Burying crop straw into the soil as a capillary barrier represents an effective approach for amelioration of coastal saline soils. However, there are still insufficient studies thoroughly evaluating the effects of buried straw layer on saline soil quality, with a particular paucity of studies on the dynamic stability of rhizosphere microbial networks. The relationship of rhizosphere microbial communities to plant productivity and quality was explored in coastal saline soil amended with straw (0 cm, 5 cm, 10 cm thick). Dynamic disturbances of microbial co-occurrence networks were simulated to assess network stability and resilience. Straw application reduced the cumulative phreatic evaporation, resulting in lower soil salinity in the root zone. This was accompanied by distinct microbial community variations, with increased abundances of beneficial taxa (e.g., actinomycetes, plant growth-promoting microbes) and decreased abundances of potential pathogens. Partial least squares path modeling revealed that straw application enhanced soil nutrient cycling and supply by directly increasing microbial diversity and indirectly heightening network complexity, which culminated in improved plant productivity and quality. Notably, the plant productivity and quality index was elevated 5.5-fold under high straw application. Although high straw application considerably modified soil quality, low straw application boosted fungal network stability. The fungal network showed compromised dynamic stability and poor resilience under high straw application, especially when subjected to external disturbances. This study uncovers the ecological effects of straw application in coastal saline soil, emphasizing the distinct responses of rhizosphere microbial networks to straw layer thickness.

Accurate mapping rice cropping intensity and cropping area is crucial for global food security, water resource managing, as well as economic stability. However, in cloudy and foggy rice-producing areas, current automated mapping of rice cropping intensity is significantly restricted by a lack of sufficient training samples, diverse backscatter patterns of rice, low efficiency in time-series feature extraction, and similar phenological evolution of rice and non-rice crops. To tackle the above issues, this study constructed a depolarization index (DI) time series that can stably track rice phenology, integrated periodicity assessment and cosine component weighting to simplified harmonic analysis method, and introducing DI increment assessment and temperature constraints to eliminate interference from non-rice crops. The rice cropping intensity and cropping area in southern China and the five Southeast Asian countries were mapped at a resolution of 10 m on the Google Earth Engine (GEE) platform. The overall accuracy (OA) was 84.07%, which was highly consistent with the provincial agricultural statistics (R2>0.79), performed significantly improved mapping accuracy and enhanced detail capture, while also revealed significant regional differences in the effectiveness of temperature constraints across climatic zones. The proposed framework enriches the algorithm library of rice cropping intensity mapping that does not rely on training samples, providing valuable methodological insights for large-scale rice monitoring in complex environments. The latest release of 10 m rice cropping intensity map is publicly available at https://doi.org/10.5281/zenodo.15095946.

Saline water irrigation is applied to address water scarcity due to climate change; however, it causes soil salinisation, which is harmful to soil quality and crop growth. Drought hardening can trigger resistance to additional stress events by activating various defence mechanisms. Herein, we examined the effect of drought hardening pre-treatment on soil sodium ion accumulation, sodium ion distribution in various organs, sodium ion excretion by plants and tomato yield change under saline water irrigation through a 2-year experiment. Different water and salinity treatments were designed, including four water levels (W1, W2, W3 and W4, indicating soil moisture contents of 75 %-85 %, 60 %-70 %, 55 %-65 % and 40 %-50 % of the field capacity, respectively) and three salt levels (S2, S4 and S6, indicating sodium chloride (NaCl) addition of 2, 4 and 6 g L- 1, respectively). The control treatment (CK) was treated without drought hardening and irrigated with tap water with no salt. The amount of soil sodium ions increased the most during the developmental stage, and sodium ion content in the whole-pot soil increased during the developmental stage by 420.18 %-1947.99 % in the 2-year experiment, compared with that during the seedling stage. Sodium ions were lowest in the middle-layer roots and highest in lower-layer leaves at the end of maturation stage. Sodium ions were lowest in the middle-layer roots decreased by 3.69 %-41.12 % in 2022, compared with that in upper- and lower-layer roots, and the decreased percentages were 14.06 %-57.89 % in 2023 at the end of maturation stage. Moreover, sodium ions in soil and various tomato organs increased with irrigation salinity, except for the W1S2 treatment in 2022 and the W4S4 treatment in 2023. Compared with sodium ions in soil and sodium ion excretion by plant, the sodium ion content in tomato organs was the lowest, at 1.98 %-12.35 %. The root is an important regulated organ that affects the distribution of sodium ions in both soil and plant organs. Mild drought hardening resulted in the lowest amount of sodium ions in soil under the S2 and S4 treatments and led to the highest sodium ion excretion. Yield increased with decreased irrigation salinity and gained the largest value under the mild drought hardening treatment. Yield under mild drought hardening treatment increased by 19.61 %-151.41 %, compared with other salt treatments. In conclusion, mild drought hardening could alleviate salinisation and improve tomato resistance to salt stress. This paper provides some reference for future research on the mechanism by which drought hardening enhances the salt tolerance in crops, and also offers some new ideas for applying salt water irrigation to solve the problem of water shortage.

