Masayasu Maki

Spectral Index for Quantifying Leaf Area Index of Winter Wheat by Field Hyperspectral Measurements: A Case Study in Gifu Prefecture, Central Japan

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 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.

Rice-Planted Area Mapping Using Small Sets of Multi-Temporal SAR Data

A rice-planted area map is a basic information resource for rice production management. Synthetic aperture radar (SAR) is an appropriate technique for rice mapping and so far is mostly based on extracting time series changes of backscattering (σ0) in a rice-planted area. However, sometimes there is not enough data to extract the σ0 curve for the area. To overcome this problem of a lack of data, we propose a method to detect rice-planted area by using small sets of multi-temporal SAR data. This method also addresses the fluctuation of σ0 values between SAR measurements. We have applied the method using multi-temporal ALOS/PALSAR data acquired over five years during the dry season. The rice-planted area was well detected and the viability of this method was demonstrated.

Empirical Regression Models for Estimating Multiyear Leaf Area Index of Rice from Several Vegetation Indices at the Field Scale

Leaf area index (LAI) is among the most important variables for monitoring crop growth and estimating grain yield. Previous reports have shown that LAI derived from remote sensing data can be effectively applied in crop growth simulation models for improving the accuracy of grain yield estimation. Therefore, precise estimation of LAI from remote sensing data is expected to be useful for global monitoring of crop growth. In this study, as a preliminary step toward application at the regional and global scale, the suitability of several vegetation indices for estimating multi-year LAI were validated against field survey data. In particular, the performance of a vegetation index known as time-series index of plant structure (TIPS), which was developed by the authors, was evaluated by comparison with other well-known vegetation indices. The estimated equation derived from the relationship between TIPS and LAI was more accurate at estimating LAI than were equations derived from other vegetation indices. Although further research is required to demonstrate the effectiveness of TIPS, this study indicates that TIPS has the potential to provide accurate estimates for multi-year LAI at the field scale.

A decision-making model for rice paddy cropping in an urbanizing area of the Lao PDR

In Southeast Asia, economic and population growth are expected in the near future. Rapid change is anticipated especially in the Lao PDR. Concern has been expressed that population growth will lead to an increased demand for food and economic growth to changes in the use of land. For food production to keep pace with the growth of population, it is very important to understand decision-making in rice paddy cultivation in urbanizing areas; for this reason, this study with the SEM model was conducted. The original data were collected by a questionnaire survey in some Lao villages; the survey included questions on various conditions, such as the availability of water in the dry season (irrigation), the access to a city, and job opportunities other than farming. The findings of the study demonstrate that the planting of a second rice crop was related to such factors as the productivity of rain-fed rice and cash crops and, most importantly, job opportunities other than farming.

Detection of rice-planted area using multi-temporal ALOS/PALSAR data

A rice-planted area map is a basic information resource for rice production management. In this study, a method is proposed to detect the annual rice-planted area by using multi-temporal phased array type L-band synthetic aperture radar. This method addresses two problems: (i) the σ0 value of planted paddies is not consistent; and (ii) annual data fails to reveal time series change of σ0 value in rice-planted area. Data were acquired from both planted and non-planted paddies in the dry season. Accuracy assessment was performed based on field survey data. As a result, the proposed method was effective at revealing the annual rice-planted area. It was also found that data acquired in the rice growth stage contributes to determining the rice-planted area with high accuracy. Further investigation is required that sets more detailed classes to reduce the rate of misclassification and eliminates small regions in detected images.