Located across the equator, the East Africa region is among regions of Africa which have previously known the severe vegetation degradation. Some known reasons are associated with the climate change events and unprofessional agricultural practices. For this purpose, the Advanced Very High Resolution Radiometer (AVHRR) version 3 NDVI (NDVI3g) and Climate Research Unit (CRU) datasets for precipitation and temperature were used to assess the impact of climate factors on vegetation dynamics over East Africa from 1982 to 2015. Pearson correlation of NDVI and climate factors were also explored to investigate the short (October - December) rainy seasons. The phenological metrics of the region was also extracted to understand the seasonal cycle of vegetation. The results show that a positive linear trend of 14.50 × 10⁻⁴ for mean annual NDVI before 1998, where as a negative linear trend of −9.64 × 10⁻⁴ was found after 1998. The Break Point (BP) was obtained in 1998, which suggests to nonlinear responses of NDVI to climate and non-climate drivers. ENSO-vegetation in El-nino years showed a weak teleconnection between ENSO and vegetation growth changes of croplands. Also, the analyzed correlations on NDVI data resulted to the higher correlation between NDVI and precipitation than that with temperature. The Hurst exponent result showed that about, 18.63% pixels exhibited a behavior, typical of random walk (H = 0.5) suggesting that NDVI growth changes may eventually persist, overturn or fluctuate randomly in the future depending on the drivers. Vegetation trends with sustainable (unsustainable) trends were 36.8% (44.6%). Strikingly, about 20% of the total vegetated area showed unsustainable trend from degradation to amelioration. More so, results reveal that the vegetation of the croplands (non-croplands) over East Africa changed insignificantly by 6.9 × 10⁻⁵/yr (5.16 × 10⁻⁴/yr), suggesting that non-croplands are fast getting reduced Nonetheless, the NDVI growth responses to monthly and seasonal changes in climate were adjudged to be complex and dynamic. Seasonally, the short rainy season showed the higher variability in NDVI than the long rainy season. Also, the DJF, MAM and SON seasons are strongly driven by precipitation variation effect of ENSO versus NDVI series.

Drought is an extremely dangerous natural hazard that causes water crises, crop yield reduction, and ecosystem fires. Researchers have developed many drought indices based on ground-based climate data and various remote sensing data. Ground-based drought indices are more accurate but limited in coverage; while the remote sensing drought indices cover larger areas but have poor accuracy. Applying data-driven models to fuse multi-source remote sensing data for reproducing composite drought index may help fill this gap and better monitor drought in terms of spatial resolution. Machine learning methods can effectively analyze the hierarchical and non-linear relationships between the independent and dependent variables, resulting in better performance compared with traditional linear regression models. In this study, seven drought impact factors from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor, Global Precipitation Measurement Mission (GPM), and Global Land Data Assimilation System (GLDAS) were used to reproduce the standard precipitation evapotranspiration index (SPEI) for Shandong province, China, from 2002 to 2020. Three machine learning methods, namely bias-corrected random forest (BRF), extreme gradient boosting (XGBoost), and support vector machines (SVM) were applied as regression models. Then, the best model was used to construct the spatial distribution of SPEI. The results show that the BRF outperforms XGBoost and SVM in SPEI estimation. The BRF model can effectively monitor drought conditions in areas without ground observation data. The BRF model provides comprehensive drought information by producing a spatial distribution of SPEI, which provides reliability for the BRF model to be applied in drought monitoring.

Accurate extraction of crop cultivated area and spatial distribution is essential for food security. Crop classification methods based on machine learning and deep learning and remotely sensed time-series data are widely utilized to detect crop planting area. However, few studies assess the effectiveness of machine learning and deep learning algorithm integrated time-series satellite data for identifying multiple crop type classification over a large-scale region. Hence, this study aims to evaluate the effectiveness of machine learning and deep learning models in crop classification and provide a framework for large-scale multiple crop type classification based on time-series of satellite data. The time-series of the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and NaE (combined NDVI and EVI) were adopted as input features, and four widely used machine learning models, including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), and their integration (Stacking) were compared to examine the performance of multiple crop types (i.e., wheat, corn, wheat-corn, early rice, and early rice-late rice) classification in the North China Plain. The performance of two types of deep learning classifiers: the One-dimensional Convolutional Neural Network (Conv1D) and Long Short-Term Memory Networks (LSTM), were also tested. The results showed that the NaE feature performed best among three input features, and the Stacking model produced the highest accuracy (77.12%) compared to other algorithms.

In semi-arid areas of China, the ridge-furrow mulched with plastic film (RF) cultivation system is a common water-saving agricultural technique where the shortage of water resources has become a serious problem. Therefore, we aimed to explore whether this cultivation is actually an improvement over the traditional flat planting (TF) method while testing two deficit irrigation (150, 75 mm) levels to grow winter wheat. Furthermore, we examined the responses of the anti-oxidative defence system and photosynthetic capacity of winter wheat flag leaves under three simulated rainfall (275, 200 and 125 mm) conditions. The results showed that the RF system with 150 mm deficit irrigation and 200 mm simulated rainfall condition (RF2₁₅₀) treatment raised soil water content (%) at the jointing and flowering stages and achieved higher net photosynthesis rates (Pn) in flag leaves. Furthermore, such improvements were due to the reduction of malondialdehyde (MDA) content and oxidative damage during different growth stages of winter wheat. The RF2₁₅₀ treatment significantly increased the activities of superoxide dismutase (SOD); peroxidise (POD), catalase (CAT) and ascorbate peroxidase (APX) and the content of soluble protein (SP) during different growth stages of winter wheat. Furthermore, RF2₁₅₀ treatment attained the highest value at the flowering stage, while also exhibiting significant declines in contents of proline, MDA, H₂O₂ and O₂ in flag leaves. The higher free H₂O₂ and O₂ scavenging capacity and better anti-oxidative enzyme activities under the RF2₁₅₀ treatment were due to the lower level of lipid peroxidation, which effectively protected the photosynthetic machinery. The net photosynthetic rate of flag leaves was positively correlated with SOD, POD, CAT, APX and SP activities, and negatively correlated with proline, MDA, H₂O₂ and O₂ contents. We concluded that the RF2₁₅₀ treatment was the better water-saving management strategy because it significantly delayed flag leaf senescence and caused the increases in SWC, WUE, Pn, antioxidant enzyme activities and grain yield of winter wheat grown in semi-arid regions of China.