Dry topsoil restricts root growth and nutrient uptake in arid regions, thereby significantly reducing crop yield. Hydraulic lift occurs due to the dry topsoil and wet deep soil. This study investigates the effects of topsoil drought intensity (three field capacities in topsoil: 60–70% (W1), 50–60% (W2), and 40–50% (W3)) and nitrogen application rate (N1: 120, N2: 240, and N3: 360 kg ha−1) on cotton quality and the distribution of nitrogen in soil and plant under hydraulic lift using a root-splitting device. The upper pot of the root-splitting device was 22 cm high, with a 26 cm top diameter and a 23 cm bottom diameter; the lower pot of the root-splitting device was 45 cm high, with a 48 cm top diameter and a 36 cm bottom diameter. Topsoil moisture was maintained at W1 without nitrogen application under the control treatment (CK). The W2 and W3 treatments (representing different topsoil drought intensities) were designed to compare the interactive effects of water and nitrogen fertiliser on nitrogen distribution and cotton quality with the CK treatment. Results indicate that the concentrations of nitrate nitrogen (NO3−-N) in the 10–20 cm soil were generally higher than those in the 0–10 cm soil. The topsoil drought intensity and nitrogen application rate had significant impacts on nitrogen concentrations in cotton organs. The W2 treatment produced the maximum nitrogen concentration, except for the root nitrogen concentration in 2021. The nitrogen concentration in the roots and stems peaked at 240 kg ha−1 of nitrogen application rate. The topsoil drought intensity and nitrogen application rate had considerable influences on the cotton dry matter. The nitrogen application rate had a significant impact on the following indexes: internal nitrogen-fertiliser use efficiency (INUE), physiological nitrogen-fertiliser use efficiency (PNUE), and nitrogen-fertiliser recovery efficiency (NRE), except for PNUE in 2020. The INUE of other treatments decreased by 13.82–43.44% compared with CK treatment. In 2021, fibre length and elongation were significantly impacted by the topsoil drought intensity, nitrogen application rates, and their interactions. The nitrogen application rate’s effects on the uniformity index were significant in 2020 and 2021. The hydraulic lift magnitude, NRE, and NO3−-N in the 0–10 cm soil were significantly correlated with each other. There were correlations among cotton quality indexes: fibre length and strength, uniformity index and micronaire, and micronaire and elongation. These findings provide a reference for future research on the mechanism by which hydraulic lift participates in nitrogen distribution in soil and crops and also offer a new direction to utilize deep water to improve the utilization rate of water resources.

Accurate runoff prediction in complex slope catchments remains challenging due to terrain heterogeneity and dynamic rainfall interactions. This study conducts a systematic comparison between a physics-based Two-Dimensional Slope Hydrodynamic Model (TDSHM) and data-driven deep learning models (LSTM and CNN) for runoff forecasting under variable rainfall conditions. Using 214 rainfall–runoff events (2013–2023) from the Qiaotou watershed in Nanjing, China, the TDSHM integrates rainfall momentum, wind effects, and hydrodynamic principles to resolve spatiotemporal flow dynamics, while LSTM and CNN models leverage seven hydrological features for data-driven predictions. Results demonstrate that the TDSHM achieved superior accuracy, with a mean relative error of 10.77%, Nash–Sutcliffe Efficiency (NSE) of 0.801, and Mean Absolute Error (MAE) of 3.17 mm, outperforming LSTM (24.38% error, NSE = 0.751, MAE = 4.61 mm) and CNN (28.10% error, NSE = 0.506, MAE = 6.82 mm). The TDSHM’s explicit physical interpretability enabled precise simulation of vegetation-modulated runoff processes, validated against field observations (92% predictions within ±15% error). While LSTM captured temporal dependencies effectively, CNN exhibited limitations in sequential data processing. This study highlights the TDSHM’s robustness for scenarios requiring mechanistic insights and the complementary role of LSTM in data-rich environments. The findings provide critical guidance for flood risk management, soil conservation, and model selection trade-offs between physical fidelity and computational efficiency.

