author = "Almeida, Catherine Torres de and Delgado, Rafael Coll and 
                         Galv{\~a}o, L{\^e}nio Soares and Arag{\~a}o, Luiz Eduardo 
                         Oliveira e Cruz de and Ramos, Mar{\'{\i}}a Concepci{\'o}n",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal Rural do Rio de Janeiro (UFRRJ)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {University of 
                title = "Improvements of the MODIS Gross Primary Productivity model based 
                         on a comprehensive uncertainty assessment over the Brazilian 
              journal = "Isprs Journal of Photogrammetry And Remote Sensing",
                 year = "2018",
               volume = "145",
                pages = "268--283",
             keywords = "GPP, MODIS, Amazon.",
             abstract = "Tropical forests and savannas are responsible for the largest 
                         proportion of global Gross Primary Productivity (GPP), a major 
                         component of the global carbon cycle. However, there are still 
                         deficiencies in the spatial and temporal information of tropical 
                         photosynthesis and its relations with environmental controls. The 
                         MOD17 product, based on the Light Use Efficiency (LUE) concept, 
                         has been updated to provide GPP estimates around the globe. In 
                         this research, the MOD17 GPP collections 5.0, 5.5 and 6.0 and 
                         their sources of uncertainties were assessed by using measurements 
                         of meteorology and eddy covariance GPP from eight flux towers in 
                         Brazilian tropical ecosystems, from 2000 to 2006. Results showed 
                         that the MOD17 collections tend to overestimate GPP at low 
                         productivity sites (bias between 111% and 584%) and underestimate 
                         it at high productivity sites (bias between \−2% and 
                         \−18%). Overall, the MOD17 product was not able to capture 
                         the GPP seasonality, especially in the equatorial sites. 
                         Recalculations of MOD17 GPP using site-specific meteorological 
                         data, corrected land use/ land cover (LULC) classification, and 
                         tower-based LUE parameter showed improvements for some sites. 
                         However, the improvements were not sufficient to estimate the GPP 
                         seasonality in the equatorial forest sites. The use of a new soil 
                         moisture constraint on the LUE, based on the Evaporative Fraction, 
                         just showed improvements in water-limited sites. Modifications in 
                         the algorithm to account for separate LUE for cloudy and clear sky 
                         days presented noticeably improved GPP estimates in the tropical 
                         ecosystems investigated, both in magnitude and in seasonality. The 
                         results suggest that the high cloudiness makes the diffuse 
                         radiation an important factor to be considered in the LUE control, 
                         especially over dense forests. Thus, the MOD17 GPP algorithm needs 
                         more updates to accurately estimate productivity in tropical 
                  doi = "10.1016/j.isprsjprs.2018.07.016",
                  url = "http://dx.doi.org/10.1016/j.isprsjprs.2018.07.016",
                 issn = "0924-2716 and 1872-8235",
                label = "lattes: 5507769922001047 3 AlmeidaDelGalAraRam:2018:ImMOGr",
             language = "en",
           targetfile = "almeida_improvements.pdf",
        urlaccessdate = "25 nov. 2020"