Soil CO2 Efflux and Remotely Sensed Surface Temperature

As described in the previous section, CO2 efflux from the soil surface has been expressed as a function of soil temperature, soil moisture, or air temperature (Grant and Rochette, 1994; Maisongrande et al., 1995; Nakadai et al., 1996). Nevertheless, since the activity of soil microorganisms is related to temperature, water, oxygen, and organic matter in the soil (Paul and Clark, 1989), it may be related to remotely sensed data in the optical, thermal, or microwave domains via information on the color, temperature, and water content of the soil, as well as on vegetation coverage. The present experimental study (Inoue et al., 2004) can be the first to report that remote sensing information may be used directly to estimate the SSFCO2.

Since it has been one of the important issues to quantify the dynamics of the SSFCO2, this study first focused on the relationship between remotely sensed information and the ESFCO2 under bare soil conditions (— SSFCO2). The relationship of the SSFCO2 with air temperature, soil temperature, soil water content, and remotely sensed surface temperature were investigated based on their hourly average data (Fig. 6). Soil temperatures for the 5-10-cm

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Figure 5: Typical time course changes of ecosystem surface CO2 flux (ESFCO2 ) over soybean canopy and bare field estimated by eddy covariance method (a). Soil surface CO2 flux (SSFCO2) under bare soil conditions is indicated in the time of day axis (b). DAS, DAP, and LAI are days after seeding, days after ploughing, and leaf area index, respectively (Inoue et al., 2004) (For colour version, see Colour Plate Section).

Figure 5: Typical time course changes of ecosystem surface CO2 flux (ESFCO2 ) over soybean canopy and bare field estimated by eddy covariance method (a). Soil surface CO2 flux (SSFCO2) under bare soil conditions is indicated in the time of day axis (b). DAS, DAP, and LAI are days after seeding, days after ploughing, and leaf area index, respectively (Inoue et al., 2004) (For colour version, see Colour Plate Section).

5 10 15 20 25 30 35 40 0 5 10 15 20 25 30 35 40 45 50 55

(c) Soil temperature (°C) Remotely sensed surface temperature (°C)

Figure 6: Relationship of SSFCO2 under bare soil conditions with (a) air temperature, (b) soil water content of the top 10-cm layer, (c) soil temperature at depth of 5-10 cm, and (d) remotely sensed surface temperature. Number of data for (b) was less than for the others because less availability of soil moisture measurements (Inoue et al., 2004).

5 10 15 20 25 30 35 40 0 5 10 15 20 25 30 35 40 45 50 55

(c) Soil temperature (°C) Remotely sensed surface temperature (°C)

Figure 6: Relationship of SSFCO2 under bare soil conditions with (a) air temperature, (b) soil water content of the top 10-cm layer, (c) soil temperature at depth of 5-10 cm, and (d) remotely sensed surface temperature. Number of data for (b) was less than for the others because less availability of soil moisture measurements (Inoue et al., 2004).

layer obtained by the averaging thermocouple probe, averaged temperatures from the three infrared thermometers, and volumetric soil water contents measured by the TDR probe for the depth of 10 cm were used for the analysis. Correlation coefficients are indicated in Fig. 6 for reference since no specific curve was inferred from the data points. The remotely sensed surface temperature had the highest correlation coefficient (r2 — 0.64; n — 929) with the SSFCO2 flux over the soil surface. The next highest was air temperature (r2 — 0.28), and the other two had poor correlation (r2 — 0.01). The effect of soil moisture on microorganism respiration or SSFCO2 has been reported to be positive or negative, but there was no obvious relationship in the range of our data set likewise in some previous reports (Wagai et al., 1998). The effect of soil moisture may be obscured presumably because of two contrary roles in the activity of soil microorganisms; soil moisture is required as it provides the soluble part of organic matter to microorganisms, but increasing moisture may have a negative effect on oxygen availability for microorganisms in the soil pore space (Paul and Clark, 1989).

