, PNU, and NNI estimations at all stages except for the HE stage. It also showed the importance of including extra bands (coastal, yellow, and NIR2) from the WorldView-2 sensor for N status estimation. Overall, both the RapidEye and WorldView-2 data with RE bands improved the results relative to FORMOSAT-2 data. However, the WorldView-2 data with three extra bands in the visible and NIR regions showed the highest potential in estimating rice N status.

Critical Nitrogen Dilution Curve for Rice Nitrogen Status Diagnosis in Northeast China

Abstract In-season diagnosis of crop nitrogen (N) status is crucial for precision N management. Critical N dilution curve and N nutrition index (NNI) have been proposed as effective methods for diagnosing N status of different crops. Critical N dilution curves have been developed for Indica rice in the tropical and temperate zones and Japonica rice in the subtropical-temperate zone, but they have not been evaluated for short-season Japonica rice in Northeast China. The objective of this study was to evaluate the previously developed critical N dilution curves for rice in Northeast China, and develop a more suitable critical N dilution curve in this region. A total of 17 N rate experiments were conducted in Jiansanjiang, Heilongjiang province in Northeast China from 2008 to 2013. The results indicated that none of the two previously developed critical N dilution curves was suitable for diagnosing N status of the short season Japonica rice in Northeast China. A new critical N dilution curve was developed and can be described by the equation N c = 27.7W −0.34 (aboveground biomass ≥ 1 Mg DM ha −1 ) or N c = 27.7 g kg −1 DM (aboveground biomass −1 ). This new curve was lower than those previous curves. It was validated using a separate dataset, and it could discriminate non-limiting and limiting N nutritional conditions. More studies are needed to further evaluate it for diagnosing N status of different rice cultivars in Northeast China and develop efficient non-destructive methods to estimate NNI for practical applications.

Estimating rice nitrogen status with the Crop Circle multispectral active canopy sensor

The objective of this study was to determine which vegetation indices calculated from the Crop Circle active sensor bands will perform best for estimating rice nitrogen (N) status. Six field experiments were conducted in Sanjiang Plain in Heilongjiang Province, China during 2011 and 2012. The results of the study indicated that six vegetation indices were significantly related to N uptake and nitrogen nutrition index (NNI) across different years, varieties and growth stages. Subsequently, six farm fields in two different villages were selected as datasets to validate the models developed in this study. The results indicated that using Normalized Difference Red Edge (NDRE) to predict plant N uptake had the highest coefficient of determination (R2, 0.76), the lowest root mean square error (RMSE, 17.00 kg N/ha), and relative error (RE, 23.61%) across different years, varieties and locations. The NDRE also gave the best prediction for NNI, with R2 being 0.76, RMSE being 0.09 and RE being 11.63%. The second best performing vegetation index was Red Edge Chlorophyll Index (CIRE), which performed similarly to NDRE.

Proximal fluorescence sensing for in-season diagnosis of rice nitrogen status

The objective of this study was to evaluate the potential of using Multiplex 3, a hand-held canopy fluorescence sensor, to determine rice nitrogen (N) status at different growth stages. In 2013, a paddy rice field experiment with five N fertilizer treatments and two varieties was conducted in Northeast China. Field samples and fluorescence data were collected simultaneously at the panicle initiation (PI), stem elongation (SE), and heading (HE) stages. Four N status indicators, leaf N concentration (LNC), plant N concentration (PNC), plant N uptake (PNU) and N nutrition index (NNI), were determined. The preliminary results indicated that different N application rates significantly affected most of the fluorescence variables, especially the simple fluorescence ratios (SFR_G, SFR_R), flavonoid (FLAV), and N balance indices (NBI_G, NBI_R). These variables were highly correlated with N status indicators. More studies are needed to further evaluate the accuracy of rice N status diagnosis using fluorescence sensing at different growth stages.