Observing how carbon, energy, and water transports fluxes change with the introduction of intensive agriculture in the Amazon – Phenology Approach. Ricardo K. Sakai (1), D. R. Fitzjarrald (1), O. L. L. Moraes (2), O. C. Acevedo (2), M. J. Czikowsky (1), R. da Silva (3), T. Beldini (3) (1) State University of New York at Albany (2) Universidade Federal de Santa Maria (3) LBA office STM Introduction: T he interest of the impact of deforestation on climate in Amazon has been shown in several models and projects. Most of those studies indicated the transformation from a forest to a pasture field. Since September 2000, agricultural practices in this region have changed rapidly from cattle grazing, to upland (non-irrigated) rice cultivation, and then to soybean cultivation. The pattern we witnessed in the sampled field is characteristic of the entire region along the BR-163 highway that runs south from the city of Santarém, Pará. In the Large scale Biosphere-Atmosphere experiment in Amazonia (LBA) there is a continuing effort to understand the effects of this type of deforestation with a continuous data set. This study presents the results of turbulent flux measurements of carbon, heat, and moisture using the eddy correlation system over an agricultural site in the Eastern Amazon region. During the last 5 years this field has been transformed from a pasture to a rice and soybean plantation. The emphasis in this poster is to show the detection of the vegetation phenology and its relationship with carbon exchange and remote sensing data. Instrumentation: A 20 m tower was installed in an agricultural field to monitor standard micrometeorological variables and carbon dioxide concentration. The site is open, with the closest trees in the direction of the predominant easterly wind lying a kilometer from the tower. An eddy covariance system (EC), comprised of a 3D sonic anemometer (Applied Technologies, Inc, SATI/3K), and an infrared gas analyzer (IRGA, Licor 6262) to measure CO 2, and H 2 O concentrations, was installed at 8.75 m. Inlets and anemometers in the EC system faced east to accommodate predominant easterlies. Near tower top (17.8 m), incident and reflected global solar radiation (Kipp and Zonen, CM11/14) and ‘photosynthetically active radiation’ (PAR, nm), downwelling and upwelling global long- wave (CG2) radiative fluxes were collected at 0.2 Hz. Data collection started on September 2000 and continues to the present Land use history at km77 site : The original forest was cleared (burning) 1990 : burning (?) 2000: Sept 15: Started data collection 2001: January to November - pasture grass, Brachiaria brizantha, 1 animal(buffalo or cow)/ha November 14 till 20 - burning & plowing 2002: February 24 till June 13,14 - rice July 1 till July 5- burning 2003: January 2 till May 5 - rice May 15 till September 1st - soybean 2004: January 14 to Apr 3 - rice May 1 till Aug 31 - soybean 2005: May 1 till Agust 23 - soybean Phenology and Remote sensing Summary: Greenness of the vegetation, and differences in land cover type are clearly detected from changes in the PAR- albedo. Seasonal changes in these parameters are follow those of the daytime evaporation (not shown here) and carbon uptake. These changes in the albedo do not only indicate changes the net radiation regime, but they also mirror changes in energy partition and CO 2 fluxes. Data from modis indicate that the EVI can detect seasonal changes, but it can not clearly distinguish the crop type as well as the in-situ measurements. Therefore, to estimate NEE based on remote sensing platforms, we will need better techniques for the detection of the field state. In situ radiometric parameter (PAR-albedo) also shows a problem, there is not an unique relationship for each crop (rice or soil). For each year NEE and PAR-albedo presents a different relationship. Continuing studies. Planned studies will concentrate on the linking remote sensing measurements that are not so sensitive to the presence of clouds (e.g., microwave band) with in situ measurements such as the radiometric and turbulent fluxes. Clear differences result in the turbulent fluxes (CO 2, H 2 O, and heat,), radiative parameters (albedo and PAR-albedo) due to the landscape changing from pasture to crop field. Acknowledgements: This work was entirely supported by NASA as a part of the LBA-ECO program, grant NCC We are grateful for the logistics help from the staff at the LBA-ECO office at Santarém, especially for the efforts of Ms. Bethany Reed, and Mr. Paulo Coutinho. We also would like to express our gratitude to Mr. João F. de Farias, the owner of the pasture, and subsequent owner Mr. Gregorio R. Maschierro, the owner of Fazenda Paraíso, for information and for the permission to site the micrometeorological tower and perform the biometric study. MODIS data quality Cumulative plot of the Net denote Ecosystem Exchange (NEE) from the EC system. A negative indicates an uptake of carbon from the atmosphere. The circles at on the right side represents the biometric values sampled just before the harvest (check color table in the graph), error bars the standard deviation. Notice that there is a strong correlation between the canopy phenology and the CO 2 flux NEE values change according to the agricultural practice. Also, EC accumulation curves indicate that they underestimate the carbon uptake, principally for the rice crop. Data from moderate resolution imaging spectrometer (modis) data set correspond to a 7x7 1km pixel resolution whose center is close to the tower location (represented by the red triangle). The numbers correspond to the percentage of good data for each pixel through the period of 2000 till Two parameters were analyzed in this study, the normalized difference vegetation index (ndvi), and the enhanced vegetation index. Both indexes measure the “greenness” of the canopy. The EVI has the advantage that it does not become saturated as easily as the NDVI. Histogram of the “usefulness” for each pixel of the modis domain. The highest quality correspond to 0. The minimum value observed for the km77 site is 2, the threshold value taken (blue vertical line in figure). Since the NDVI and EVI are based on the visible light frequency range, pixels that has been contaminated by clouds, or shadow of clouds are also discarded. Two satellites carry modis, terra and aqua. All data present here is from terra’s modis data set. Times series for EVI (top), the ratio of upward and downward PAR (PAR-albedo), and soil moisture content (bottom) for 2000 till The red line correspond to the pasture period, the blue for the rice crops, and yellow represents the soybean crops. Notice that the PAR-albedo can detect the crop phenology and also the wet and dry season, that can be determined by the soil moisture content. EVI can detect the seasonality, but it does seem able to detect the crop phenology. Scatter plots of the EVI vs. PAR-albedo, and EVI vs. soil moisture content (bottom). “F” stands for fallow, “R” rice, “S” soy, and “P” is pasture. There is not a clear correlation between EVI and the type of crop, but it seems that there is a positive correlation between the soil moisture and EVI, indicating that the index can detect the seasonality of the ground water. Scatter plot of the NEE vs. PAR-albedo. “r” correspond to rice, “s” to soy. The number correspond to the year (e.g., r2 means rice 2002). Even though there is a correlation between these two parameters, it does not a unique relationship among each crop. For instance, the “behavior” of the cloud points for the rice 2004 (r4) is quite different from rice 2002 (r2). The same difficulty applies for soybeans. Scatter plot of the NEE vs. EVI. Lines links each 16 days bins. So the hysterisis of the curves for each plantation just reflects the progression of the crop. At the beginning the NEE value is positive, and the EVI is lower because of the bare soil. As the crop grows the NEE becomes more negative and the EVI increases.