Soil carbon stocks (SCSs) in an upland area of Tokachi District, Hokkaido, Japan, were estimated by satellite remote sensing and a soil survey. The soil parent materials in the studied area were alluvial deposits, volcanic ash plus alluvial deposits, and volcanic ash. Surface soil carbon concentrations (SSCCs) were negatively correlated with satellite image data (green, red, and near-infrared reflectance) for each parent material. The highest correlations between reflectance and SSCCs were obtained from red wavelength reflectance for alluvial deposits (r = –0.82, p < 0.01) and volcanic ash plus alluvial deposits (r = –0.91, p < 0.01), and from near-infrared reflectance for volcanic ash (r = –0.90, p < 0.01). We generated an SSCC map of the study area using the regression equations and satellite reflectance data. The soil survey results showed that SSCCs were positively correlated with SCSs in the 0–30 cm depth interval for each parent material (best-fit regressions: alluvial deposits, r = 0.97; volcanic ash plus alluvial deposits, r = 0.97; volcanic ash, r = 0.97), and they were also positively correlated with SCSs in the 30–95 cm depth interval for volcanic ash (r = 0.94). We were therefore able to generate a map of estimated SCSs from the SSCC map and the regression equations developed between SSCCs and SCSs. The estimated and measured SCSs in both depth intervals showed an almost 1:1 relationship, with root-mean-square errors of 19.5 (0–30 cm) and 28.6 Mg carbon (C) ha^–1 (30–95 cm). According to the SCS maps, SCSs at 0–30 cm depth in areas of alluvial deposits, volcanic ash plus alluvial deposits, and volcanic ash were mostly 50–150, 100–200, and 50–250 Mg C ha^–1, respectively, whereas at 30–95 cm depth in the volcanic ash area they were mostly less than 250 Mg C ha^–1.

Soil properties and functions are dramatically altered by changes in agricultural land use. However, little is known about how ecosystem C stock and its partitioning change with deforestation for agricultural land use, especially in cold humid areas. In this study, we investigated how agricultural development influences temporal changes in soil C pools in upland crop fields using a paired-plot approach. Ten pairs of control forest and agricultural development plots (2 to more than 80 years) were selected with the same crop rotation under humid temperate climate in Northeast Japan. We detected a net gain in soil C during the first 2 years of agricultural land development under the flat field condition. This gain in soil C was caused by an increase in the light fraction soil C, which represents plant residue derived-C due to agricultural development. Agricultural development resulted in the loss of soil C in fields without manure application. There was no difference in the ecosystem C stock among soil types or with the amount of manure applied. Agricultural development resulted in a slow decrease in soil C storage, indicating a slow rate of C decomposition under cool climate conditions.

Soil health has become increasingly important in recent years. The Hokkaido government initiated its original administrative strategy referred to as “Clean Agriculture” in 1991, before the concept of soil health and soil quality evolved in the 1990s. Also, Clean Agriculture has been integrated with remote sensing techniques for spatial application in arable fields. In this review paper, we summarized the scientific progress in Clean Agriculture and the management of soil health using remote sensing. One of the main pillars of Clean Agriculture is the minimal usage of chemical fertilizers and agrochemicals to increase soil fertility through the proper application of organic matter. The other two pillars are the sustainment and enhancement of the natural recycling function in agriculture and the enhancement of a stable production safe and high-quality agricultural products taking into account environmental harmony. These agronomic practices can increase soil fertility, maintain water quality, mitigate climate change, and maintain human health, and are similar to those in North America and the EU. Moreover, soil nitrogen fertility evaluated by autoclaved nitrogen (AC-N) can be estimated in large-scale fields and areas via remote sensing, which can facilitate variable nitrogen fertilization using variable-rate planters or broadcasters. Furthermore, systems comprising the growth sensor and variable-rate broadcaster can determine the additional nitrogen fertilization rates for winter wheat on the fields, which enhances soil health over relatively large areas. Further research is needed to expand the spatial utility of various Clean Agriculture techniques using multiperiod satellite images.

Toward achieving uniform crop growth within a potato field, surface humus content, growth, yield and starch value were investigated in a test field whose preceding crops were carrot and soybean. Maps of surface humus content and potato growth were drawn using images obtained from an unmanned helicopter. The results are summarized as follows: (1) Field survey revealed that stem length (cm) × SPAD value in mid-July was an effective index of nitrogen oversupply and potato yield. Values exceeding 3500 indicated nitrogen oversupply. Values less than 2500 indicated that the potato yield was less than the yield set as a goal for the Tokachi district. (2) Estimation of surface humus content was done for 93% of the survey area and of stem length × SPAD in mid-July for 68% of the survey area, by using the images obtained from an unmanned helicopter. It was possible to make maps of surface humus content and stem length × SPAD. This in turn allowed the area of nitrogen oversupply and low yield to be extracted precisely from the map of stem length × SPAD. It became clear that the danger of nitrogen oversupply was high in the high-humus area and the danger of low yield was high in the low-humus area. (3) Regardless of the preceding crops, the map of surface humus content could explain 58% of stem length × SPAD in the survey area. Toward achieving uniform crop growth, precise soil diagnosis based on the surface humus content map is necessary as the first step.

This study was carried out for the following two purposes through analysis of satellite data and meteorological data. One is to assess the feasibility of developing an efficient cultivation support system for effective reduction of costs relating to sugar beet cultivation. The other is to predict the root yield before the harvesting season. The cumulative temperature, cumulative precipitation and cumulative solar radiation from the end of April to the middle of July were selected as the predictors of the root yield prediction formula. The predictive error was 3.8t/ha, that was a result calculated by the farmers group, after weighted to the predicted root yield using NDVI. The results suggest that it is possible to predict the root yield before three months of the harvesting season. For providing such results to sugar companies in much earlier before the harvesting, the efficiency of sugar beet collection and sugar production will increase substantially.