It was obvious that the SSFCO2 changes dynamically during a day due to microenvironment such as soil and atmospheric conditions, which may be most clearly detected by remotely sensed surface temperature. On the Contrary, the effect of soil moisture and soil temperature on the SSFCO2 was not significant in our analysis, presumably because the analysis was based on hourly data while they changed much slower than air and soil surface temperature. In general, the time response is fastest in the soil surface temperature and getting slower in air temperature, soil temperature, and soil water content in this order. Therefore, soil temperature and soil water content might be better correlated with the SSFCO2 in long-term comparisons (e.g. Nakadai et al., 1996). The time response of SSFCO2 depends upon the reaction rate of microorganisms to temperature, moisture, and oxygen concentrations in the soil, as well as upon the physical processes of CO2 transfer through the soil pore. The key factors in the physical processes are the differential of CO2 concentration between the atmosphere and soil pore, as well as the differential of air pressure between them. The pressure differential is affected by air temperatures near the soil surface and within the soil, wind speed (especially, gustiness), rainfall, and soil compaction. Under open field conditions, these conditions all affect the dynamic change of SSFCO2, which must be the major reason for the data scatters in figures. Nevertheless, the high correlation between soil surface temperature and SSFCO2 suggests that the CO2 transfer from the soil surface is strongly related to the surface temperature of the soil, presumably due to the changes in microbial activity, air temperature in the soil pore near the surface, and turbulent conditions adjacent to the soil surface. Daily or monthly averages of soil temperature (or air temperature) are sometimes used for rough estimation of SSFCO2 at regional or global scales (e.g. Potter et al., 1993; Maisongrande et al., 1995). However, information on the dynamic change of SSFCO2 at high temporal resolution is useful for more mechanistic understanding of the biophysical processes in the field, as well as for more accurate estimation of ESFCO2 using dynamic simulation models such as SVAT models (Olioso et al., 2001) where the temporal resolution is often shorter than 1 h.

Although the SSFCO2 depends on several environmental and soil-related parameters as discussed previously, it is well known that temperature is the dominant factor determining the activity of microorganisms, and it was obvious that the soil surface temperature is most highly correlated with the SSFCO2. Hence, we applied the following Q10 function for more generalized understanding and prediction:

where SSFCO2 is the CO2 flux over the soil surface, Q10 is the temperature coefficient, TiR is the remotely sensed surface temperature (°C), and a and b are parameters, respectively. The SSFCO2 was better estimated by the Q10 function from the remotely sensed surface temperature (r2 — 0.66, RMSE — 0.098) than by the single correlation (Fig. 7). The Q10 coefficient was estimated to be 1.47, which was between the values for the physical and biochemical reactions. The critical temperature at which the SSFCO2 approached zero was estimated to be 10.0°C, which may be an apparent lowest limit for the microbial activity. Some part of variation should also be caused by experimental errors in both flux and temperature measurements. Nevertheless, a large number of data from different seasons and years show that the equation represents a robust relationship between the remotely sensed surface temperature and the SSFCO2 under bare soil conditions. From a theoretical point of view, the contents of both moisture and organic matter in the soil would affect the SSFCO2, which may be incorporated in the Q10 and the two parameters (a and b). We attempted to include the effect of soil water content in these coefficients, but no significant improvement was obtained in the estimation of SSFCO2.

0 5 10 15 20 25 30 35 40 45 50 Remotely sensed surface temperature (°C)

Figure 7: Relationship between soil surface CO2 flux (SSFco2) under bare soil conditions and remotely sensed surface temperature expressed by a Qi0 function (Inoue et al., 2004).

0 5 10 15 20 25 30 35 40 45 50 Remotely sensed surface temperature (°C)

Figure 7: Relationship between soil surface CO2 flux (SSFco2) under bare soil conditions and remotely sensed surface temperature expressed by a Qi0 function (Inoue et al., 2004).

It is straightforward to estimate the soil surface temperature directly by remote sensing under bare or sparely vegetated conditions, but vegetation coverage may have to be known at the same time for wide area applications. For this purpose, optical remote sensing data may be used; the average value of NDVI for bare soil surfaces was 0.178 with a standard deviation of 0.027, while it exceeded 0.5 even at a low value of LAI such as 0.5. Therefore, it may be possible to clearly detect bare (or sparsely vegetated) areas by optical remote sensing.

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