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No-till is often referred to as a climate change mitigation option, possibly with a stronger conviction, than as a practice to manage soil organic C (SOC) content. We conducted a global meta-analysis to evaluate the effect of no-till (NT) on SOC concentration (SOCc, g C kg−1 soil) and stock (SOCs, Mg C ha−1 land) across climate, soil texture, cropping systems, and no-till duration to appraise the priority-setting. Compared to conventional tillage, NT favoured a significant rise (ΔSOCc) of 38% in the 0–5 cm soil layer and a much lesser 6% increase in the 5–10 cm layer and no change beyond 10 cm. The temperate climate had nearly twice ΔSOCc in the 0–5 cm layer compared to other climates, while the tropical climate favoured sub-surface accumulation. Coarse- and medium-textured soils and the inclusion of legumes in crop rotation facilitated larger positive ΔSOCs under NT. The microbial biomass C was the most abundant C pool, with 61% and 23% increases under NT in 0–5 and 5–10 cm layers. A large ΔSOCc in aggregates also characterized the top 0–5 cm layer. The difference in SOCs was realized to a maximum 30 cm depth (5.4 Mg ha−1 or 14%) in favour of NT, although varying with the duration of its adoption. The contribution of NT in mitigating global anthropogenic greenhouse gas emissions is meagre, although it can substantially offset emissions from agriculture (17–58%). The benefit of NT in improving SOC is primarily restricted to the surface layer, which is potentially exposed, and therefore an increase could be short-lived. Nevertheless, a short-term gain in SOC is likely to enhance soil quality and crop productivity. Thus, NT may be promoted as a sustainable agricultural management practice rather than emphasizing its role as a potential climate change mitigation option.

In modern days, a sustainable urban planning system requires a balance of vegetation, water, and settlement. The proportions of these surface features directly influence the land surface temperature (LST) in an urban area. LST primarily depends on the emittance of land use/land cover (LULC) categories. In an urban area, changes in LULC categories as well as local warming are the prime regulators of LST change. The study analyses the LULC change and its impact on LST in Imphal City, India. Landsat satellite data for the summer season and winter seasons for 1991, 2001, 2011, and 2021 have been used in this study. Results show that the mean LST of the study area increased at >1% rate/decade. The green area and water area decreased the LST values whereas the built-up area and fallow lands increased the LST values. The study indicates the consequences of proper land conversion to regulate the LST change. Moreover, the influence of population on LST is also determined. The continuous rising trend of population is a positive factor of increasing LST. The study may help the town and country planners to generate sustainable urban land.

The study focused on investigating the seasonal and spatiotemporal relationship between the relationships of LST with four spectral indices (MNDWI, NDBaI, NDBI, and NDVI) in and around Manipur City of India using eight cloud-free Landsat data from the summer and winter seasons for 1991, 2001, 2011, and 2021. These spectral indices respond differently to the change of LST in an urban landscape. Pearson’s linear correlation coefficient was the basis of the correlation analysis. The study finds that LST builds a moderate negative relationship with NDVI (R = -0.42) and MNDWI (R = -0.42), a moderate positive relationship with NDBaI (R=0.48), and NDBI (R = 0.61). The relationship is more stable in the winter season (CV = 7.31, 7.04, 10.45, and 28.71 for MNDWI, NDBaI, NDBI, and NDVI, respectively) than in summer (CV = 44.46, 36.09, 23.67, and 29.71 for MNDWI, NDBaI, NDBI, and NDVI, respectively). The strength of the relationship is gradually increasing in the winter season while there is no such effect noticed on the trend in the summer season. The LST-NDBI relationship is the most consistent (CV = 18.19), while the LST-NDVI relationship is the most variable (CV = 30.37).

The present study compares the seasonal relationship of land surface temperature (LST) with six vegetation indices (DVI, EVI, FV, NDVI, RVI, and SAVI) and three other land surface indices (MNDWI, NDBI, and NMDI) in Imphal City, Northeast India, using the summer and winter Landsat 8 satellite image of 2021. These land surface indices respond differently to changes in LST in an urban landscape. The relationships between LST and these indices were presented spatially using Pearson correlation coefficient method, scatter plot graphs, box plot graphs, and cross-sectional drawings. The study found that LST had a moderate negative relationship (less than -0.45) with all six vegetation indices in summer and winter. Among the other land surface indices, MNDWI had a weak negative relationship (-0.25) in summer and a moderate negative relationship (-0.45) in winter. NDBI generates a moderate positive relation (0.56) in summer and a strong positive relation (0.69) in winter. NMDI forms a weak positive relation (0.08) in summer and a moderate positive relation (0.35) in winter. From the cross-section analysis, it was observed that LST was low along the high values of vegetation indices and vice versa. Moreover, built-up and bare surfaces generated a high LST along the cross-section. [ABSTRACT FROM AUTHOR]

The present study analyses the seasonal influence of error estimated in downscaled land surface temperatures (LSTs) in a humid subtropical city using Landsat 8 data of summer and winter seasons in 2021. Thermal sharpening (TsHARP) algorithm is one of the most frequently used downscaling techniques which is originally based on normalised difference vegetation index (NDVI). This study assesses the capability of the TsHARP technique with a separate combination of four selected spectral indices (modified normalised difference water index, normalised difference bareness index, normalised difference built-up index [NDBI], and NDVI), and by determining the root mean square error (RMSE) and mean error produced by the sharpened LST. Besides, sharpened LST has also been estimated by combining the four spectral indices. It is observed that NDBI provides the most effective output (RMSE is 1.11 [30 m], 1.05 [120 m], 1.02 [240 m], and 0.99 [480 m] in summer, whereas RMSE is 0.61 [30 m], 0.59 [120 m], 0.57 [240 m], and 0.56 [480 m] in winter). NDBI-based sharpened LST generates the best relationship (R = 0.565 in summer and R = 0.537 in winter) with surface features. Fallow land generates the best relationship (R = 0.512 in summer and R = 0.530 in winter) with sharpened LST. The summer season (R = 0.438) generates a better relationship between surface features and sharpened LST than the winter season (R = 0.409). [ABSTRACT FROM AUTHOR]