Fusion of MODIS and Landsat-Like Images for Daily High Spatial Resolution NDVI

Impact of rainfed agriculture on spatio-temporal patterns of water balance and the interaction between groundwater and surface water in sub-humid plains

The expansion of rainfed agriculture, especially soybean cultivation in sub-humid plains, alters water balance and the exchange between groundwater-surface water (GW-SW). However, to date, there are no studies that analyze how these anthropic disturbances affect hydrological connectivity in these systems, especially the GW-SW interactions. The objective of this study is to analyze how the increase in rainfed agriculture affects the spatio-temporal patterns of the water balance and the GW-SW interaction. For this analysis, a coupled GW-SW flow model was implemented under land use and land cover (LULC) scenarios, to quantify the spatio-temporal dynamics for different components of water balance and GW-SW interactions for the upper creek basin of Del Azul. A simulation was carried out for a period of 13 years (2003-2015) on a daily scale and it was contrasted through three multitemporal LULC maps. The results point that substitution of natural pastures, the reduction of winter crops and the decrease of crop rotation, due to the increase of soybean monoculture in the basin under study, modifies the water balance, especially the annual rates of surface runoff and soil moisture which may increase between 3.5 and 9.4 % and between 1.4 and 4.4 % respectively, thus increasing the annual streamflows between 2.6 and 6.8 % and the groundwater heads between 0.2 and 0.6 m. This leads to changes in the hetereogeneity of the GW-SW interaction, a reduction between 0.3 and 3 % is observed in the discharge from the Pampeano aquifer to the Del Azul stream, while the recharge rates from the Del Azul stream to the Pampeano aquifer increase between 2 and 17.8 %. The application of the SWAT-MODFLOW model under LULC scenarios, improves the prediction of the regional hydrologic connectivity on sub-humid plains, because the hydrological processes occurring in the surface and non-saturated zone are governed by shallow groundwater dynamics.

Change Analysis on the Spatio-Temporal Patterns of Main Crop Planting in the Middle Yangtze Plain

As a traditional agricultural production base in China, the Middle Yangtze Plain (MYP) is a typical region to explore the intensification, large-scale, and agglomeration of agricultural land, and its crop planting situation is sensitive to changes in national agricultural policy and economic development. So far, the research of crop remote sensing extraction mainly has focused on the areas with simple crops rotation patterns, by using short-time sequence remote sensing data with low spatial resolution. The objective of this study was to address how to accurately map the spatial distribution of main crops considering their spectral and phenological features, and what characteristics of spatio-temporal patterns dynamics of crops occurred in the MYP in 1990–2020. Based on Landsat and MODIS data, using the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) as well as the raster-based spectral and phenological differential change method (RSPDCM), this study mapped the spatial distribution of main crops (rice, cotton, maize, soybean, rapeseed and winter wheat) in the MYP during 1990–2020 and analyzed their planting characteristics. The RSPDCM has a good overall accuracy of more than 89%. The planting characteristics of the main crops were highly intensive and agglomerate double-cropping rotation in the MYP’s paddy field. Rice and rapeseed were the two most important crops, accounting for 74.75% of the annual planting area. The highly intensive and large-scale areas were mainly distributed in the Dongting Lake Plain (DTLP) and Poyang Lake Plain (PYLP), while the highly agglomerate areas of main crops were mainly distributed in the Jianghan Plain (JHP). This study innovatively provides a high-precision multi-cropping spatial dynamic mapping method and basic information, which is helpful to realize high-precision remote sensing extraction of crops in different regions of the world and provide basic data for optimizing the allocation of agricultural production resources in top grain-producing areas.