No abstract

Timely and nondestructive monitoring of leaf area index (LAI) using remote sensing techniques is crucial for precise and efficient management of crops. In this paper, a new spectral index (SI) for estimating LAI of winter wheat (Triticum aestivum L.) is proposed on the basis of field hyperspectral measurements. A simple index based on the empirical relationships between LAIs and SIs of all available two-waveband combinations from hyperspectral data is developed by considering the difference between reflectance values at 760 and 739 nm (DSIR760-R739 = R760 -R739). Among published and newly developed SIs, DSIR760-R739 exhibited a significant and strong linear relationship with LAI OPEN ACCESSRemote Sens. 2015, 7 5330 and showed outstanding performance in LAI assessments. The permissible bandwidths for broad-band DSIR760-R739 investigated using simulated reflectance were 5 nm for both 760 and 739 nm center wavelengths. The results indicate that the linear regression model based on the narrow-band and broad-band DSIR760-R739 is a simple but accurate method for timely and nondestructive monitoring of LAI.

Reflectance and vegetation indices obtained from aerial images are often used for monitoring crop fields. In recent years, unmanned aerial vehicles (UAVs) have become popular and aerial images have been collected under various solar radiation conditions. The value of observed reflectance and vegetation indices are considered to be affected by solar radiation conditions, which may lead to inaccurate estimations of crop growth. In this study, in order to evaluate the effect of solar radiation conditions on aerial images, canopy reflectance in paddy fields was simulated by a radiative transfer model, FLiES (Forest Light Environmental Simulator), for various solar radiation conditions and canopy structures. Several parameters including solar zenith angle, proportion of diffuse light for incident sunlight, plant height, coordinates of plants and leaf area density, were tested in FLiES. The simulation results showed that the solar zenith angle did not vary the canopy reflectance under the conditions of the proportion of diffuse light at 1.0, but the variation was greater at lower proportions of diffuse light. The difference in reflectance caused by solar radiation was 0.01 and 0.1 at the maximum for red and near-infrared bands, respectively. The simulation results also showed that the differences in reflectance affect vegetation indices (Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index 2 (EVI2)). The variation caused by solar radiation conditions was the least for NDVI and the greatest for SR. However, NDVI was saturated at the least leaf area index (LAI), whereas SR was only slightly saturated. EVI2 was intermediate between SR and NDVI, both in terms of variation and saturation. The simulated reflectance and vegetation indices were similar to those obtained from the aerial images collected in the farmers’ paddy fields. These results suggest that a large proportion of diffuse light (close to 1.0) or a middle range of solar zenith angle (45 to 65 degrees) may be desirable for UAV monitoring. However, to maintain flexibility of time and occasion for UAV monitoring, EVI2 should be used to evaluate crop growth, although calibration based on solar radiation conditions is recommended.

Food security has become a serious concern recently in Southeast Asia. The reduction of agricultural land because of economic development is decreasing the food supply. Simultaneously, due to rapid population growth, the food demand is increasing. Therefore, to ensure a stable food supply, it is important to estimate the supply capability of rice, which is the staple food in most Asian countries. In this study, a crop model (SIMRIW-RS) that can combine remote sensing data with a crop model (SIMRIW) was used to estimate rice yield at a regional scale. This model was applied to the estimation of rice yield in paddy fields located in the suburbs of Vientiane, Laos. Satellite (COSMO-SkyMed)-derived data for leaf area index (LAI) were integrated into SIMRIW-RS, and the transplanting date detected by COSMO-SkyMed was used to set the starting date of the simulation. Results were verified by surveying farmers. Transplanting dates were detected with high accuracy in all but a few fields. On the basis of the results of regression analysis between actual LAIs and the corresponding backscatter coefficients of COSMO-SkyMed, we suggest that COSMO-SkyMed can estimate LAIs at early growth stages when LAI is small. The results of yield estimation after integrating the LAIs derived from COS-MO-SkyMed data into SIMRIW-RS indicated that the estimation accuracy of the rice yield was improved compared with the estimation result without adjusting parameters in the model, and this held so long as LAI was retrieved with high accuracy by satellite data. However, when LAI could not be estimated accurately, integration has the potential to worsen the model's accuracy compared with the estimation result without any such readjustment. This study therefore indicates that SIMRIW-RS has the potential to estimate rice yield accurately when the LAI of rice is estimated with high accuracy from satellite data.