The extreme weather and the deteriorating water environment have exacerbated the crisis of freshwater resource insufficiency. Many studies have shown that salty water could replace freshwater to partly meet the water demand of plants. To study the effects of early-stage drought hardening and late-stage salt stress on tomatoes (Solanum lycopersicum L.), we conducted a 2-year pot experiment. Based on the multi-objective demands of high yield, high quality, and water saving, yield indicators, quality indicators, and a water-saving indicator were selected as evaluation indicators. Three irrigation levels (W1: 85% field capacity (FC), W2: 70% FC, W3: 55% FC) and three salinity levels (S2: 2 g/L, S4: 4 g/L, S6: 6 g/L) were set as nine treatments. In addition, a control treatment (CK: W1, 0 g/L) was added. Each treatment was evaluated and scored by principal component analysis. The results for 2022 and 2023 found the highest scores for CK, W2S2, W3S2 and CK, W2S4, W2S2, respectively. Based on response surface methodology, we constructed composite models of multi-objective demands, whose results indicated that 66–72% FC and 2 g/L salinity were considered the appropriate water–salt combinations for practical production. This paper will be beneficial for maintaining high yield and high quality in tomato production using salty water irrigation.

ContextCotton production is influenced by water and nitrogen (N). However, the magnitude and direction of seed cotton yield, water use efficiency (WUE) and N use efficiency (NUE) responses to water and N inputs varied among the available studies due to different experimental and environmental factors (such as cotton varieties, climate types and irrigation systems).ObjectiveTo quantify the relationships between water and N inputs and seed cotton yield, WUE and NUE and estimate the potential for water and N optimization, 61 studies conducted in 9 countries were collected to establish a meta database.MethodsThe relationships between water and N inputs and the interesting response variables were studied using a linear mixed-effects model in a partially restricted dataset. And the potential for water and N optimization was discussed using a meta-analysis in three classified datasets.Results and conclusionsTotal water and N inputs had significant positive effects on seed cotton yield. WUE was negatively related to water input and positively related to N input, while NUE was positively related to water input and unrelated to N input. Negative interaction between water and N inputs existed in WUE and NUE, and the interaction was relatively large in NUE compared to in WUE. Reducing over-optimal water input to optimal may increase seed cotton yield by 12.3%, WUE by 25.0% and NUE by 2.2%. Similarly, reducing over-optimal N input to optimal may increase seed cotton yield and WUE by about 16.0% and NUE by 44.6%. There was great potential for optimizing water and N inputs in arid and hot desert climate (BWh) comparing with in arid and cold desert climate (BWk) and arid and cold steppe climate (BSk), and the seed cotton yield, WUE and NUE can be increased by up to around 35.0%, 16.6% and 34.5%, respectively. Surface irrigation had greater potential to optimize water and N inputs than drip irrigation, and the seed cotton yield and WUE can be increased by up to 50.7% and 43.0%, respectively.SignificanceThe findings provided suggestions for improving irrigation and fertilization in cotton production.

