Spatial and timely information on crop and filed conditions is one of the most important basics for rational and efficient planning and management in agriculture. Remote sensing, GIS, and modeling are powerful tools for such applications. This paper presents an overview of the state of the art in remote sensing of crop and field conditions with some case studies. It is also shown that a synergistic linkage between process-based models and remote sensing signatures enables us to estimate the multiple crop/ecosystem variables at a dynamic mode. Remotely sensed information can greatly reduce the uncertainty of simulation models by compensating for insufficient availability of data or parameters. This synergistic approach allows the effective use of infrequent and multi-source remote sensing data for estimating important ecosystem variables such as biomass growth and ecosystem $CO_2$ flux. This paper also shows a geo-spatial information system that enables us to integrate, search, extract, process, transform, and calculate any part of the data based on ID#, attributes, and/or by river-basin boundary, administrative boundary, or boundaries of arbitrary shape/size all over Japan. A case study using the system demonstrates that the nitrogen load from fertilizer was closely related to nitrate concentration of groundwater. The combined use of remote sensing, GIS and modeling would have great potential for various agro-ecosystem applications.

Rice yield and protein content have been shown to be highly variable across paddy fields. In order to characterize this spatial variability of rice within a field, the two-year experiments were conducted in 2002 and 2003 in a large-scale rice field of $6,600m^2$ In year 2004, an experiment was conducted to know if prescribed N for site-specific fertilizer management at panicle initiation stage (VRT) could reduce spatial variation in yield and protein content of rice while increasing yield compared to conventional uniform N topdressing (UN, ,33 kg N/ha at PIS) method. The trial field was subdivided into two parts and each part was subjected to UN and VRT treatment. Each part was schematically divided in $10\times10m$ grids for growth and yield measurement or VRT treatment. VRT nitrogen prescription for each grid was calculated based on the nitrogen (N) uptake (from panicle initiation to harvest) required for target rice protein content of $6.8\%$, natural soil N supply, and recovery of top-dressed N fertilizer. The required N uptake for target rice protein content was calculated from the equations to predict rice yield and protein content from plant growth parameters at panicle initiation stage (PIS) and N uptake from PIS to harvest. This model equations were developed from the data obtained from the previous two-year experiments. The plant growth parameters for this calculation were predicted non-destructively by canopy reflectance measurement. Soil N supply for each grid was obtained from the experiment of year 2003, and N recovery was assumed to be $60\%$ according to the previous reports. The prescribed VRT N ranged from 0 to 110kg N/ha with average of 57kg/ha that was higher than 33kg/ha of UN. The results showed that VRT application successfully worked not only to reduce spatial variability of rice yield and protein content but also to increase rough rice yield by 960kg/ha. The coefficient of variation (CV) for rice yield and protein content was reduced significantly to $8.1\%\;and\;7.1\%$ in VRT from $14.6\%\;and\;13.0\%$ in UN, respectively. And also the average protein content of milled rice in VRT showed very similar value of target protein content of $6.8\%$. Although N use efficiency of VRT compared to UN was not quantified due to lack of no N control treatment, the procedure used in this paper for VRT estimation was believed to be reliable and promising method for managing within-field spatial variability of yield and protein content. The method should be received further study before it could be practically used for site-specific crop management in large-scale rice field.

Remote sensing(RS)- and geographic information system(GIS)-based information management to measure and assess agri-environment schemes, and to quantify and map environment indicators for nature and land use, climate change, air, water and energy balance, waste and material flow is in high demand because it is very helpful in assisting decision making activities of farmers, government, researchers, and consumers. The versatility and ability of RS and GIS containing huge soil database to assess agricultural environment spatially and temporally at various spatial scales were investigated. Spectral and microwave observations were carried out to characterize crop variables and soil properties. Multiple sources RS data from ground sensors, airborne sensors, and also satellite sensors were collected and analyzed to extract features and land cover/use for soils, crops, and vegetation for support precision agriculture, soil/land suitability, soil property estimation, crop growth estimation, runoff potential estimation, irrigated and the estimation of flooded areas in paddy rice fields. RS and GIS play essential roles in a management and monitoring information system. Biosphere-atmosphere interection should also be further studied to improve synergistic modeling for environment and sustainability in agri-environment schemes.

Leaf color and plant vigor are key indicators of crop health. These visual plant attributes are frequently used by greenhouse managers, producers, and consultants to make water, nutrient, and disease management decisions. Remote sensing techniques can quickly quantify soil and plant attributes, but it requires humans to translate such data into meaningful information. Over time, scientists have used reflectance data from individual wavebands to develop a series of indices that attempt to quantify things like soil organic matter content, leaf chlorophyll concentration, leaf area index, vegetative cover, amount of living biomass, and grain yield. The recent introduction of active sensors that function independent of natural light has greatly expanded the capabilities of scientists and managers to obtain useful information. Characteristics and limitations of active sensors need to be understood to optimize their use for making improved management decisions. Pot experiments involving sand culture were conducted in 2003 and 2004 in a green house to evaluate corn and red pepper biomass. The rNDVI, gNDVI and aNDVI by ground-based remote sensors were used for evaluation of corn and red pepper biomass. The result obtained from the case study was shown that ground remote sensing as a non-destructive real-time assessment of plant nitrogen status was thought to be a useful tool for in season crop nitrogen management providing both spatial and temporal information.