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Accurate and up-to-date mapping and monitoring of rubber plantations is challenging. In this study, we presented a simple method for rapidly and accurately mapping rubber plantations in the Xishuangbanna region of southwest China using phenology-based vegetation index differencing. Temporal profiles of the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Atmospherically Resistant Vegetation Index (ARVI), Normalized Difference Moisture Index (NDMI), and Tasselled Cap Greenness (TCG) for rubber trees, natural forests and croplands were constructed using 11 Landsat 8 OLI images acquired within one year. These vegetation index time series accurately demonstrated the unique seasonal phenological dynamics of rubber trees. Two distinct phenological phases (i.e., defoliation and foliation) of rubber trees were clearly distinguishable from natural forests and croplands. Rubber trees undergo a brief defoliation-foliation process between late December and mid-March. Therefore, vegetation index differencing between the nearly complete defoliation (leaf-off) and full foliation (leaf flushing) phases was used to delineate rubber plantations within fragmented tropical mountainous landscapes. The method presented herein greatly improved rubber plantation classification accuracy. Overall classification accuracies derived from the differences of the five vegetation indices varied from 92% to 96% with corresponding kappa coefficients of 0.84-0.92. These results demonstrate the promising potential of phenology-based vegetation index differencing for mapping and monitoring rubber expansion at the regional scale.

Runoff modeling is one of the key challenges in the field of hydrology. Various approaches exist, ranging from physically based over conceptual to fully data driven models. In this paper, we propose a data driven approach using the state-of-the-art Long-Short-Term-Memory (LSTM) network. The proposed model was applied in the Poyang Lake Basin (PYLB) and its performance was compared with an Artificial Neural Network (ANN) and the Soil & Water Assessment Tool (SWAT). We first tested the impacts of the number of previous time step (window size) in simulation accuracy. Results showed that a window in improper large size will dramatically deteriorate the model performance. In terms of PYLB, a window size of 15 days might be appropriate for both accuracy and computational efficiency. We then trained the model with 2 different input datasets, namely, dataset with precipitation only and dataset with all available meteorological variables. Results demonstrate that although LSTM with precipitation data as the only input can achieve desirable results (where the NSE ranged from 0.60 to 0.92 for the test period), the performance can be improved simply by feeding the model with more meteorological variables (where NSE ranged from 0.74 to 0.94 for the test period). Moreover, the comparison results with the ANN and the SWAT showed that the ANN can get comparable performance with the SWAT in most cases whereas the performance of LSTM is much better. The results of this study underline the potential of the LSTM for runoff modeling especially for areas where detailed topographical data are not available.