In an arid region, water shortage limits agricultural development, and worse, soil salinization is accompanied by soil moisture drought. In this region, hydraulic lift occurs due to the drying upper layer caused by high precipitation and the wet lower layer. Hydraulic lift is defined as water redistribution from wetter, deeper soil layers to drier, shallower soil layers near the soil surface through the plant roots. To examine the effects of water and salt stresses on tomato yield and fruit quality under the condition of hydraulic lift, a 2year experiment was conducted. Different water and salt treatments were designed, including three water levels (W1, W2 and W3 indicating soil moisture contents of 60%–70%, 50%–60% and 40%–50% of the field capacity, respectively) and four salt levels (S0, S1, S2 and S3 indicating NaCl addition of 0%, 0.2%, 0.4% and 0.6% of the dry soil weight, respectively) of the upper pot, and water and salt levels of control treatment (CK) were W1 and S0, respectively. The yield under other treatments significantly decreased by 4.59%–58.39% and 5.12%–62.96% in 2018 and 2019, respectively, compared with that under CK, and the yield under W1S1 treatment had no significance with that under CK in 2018. The firmest fruit quality was observed in the plant under W3S1 treatment, and the percentage increases were 28.67% and 28.89% in both years compared with that under CK. Water, salt stress and their interactions had significant effects on tomato taste quality and vitamin C. Tomato taste quality and vitamin C decreased under the W3 and S3 treatments. In both years, the total magnitudes of hydraulic lift during the entire growth period were higher under the W1S2 treatment (65.20% and 76.06%, respectively) than that under CK. Whereas yield and total magnitudes of hydraulic lift were significantly both correlated with single fruit weight, single fruit volume, fruit shape index and taste qualities, no correlations were observed between hydraulic lift and yield. Mild waterdeficit and salt stresses could improve tomato quality with negligible yield loss, and hydraulic lift had positive effects on fruit quality. Principal component analysis revealed that the combination of W1 and S1 treatments increased fruit quality and total hydraulic lift magnitudes with an acceptable yield decline. These results are important for tomato production in arid saline-alkali region where hydraulic lift is positively corelated with fruit quality and famers may consider this trait to resist drought and soil salinization. Future studies focusing on the effects of the internal mechanisms of hydraulic lift caused by changes in sap flow on tomato quality and yield are warranted.

Assessing the impact of varied rainfall patterns on soil and water loss within a hilly watershed over an extended temporal scope holds paramount importance in comprehending regional runoff and sediment traits. This study utilized continuous rainfall and sediment data spanning from 2013 to 2021, and the K-means clustering method was employed to analyze rainfall types. Subsequently, the rain-type characteristics underwent further analysis through LSD, and a multiple linear regression equation was formulated. The result showed that: within the Qiaotou small basin, rainfall, maximum rainfall intensity within 30 min (I30), and rainfall erosivity exhibited notable effects on sediment yield and loss. The water-sediment attributes of 305 rainfall events were characterized by rainfall below 100 mm, I30 of less than 35 mm/h, a runoff coefficient below 0.5, and sediment content under 0.6 g/L. According to the characteristics of different rainfall types and the degree of influence on water and sediment in small watersheds, 305 rainfall events in the basin were divided into three types by the K-means clustering analysis method: A (heavy rainfall, moderate rain), B (small rainfall, light rain), and C (medium rainfall, heavy rain). The most frequent rain type observed was B, followed by C, while A had the lowest frequency. Despite the lower intensity of B-type rainfall, it holds significant regional importance. Conversely, C-type rainfall, although intense and short, serves as the primary source of sediment production. The multiple regression equation effectively models both sediment yield modulus and flood peak discharge, exhibiting an R2 coefficient exceeding 0.80, signifying significance. This equation enables the quantitative calculation of pertinent indicators. Sediment yield modulus primarily relies on sediment concentration, runoff depth, and rainfall, while peak discharge is significantly influenced by runoff depth, sediment concentration, and I30. Furthermore, the efficacy of various soil and water conservation measures for flow and sediment reduction correlates with I30. Overall, the impact of different measures on reducing flow and sediment increases with a higher I30, accompanied by a reduced fluctuation range.

Plant height and biomass are important indicators of rice yield. Here we combined measured plant physiological traits with a crop growth model driven by unmanned aerial vehicle spectral data to quantify the changes in rice plant height and biomass under different irrigation and fertilizer treatments. The study included two treatments: I—water availability factor (i.e., three drought objects, optimal, and excess water); and II—two levels of deep percolation and five nitrogen fertilization doses. The introduced model is extreme learning machine (ELM), back propagation neural network (BPNN), and particle swarm optimization-ELM (PSO-ELM), respectively. The results showed that: (1) Proper water level regulation (3~5 cm) significantly increased the accumulation of spike biomass, which was about 6% higher compared to that under flooded conditions. (2) For plant height inversion, the ELM model was optimal with a mean coefficient of determination of 0.78, a mean root mean square error of 0.26 cm, and a mean performance deviation rate of 2.08. For biomass inversion, the PSO-ELM model was optimal with a mean coefficient of determination of 0.88, a mean root mean square error of 3.8 g, and a mean performance deviation rate of 3.29. This study provided the possible opportunity for large-scale estimations of rice yield under environmental disturbances.

