Monthly time series, from 2001 to 2016, of the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) from MOD13Q1 products were analyzed with Seasonal Trend Analysis (STA), assessing seasonal and long-term changes in the mangrove canopy of the Teacapan-Agua Brava lagoon system, the largest mangrove ecosystem in the Mexican Pacific coast. Profiles from both vegetation indices described similar phenological trends, but the EVI was more sensitive in detecting intra-annual changes. We identified a seasonal cycle dominated by Laguncularia racemosa and Rhizophora mangle mixed patches, with the more closed canopy occurring in the early autumn, and the maximum opening in the dry season. Mangrove patches dominated by Avicennia germinans displayed seasonal peaks in the winter. Curves fitted for the seasonal vegetation indices were better correlated with accumulated precipitation and solar radiation among the assessed climate variables (Pearson’s correlation coefficients, estimated for most of the variables, were r ≥ 0.58 p < 0.0001), driving seasonality for tidal basins with mangroves dominated by L. racemosa and R. mangle. For tidal basins dominated by A. germinans, the maximum and minimum temperatures and monthly precipitation fit better seasonally with the vegetation indices (r ≥ 0.58, p < 0.0001). Significant mangrove canopy reductions were identified in all the analyzed tidal basins (z values for the Mann-Kendall test ≤ −1.96), but positive change trends were recorded in four of the basins, while most of the mangrove canopy (approximately 87%) displayed only seasonal canopy changes or canopy recovery (z > −1.96). The most resilient mangrove forests were distributed in tidal basins dominated by L. racemosa and R. mangle (Mann-Kendal Tau t ≥ 0.4, p ≤ 0.03), while basins dominated by A. germinans showed the most evidence of disturbance.

Parameterisation of unsaturated flow models for estimating spatial-scale groundwater recharge is usually reliant on expert knowledge or best-estimated parameters rather than robust uncertainty analysis. This study chose the Campaspe catchment in southeastern Australia as a field example and examined the uncertainty of spatial groundwater recharge by performing uncertainty analysis. The study area was first divided into 13 zones according to different vegetation types, soil groups and precipitation. Individual models were then established for these zones using the biophysically based modelling code WAVES (Water Atmosphere Vegetation Energy and Solutes), which is capable of simulating unsaturated flow. The Monte Carlo method, together with the Latin-Hypercube sampling technique, was employed to perform uncertainty analysis by comparing modelled monthly evapotranspiration (ET) to MODIS ET. The results show that the common one-estimate-per-site approach can still identify the spatial pattern of groundwater recharge in the study area due to the presence of a precipitation pattern. In comparison, the uncertainty analysis not only identifies the spatial pattern, but also provides confidence levels in groundwater recharge that are critical for water resources management. The results also show that recharge absolute uncertainty is directly proportional to the amount of water input, but relative uncertainty in recharge is not. This study indicates that spatial recharge estimation without model calibration or knowledge of model uncertainty could be highly uncertain. MODIS ET can be used to reduce recharge uncertainty, but it is unlikely to lower the recharge uncertainty by a large extent because of the MODIS ET estimation error.