Maps are an indispensable platform in every field such as disaster management, education and urban planning. With the expansion of the Internet, the use of web maps has been progressing in recent years. Thanks to Web maps, we have been able to do a lot of things that were not possible with paper maps. This paper introduces GSI Maps, a web map provided by the Geospatial Information Authority of Japan, which provides various geospatial information and have a lot of functions for their use.
Agricultural Data Collaboration Platform WAGRI started full operations by the National Agriculture and Food Research Organization (NARO) from April 2019. This paper introduces the overview of WAGRI and the situation of full operations by NARO. We discuss the business model (BtoBtoC model) of WAGRI, the Application Programming Interface (APIs) for access the data, and agriculture-related data such as on pesticides, fertilizers, maps, weather, growth prediction, soil and so on.
We propose a method for obtaining validation data for the moderate-resolution-sensor-derived Fire Radiative Power (FRP) products from the high spatial resolution satellite data. This method uses two shortwave infrared channels (1.6 ［μm］ and 2.2 ［μm］) to retrieve FRP validation data. Although fire-contaminated high spatial resolution pixels are mostly saturated because of intense fire radiation, our method exploits the saturated fire pixels for FRP validation data retrieval and as the result, an estimation width is obtained as FRP validation data. In this study, FRP validation data was constructed using the ASTER high resolution data and was evaluated by comparing the simultaneous observed MODIS FRP products. The correlation coefficients between our method derived FRP validation data and the MODIS FRP were approximately between 0.7 and 0.9. The corresponding rates between our method derived FRP validation data and the MODIS FRP were approximately between 0.7 and 0.8.
Crop maps are useful for agricultural field management and synthetic aperture radar data are attractive for generating crop classification because of their all-weather, all-day imaging capability. Additionally, classification algorithms are essential for generating accurate and multi-Grained Cascade Forest, which is also called ‘deep forest', was developed and its high performances have been shown for pattern recognition, voice recognition and so on. In this study, the capability of TerraSAR-X (including TanDEM-X) dual-polarimetric data for crop classification in the Tokachi Plain, Japan was investigated and the comparison of three different classification algorithms including classification and regression tree, random forests and deep forest was conducted.
Crop growth monitoring techniques using remotely sensed data have been required for precise management of crop production. In this study, multi-temporal TerraSAR-X (including TanDEM-X) dual-polarimetric data were used for monitoring the growth of beans and beetroot. In addition to gamma naught values, polarimetric parameters were calculated using m-chi decomposition and dual polarization entropy/alpha decomposition. The results showed that the gamma naught of VV polarization and two polarimetric parameters of the m-chi decomposition (single-or odd-bounce (Odd) and randomly oriented (Rnd) scattering) from X-band SAR data possess potential for monitoring crop growth.
Green tea-flavored sweets have become popular and then some techniques, such as shading treatments, have been developed for increasing chlorophyll content, which is important for improving tea leaf appearance. Chlorophyll content estimation is one of the most common applications of hyperspectral remote sensing, however, previous studies were based on measurements under relatively low stress conditions. The PROSPECT model is one of the most famous radiative transfer models and has been widely used for retrieving chlorophyll, carotenoid, or dry matter content. In this study, the performances of the three versions, which can estimate chlorophyll content from reflectance, were compared. Using PROSPECT-D, root mean square errors of 7.12μg/cm2 was achieved and then it could be a strong tool for assessing the qualities of shade grown tea.
Theanine is the most abundant free amino acid in tea leaves (Camellia sinensis) and it is also one of important factors for assessing the quality of tea ; thus, developing an in-situ method to monitor theanine is useful for agricultural management. Some hyperspectral remote sensing techniques, especially spectral indices, have been applied for assessing vegetation properties such as pigment content and water content. In this study, searching for new indices was attempted based on hyperspectral reflectance. The newly identified index, which is expressed as a differential type of index using reflectance at 1735 nm and 1755 nm, possessed a great performance, achieving a root mean square error with leave-one-out cross validation of 0.065 mg/cm2.