Biochar application is an effective way to improve soil organic carbon (SOC) content and ensure food security. However, there were differences in SOC content following biochar application under different conditions. We collected 637 paired comparisons from 101 articles to determine the following: (1) the average effect of biochar application on SOC content and (2) the response of SOC content to different soil nutrient contents, climate zones and cropping systems following biochar application. The results showed that the soil available phosphorus (P) content and soil available potassium (K) content reached the highest level in the category of <10 mg kg−1 and >150 mg kg−1, respectively. Soil total P content subgroups achieved maximum increase in the intermediate category. The Cw zone (temperate, without dry season) obtained the maximum level of SOC content. Compared with plough tillage, rotary tillage presented significantly higher SOC content. Therefore, low available P and K contents, moderate soil total N and P contents, rotary tillage and the Cw zone were more effective in increasing SOC content. Furthermore, the results of a random forest algorithm showed that soil nutrient contents were the most important variables. This study provided a scientific basis for SOC sequestration and improving soil fertility.

为充分利用咸水资源,采用避雨棚桶栽玉米试验,研究了淡水,4,5,6 g/L这4 个灌水矿化度和0％,2％,5％这 3 个生物炭施用量对玉米叶片叶绿素含量、净光合速率和荧光参数等光合特性指标,以及植株籽粒产量和水分利用效率的影响.试验结果表明:与淡水灌溉相比,咸水灌溉下,玉米叶片叶绿素含量降低了4.07％～19.62％,净光合速率降低 27.23％～57.46％,叶片初始荧光产量F0 逐渐增大,而 Fm 和 Fv/Fm 逐渐减小,光合作用受到抑制,玉米单株籽粒产量降低8.42％～36.03％,水分利用效率降低4.81％～33.96％;施加生物炭可以缓解咸水灌溉带来的盐分胁迫影响,促进玉米叶片光合作用,提高作物的CO2和光能利用潜力,增加玉米籽粒产量、干物质质量及水分利用效率.综合考虑其增益效果和经济性,处理B2S4 的缓解效果最佳,可为咸水灌溉的实际生产提供参考.

Drought hardening could promote the development of plant roots, potentially improving the resistance of crops to other adversities. To investigate the response and resistance of physiological and growth characteristics induced by drought hardening to salt stress in the later stages, a greenhouse experiment was carried out from 2021 to 2022 with one blank control treatment and twelve treatments that comprised combinations of four irrigation regimes (W1 = 85%, W2 = 70%, W3 = 55%, and W4 = 40% of the field capacity) and three irrigation water salinity levels (S2, S4, and S6, referring to 2 g, 4 g, and 6 g of sodium chloride added to 1000 mL of tap water, respectively). The results show that saline water irrigation introduced a large amount of salt into the soil, resulting in the deterioration of tomato growth, physiology, yield, and water use efficiency (WUE), but had a positive, significant effect on fruit quality. When the irrigation water salinity was 2 g L−1, the W2 treatment could reduce soil salt accumulation, even at the end of the maturation stage; consequently, enhancing the increments in plant height and leaf area index during the whole growing stage. The physiological activity of tomato plants under the W2 and W3 treatments showed a promoting effect. Correspondingly, the maximum values of the fruit quality of tomato plants irrigated with the same saline water were all obtained with the W2 or W3 treatment. However, the yield and WUE of the W3 treatment were lower than that of the W2 treatment, which was the highest among the same saline water irrigation treatments, consistent with the reflection of the changing trend of the ratio of fresh weight to dry weight. Overall, drought hardening can be considered an economically viable approach to mitigate the hazards of saline water irrigation, and the W2S2 combination is recommended for tomato production due to the maximum values of yield and WUE with a higher fruit quality among the twelve saline water irrigation treatments.

