Grassland cover declined in Southern Africa but increased in other African subcontinents in early twenty-first century.

The African continent has the most extensive grassland cover in the world, providing valuable ecosystem services. African grasslands, like other continental grasslands, are prone to various anthropogenic disturbances and climate, and require data-driven monitoring for efficient functioning and service delivery. Yet, knowledge of how the African grassland cover has changed in the past years is lacking, especially at the subcontinent level, due to lack of relevant long-term, Africa-wide observations and experiments. In this study, we used Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) data spanning 2001 to 2017 to conduct land use land cover (LULC) change analyses and map grassland distribution in Africa. Specifically, we assessed the changes in grassland cover across and within African subcontinents over three periods (2001-2013, 2013-2017, and 2001-2017). We found that the African grassland cover was 16,777,765.5 km2, 16,999,468.25 km2, and 16,968,304.25 km2 in 2001, 2013, and 2017, respectively. There were net gain (1.32%) and net loss (- 0.19%) during 2001-2013 and 2013-2017 periods, respectively, and the annual rate of change during these periods were 0.11% and - 0.05%, respectively. Generally, the African grassland cover increased by 1.14% (0.07% per annum) over the entire study period (2001-2017) at the expense of forestland, cropland, and built-up areas. The East and West African grassland cover reduced by 0.07% (- 0.02% per annum) and 1.35% (- 0.34% per annum), respectively from 2013 to 2017 but increased in other periods. On the other hand, the grassland cover in North and Central Africa increased throughout the three periods while that of Southern Africa decreased over the three periods. Overall, the net gains in the grassland cover of other African subcontinents offset the loss in Southern Africa and promoted the overall gain across Africa. This study underscores the need for continuous monitoring of African grasslands and the causes of their changes for efficient delivery of ecosystem services.

Deep Learning Convolutional Neural Network for the Retrieval of Land Surface Temperature from AMSR2 Data in China.

A convolutional neural network (CNN) algorithm was developed to retrieve the land surface temperature (LST) from Advanced Microwave Scanning Radiometer 2 (AMSR2) data in China. Reference data were selected using the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product to overcome the problem related to the need for synchronous ground observation data. The AMSR2 brightness temperature (TB) data and MODIS surface temperature data were randomly divided into training and test datasets, and a CNN was constructed to simulate passive microwave radiation transmission to invert the surface temperature. The twelve V/H channel combinations (7.3, 10.65, 18.7, 23.8, 36.5, 89 GHz) resulted in the most stable and accurate CNN retrieval model. Vertical polarizations performed better than horizontal polarizations; however, because CNNs rely heavily on large amounts of data, the combination of vertical and horizontal polarizations performed better than a single polarization. The retrievals in different regions indicated that the CNN accuracy was highest over large bare land areas. A comparison of the retrieval results with ground measurement data from meteorological stations yielded R2 = 0.987, RMSE = 2.69 K, and an average relative error of 2.57 K, which indicated that the accuracy of the CNN LST retrieval algorithm was high and the retrieval results can be applied to long-term LST sequence analysis in China.