Moderate resolution imaging spectroradiometer (MODIS) time series (TS) have been widely applied for flood monitoring in large tropical wetlands. However, little systematic work is available on the influence of pixel q...Moderate resolution imaging spectroradiometer (MODIS) time series (TS) have been widely applied for flood monitoring in large tropical wetlands. However, little systematic work is available on the influence of pixel quality, vegetation cover, and the annual hydroclimatic cycle on classification performance. In this study, this issue is examined based on a six-year, 250 m resolution MOD13Q1 TS underpinned by extensive in situ measurements. The most parsimonious logistic regression model was obtained for land surface water index (LSWI) and enhanced vegetation index (EVI). The inclusion of the 500 m MCD12Q1 land cover Type 2 product improves accuracy. Performance markedly decreases for subsets that include pixels with a VI quality assurance (QA) level poorer than 0110 and/or a pixel reliability (PR) of three. When a Savitzky-Golay filter was used for TS reconstitution, performance is slightly lower than those obtained in a classification of a VI QA 0001 or PR = 0 level strata;moreover, these have the advantage of gap-free flood monitoring. The overall accuracy (OA) of the PR = 0 subset is better for grasslands, and slightly lower for Savannah, and for woodland and forests. The average OA is highest for the dry season, intermediate for the rainy/flooded season, and lowest for the transitional seasons, when the wetland becomes flooded or dries. Comparisons of internal, k-fold, and external validations indicate that only external validation enables a realistic assessment of flood-mapping performance. The complete substitution of PR = 3 pixels by filled-in values is recommended for operational flood monitoring, and it is concluded that the use of the simplified PR metrics as filtering criteria for gap filling and smoothing is sufficient for flood monitoring in the Pantanal. Classification metrics vary more strongly as a function of the hydrological period than by vegetation cover. MOD13Q1 users should be aware that OA in forest stands during the transition seasons are, on average, 25 p.p. lower than the average OAs obta展开更多
文摘Moderate resolution imaging spectroradiometer (MODIS) time series (TS) have been widely applied for flood monitoring in large tropical wetlands. However, little systematic work is available on the influence of pixel quality, vegetation cover, and the annual hydroclimatic cycle on classification performance. In this study, this issue is examined based on a six-year, 250 m resolution MOD13Q1 TS underpinned by extensive in situ measurements. The most parsimonious logistic regression model was obtained for land surface water index (LSWI) and enhanced vegetation index (EVI). The inclusion of the 500 m MCD12Q1 land cover Type 2 product improves accuracy. Performance markedly decreases for subsets that include pixels with a VI quality assurance (QA) level poorer than 0110 and/or a pixel reliability (PR) of three. When a Savitzky-Golay filter was used for TS reconstitution, performance is slightly lower than those obtained in a classification of a VI QA 0001 or PR = 0 level strata;moreover, these have the advantage of gap-free flood monitoring. The overall accuracy (OA) of the PR = 0 subset is better for grasslands, and slightly lower for Savannah, and for woodland and forests. The average OA is highest for the dry season, intermediate for the rainy/flooded season, and lowest for the transitional seasons, when the wetland becomes flooded or dries. Comparisons of internal, k-fold, and external validations indicate that only external validation enables a realistic assessment of flood-mapping performance. The complete substitution of PR = 3 pixels by filled-in values is recommended for operational flood monitoring, and it is concluded that the use of the simplified PR metrics as filtering criteria for gap filling and smoothing is sufficient for flood monitoring in the Pantanal. Classification metrics vary more strongly as a function of the hydrological period than by vegetation cover. MOD13Q1 users should be aware that OA in forest stands during the transition seasons are, on average, 25 p.p. lower than the average OAs obta