Dry topsoil and relatively moist subsoil can occur in specific areas and times, limiting plant growth but creating conditions for hydraulic lift (HL). There is a lack of a rational water and nitrogen (N) strategy to improve cotton growth and maintain HL. This study investigated the effects of three topsoil water conditions (W0.6: 60–70%, W0.5: 50–60%, and W0.4: 40–50% of field capacity) and three N rates (N120-120, N240-240, and N360-360 kg N ha−1) plus one control treatment on cotton growth and HL under dry topsoil conditions in 2020 and 2021. The results showed that plant height and leaf area increased with increasing N rate, but the differences among topsoil water conditions were relatively small, except for leaf area in 2021. The HL water amount of all treatments increased gradually and then continued to decline during the observation period. There was a trend that the drier the topsoil or the more N applied, the greater the amount of HL water. Additionally, topsoil water conditions and N rate significantly affected the total HL water amount and root morphological characteristics (root length, surface area, and volume). Seed and lint cotton yield tended to decrease with increasing topsoil dryness at N240 or N360, except for lint yield in 2021, or with decreasing N rate, especially under W0.6. As topsoil became drier, the total evapotranspiration (ET) decreased, while with the increase in N rate, ET showed small differences. Water use efficiency increased with a higher N rate, while N partial factor productivity (PFPN) did the opposite. Furthermore, the PFPN under W0.4 was significantly lower than that under W0.6 at N240 or N120. These findings could be useful for promoting the utilization of deep water and achieving sustainable agricultural development.

It has been extensively investigated whether salt water can be used as an alternative water resource for irrigation in tomato production due to the increasing freshwater scarcity. Changes in tomato yield under salt water irrigation were variable, ranging from -96.8% to 36.2%. Thus, a meta-analysis was performed by collecting 988 paired comparisons from 69 articles to draw a quantitative summarization that there was a significant negative effect of salt water irrigation on tomato yield with a grand mean decrease of 27.8%. Compared to the control, tomato yield significantly reduced by 39.0% on average following saline water irrigation (SI), significantly higher than that under brackish water irrigation (BI). Medium soil is the most suitable place to apply salt water for irrigation. BI performed best in alkali soil, while following SI higher tomato yield was obtained in non-alkali soil. There was a trend whereby decreased soil bulk density and soil salinity led to smaller reduction in tomato yield regardless of irrigation water salinity. Applying local irrigation or discontinuous irrigation was more possible to achieve higher tomato yield under salt water irrigation. These findings can provide a reference for developing rational salt water irrigation policies in tomato production.

Photosynthesis is the key physiological activity in the process of crop growth and plays an irreplaceable role in carbon assimilation and yield formation. This study extracted rice (Oryza sativa L.) canopy reflectance based on the UAV multispectral images and analyzed the correlation between 25 vegetation indices (VIs), three textural indices (TIs), and net photosynthetic rate (Pn) at different growth stages. Linear regression (LR), support vector regression (SVR), gradient boosting decision tree (GBDT), random forest (RF), and multilayer perceptron neural network (MLP) models were employed for Pn estimation, and the modeling accuracy was compared under the input condition of VIs, VIs combined with TIs, and fusion of VIs and TIs with plant height (PH) and SPAD. The results showed that VIs and TIs generally had the relatively best correlation with Pn at the jointing-booting stage and the number of VIs with significant correlation (p< 0.05) was the largest. Therefore, the employed models could achieve the highest overall accuracy [coefficient of determination (R-2) of 0.383-0.938]. However, as the growth stage progressed, the correlation gradually weakened and resulted in accuracy decrease (R-2 of 0.258-0.928 and 0.125-0.863 at the heading-flowering and ripening stages, respectively). Among the tested models, GBDT and RF models could attain the best performance based on only VIs input (with R-2 ranging from 0.863 to 0.938 and from 0.815 to 0.872, respectively). Furthermore, the fusion input of VIs, TIs with PH, and SPAD could more effectively improve the model accuracy (R-2 increased by 0.049-0.249, 0.063-0.470, and 0.113-0.471, respectively, for three growth stages) compared with the input combination of VIs and TIs (R-2 increased by 0.015-0.090, 0.001-0.139, and 0.023-0.114). Therefore, the GBDT and RF model with fused input could be highly recommended for rice Pn estimation and the methods could also provide reference for Pn monitoring and further yield prediction at field scale.

