## 2 Materials and Methods
### Methods and Analysis  
This study used SMAP L4C daily products from 1 April 2015 to 31 December 2018. The daily GPP and NEE estimates were converted into monthly. Monthly (for selected six months) spatial maps of GPP and NEE were developed for each land-use and associated climate zone during the study period. The monthly values of the GPP and NEE data were used to develop time series plots at ten sites to understand the temporal variations of CO\({}_{2}\) flux at each location associated with particular land-use. These graphs were used to quantify the temporal and seasonal variations of CO\({}_{2}\) flux as extracted from SMAP Level 4 carbon products. These time series plots were also used to compare the GPP and NEE for the different land uses during the study period. The SMAP NEE and GPP data were evaluated at Prairie View A&M University (PVAMU) site at satellite footprint (Resolution = 9 km), which was an open-developed area. An eddy covariance (EC) flux tower was installed (April 2016) at PVAMU to monitor carbon fluxes and climatic and hydrologic variables. SMAP NEE and GPP products were evaluated using in-situ NEE and GPP data. The half-hourly CO\({}_{2}\) flux data were used to estimate daily NEE and GPP. Assuming loss of disturbance negligible, NEE data were partitioned into GPP and Re using GPP = Re - NEE, where daytime NEE is the difference between GPP and Re and night-time NEE is equal to ER. Therefore, GPP is negligible during the night-time. It should be noted that no other EC flux tower's measurements were available in the state during the study period. Therefore, no other locations were used to validate SMAP GPP and NEE measurements. A sequence of time series plots was developed to identify the sensitive months when GPP and NEE values were changing or abruptly changed during the study period. Based on the changes in seasonal distributions of GPP and NEE, six different months (Apr, May, Jun, Aug, Sep, and Dec) of 2015 and 2018 were used to develop spatial distribution maps of GPP and NEE (Figure 3). In addition, changes in spatial coverage between 2015 and 2018 for specified GPP/NEE range were calculated. First, the spatial area for specified NEE and GPP ranges were calculated for the selected months of 2015 and 2018 (Table 2). Then, the change in spatial coverage of specified NEE and GPP ranges between the selected months of 2015 and 2018 were calculated by subtracting the spatial area of specified GPP and NEE ranges of 2015 from 2018 for each month. ArcGIS (Spatial Analyst-Zonal Statistics tool) was used to summarize average GPP and NEE for all of the selected land-use categories in each climate zone [68]. The daily GPP and NEE values (g C m\({}^{-2}\) d\({}^{-1}\)) were used to estimate annual GPP and NEE values (g C m\({}^{-2}\) year\({}^{-1}\)) in each climate zone (Table 3). First, annual GPP and NEE were estimated at each pixel in every climate zone. Then, average GPP and NEE were computed in each climate zone. Additionally, GPP and NEE values were used to calculate the standard deviation (SD) of annual carbon fluxes each year in each climate zone. Since SMAP L4C data were available only for nine months in 2015, this year was excluded from the analysis. The monthly GPP and NEE (2015-2018) data from two climate zones with the same land-use were used to calculate the coefficient of determination (R\({}^{2}\)) and \(p\)-values each year. Box and whisker plots were developed using monthly GPP and NEE values at select locations of each climate zone dominated by one of five major land uses across Texas. Figure 3: Daily gross primary productivity (GPP) and NEE at Upper Coast (UC) and High Plains (HP) for cropland during 2015 and 2016 to select the critical month of each year to develop spatial distribution maps. A sequence of time series plots was developed to identify the sensitive months when GPP and NEE values were changing or abruptly changed during the study period.