## 1 Introduction  
The most important global interactions between the biosphere and atmosphere involve the transfer of energy, water, and carbon. Carbon is assimilated by the biosphere through photosynthesis and released through autotrophic and heterotrophic respiration [1]. Emissions and re-absorption of carbon gases from natural ecosystems were in a state of equilibrium for millions of years; however, this balance has been disturbed by human activities. Consequently, atmospheric concentrations of carbon dioxide (CO\({}_{2}\)) have been increasing and are widely believed to be responsible for global warming [2]. Understanding the drivers of the spatial and temporal patterns of surface-atmosphere CO\({}_{2}\) exchange is therefore crucial to improving predictions of future concentrations of atmospheric CO\({}_{2}\)[3]. To achieve this goal, plant cover and corresponding surface CO\({}_{2}\) uptake must be monitored on a large scale. Such data will facilitate accurate estimates of regional and global carbon budgets and, ultimately, more accurate prediction of carbon source-sink relationships and atmospheric CO\({}_{2}\) concentrations [4]. Remote sensing can be used to estimate surface-atmosphere CO\({}_{2}\) exchange such as gross primary productivity (GPP). GPP is the total carbon assimilated by vegetation [5]. Based on biological processes GPP is the sum of net primary productivity (NPP) with respiration. GPP can be estimated by combining remote sensing with carbon cycle processing [6]. GPP have been estimated based on biophysical parameters derived from vegetation indices (such as the normalized difference vegetation index, NDVI), land-cover data, and light-use-efficiency parameters [7]. Most models of ecosystem carbon exchange that are based on remote sensing use some form of the light use efficiency (LUE) model. The LUE model states that carbon exchange is a function of the amount of light energy absorbed by vegetation and the efficiency with which that light energy is used to fix carbon [8]. [PERSON] [9] developed a method for estimating plant productivity from observations of APAR and estimates of LUE. Remotely-sensed optical signatures have proved useful for estimating a range of ecological variables including leaf area index (LAI) and the absorptivity of photosynthetically active radiation (APAR) [10,11]. The fraction of absorbed photosynthetically active radiation (fAPAR) driven by vegetation cover is related to the NDVI. The strong relationship between NDVI and fAPAR has been examined in detail through theoretical and experimental analyses [10,12,13]. The NDVI has become a popular tool for assessing different aspects of plant processes while simultaneously determining spatial variation in vegetation cover [14]. Previous work in the Kalimantan tropical forest estimated a GPP value of between 2,859 and 3,227 gC m\({}^{-2}\) yr\({}^{-1}\)[15], and research in an Amazonian tropical forest showed a GPP of 3,040 gC m\({}^{-2}\) yr\({}^{-1}\)[1]. The seasonal dynamics of GPP prediction from satellite data were similar to those of GPP from observations. Seasonally-integrated GPP observations over an eight-month period accounted for 98% of the annual GPP prediction [16]. In a tropical evergreen forest in the Brazilian Amazon, prediction of GPP from MODIS satellite data was consistent with GPP estimation from eddy flux tower measurements [17,18], with a GPP prediction from MODIS of about 2,977 gC m\({}^{-2}\) year\({}^{-1}\)[17]. Research in the Labanan Concession Area in the East Kalimantan area of Indonesia showed an annual range of GPP from 1,710 to 2,635 gC m\({}^{-2}\) year\({}^{-1}\) based on MODIS satellite data [19]. Estimates ofGPP from Landsat were linearly related to daytime maize GPP measurements with root mean squared error less than 1.58 gC m\({}^{-2}\) d\({}^{-1}\) over a GPP range of 1.88 to 23.1 gC m\({}^{-2}\) d\({}^{-1}\)[20]. Denpasar represent an urban city in Bali. Remote sensing is a tool for mapping and monitoring urban areas.