Analysis of the spatial and temporal variation patterns of surface evapotranspiration is important for understanding global climate change, promoting scientific deployment of regional water resources, and improving crop yield and water productivity. Based on Landsat 8 OIL_TIRS data and remote sensing image data of the lower Yangtze River urban cluster for the same period of 2016–2021, combined with soil and meteorological data of the study area, this paper constructed a multiple linear regression (MLR) model and an extreme learning machine (ELM) inversion model with evapotranspiration as the target and, based on the model inversion, quantitatively and qualitatively analyzed the spatial and temporal variability in surface evapotranspiration in the study area in the past six years. The results show that both models based on feature factors and spectral indices obtained a good inversion accuracy, with the fusion of feature factors effectively improving the inversion ability of the model for ET. The best model for ET in 2016, 2017, and 2021 was MLR, with an R2 greater than 0.8; the best model for ET in 2018–2019 was ELM, with an R2 of 0.83 and 0.62, respectively. The inter-annual ET in the study area showed a “double-peak” dynamic variation, with peaks in 2018 and 2020; the intra-annual ET showed a single-peak cycle, with peaks in July–August. Seasonal differences were obvious, and spatially high-ET areas were mainly found in rural areas north of the Yangtze River and central and western China where agricultural land is concentrated. The net solar radiation, soil heat flux, soil temperature and humidity, and fractional vegetation cover all had significant positive effects on ET, with correlation coefficients ranging from 0.39 to 0.94. This study can provide methodological and scientific support for the quantitative and qualitative estimation of regional ET.

为研究水稻叶片叶绿素相对含量(SPAD)在3种水分处理和5种施氮处理下的变化规律,探讨无人机多光谱遥感技术反演水稻SPAD的可行性,本研究利用大疆精灵4多光谱无人机,采集了水稻拔节孕穗期、抽穗开花期和乳熟期的冠层多光谱遥感影像,并同步测定水稻SPAD值,基于25个光谱变量(5个波段反射率和20个植被指数),采用多元线性逐步回归、岭回归和套索回归3种方法构建了水稻SPAD的反演模型.结果表明:水稻3个生育期的SPAD最佳反演模型均是采用套索回归方法构建的,其中乳熟期建立的SPAD最佳反演模型在3个生育期中的反演精度最高,决定系数为0.782,均方根误差为1.2177,相对误差为6.6113％.因此,该研究可对水稻叶片SPAD进行遥感监测,并为水稻精准灌溉和施肥提供科学依据和数据支撑.

The south of China is prone to waterlogging and non-point source pollution due to its heavy rainfall. Draining excess water to lower the groundwater level and application of biochar to improve soil properties can mitigate the waterlogging to some extent. Hence, the RZWQM2 model calibrated and validated using measured data was used to identify suitable field management scenarios (defined as a combination of different groundwater depths and biochar additions) under different future climate conditions (RCP 4.5 and RCP 8.5 under the BCC-CSM1-1 regional climate model). Dry yield and water use efficiency (WUE) simulations showed that compared to no biochar addition, dry yield increased by 0.17 - 2.94% and -0.3 - 2.77% under RCP 4.5 and RCP 8.5, respectively, and WUE increased by -0.72 - 2.91% and -1.59 - 2.79% under RCP 4.5 and RCP 8.5, respectively, for biochar additions of 1 - 2%. The simulation results of the N loss showed that compared to the groundwater depth of 60 cm, the N loss from surface drainage and subsurface drainage was reduced by 36.68 - 81.79% and 30.53 - 80.67% under RCP 4.5 and RCP 8.5, respectively, when the groundwater depth was 80 - 100 cm. It is appropriate to control the groundwater depth in the range of 80 - 100 cm and biochar addition ratio around 2% under RCP 4.5, and under RCP 8.5, the groundwater depth in the range of 80 - 100 cm and biochar addition ratio around 1%. The results of the study suggest that it is necessary to adopt reasonable strategies to cope with future climate change in order to ensure the safety of agricultural production. The RZWQM2 model may be a reliable approach to optimize field management strategies.

从农田(源头)—沟渠(过程)—水塘/湿地(末端),构建了"控制灌排—生态沟渠—仿生增氧人工湿地"农田面源污染全流程控制技术.通过控制灌排、基于基底功能强化的沟渠生态化改造、多功能仿生增氧湿地构建等单元治理技术耦合,形成了农田面源污染治理技术体系,并在南京市八卦洲街道PY农业园区进行了中试示范研究.研究结果表明:全流程耦合技术能有效降低面源排水的污染物质量浓度,示范区面源排水中主要污染物CODMn、NH3-N、TN、TP质量浓度降幅均超过20％以上,实现了污染负荷的高效削减.研究结果为乡村振兴中农田面源污染的有效削减和防控提供了重要的技术支撑.