Perennial waterlogged soil (PWS) is induced by the high level of groundwater, and has a persistent impact on natural ecosystems and agricultural production. Traditionally, distribution information regarding PWS is mainly collected from in situ measurements through groundwater level surveys and physicochemical property analyses. However, in situ measurements of PWS are costly and time-consuming, only rough estimates of PWS areas are available in some regions. In this paper, we developed a method to monitor the perennial waterlogged cropland using time-series moderate resolution imaging spectroradiometer (MODIS) data. The Jianghan Plain, a floodplain located in the middle reaches of the Yangtze River, was selected as the study area. Temporal variations of the enhanced vegetation index (EVI), night land surface temperature (LST), diurnal LST differences (ΔLST), albedo, and the apparent thermal inertia (ATI) were used to analyze the ecological and thermodynamic characteristics of the waterlogged croplands. To obtain pure remote sensing signatures of the waterlogged cropland from mixed pixels, the croplands were classified into different types according to soil and land cover types in this paper, and a linear mixing model was developed by fitting the signatures using the multiple linear regression approach. Afterwards, another linear spectral mixing model was used to get the proportions of waterlogged croplands in each 1 km×1 km pixel. The result showed an acceptable accuracy with a root-mean-square error of 0.093. As a tentative method, the procedure described in this paper works efficiently as a method to monitor the spatial patterns of perennial sub-surface waterlogged croplands at a wide scale.