To overcome the multiple challenges of water scarcity, agricultural land conversion, food security, and carbon emissions, an optimal collaborative management scheme for food production is urgently needed, especially in high food-production and food-consumption countries such as China. The water–land–food–carbon (WLFC) nexus provides a new perspective, but its interactions are complex, dynamic, and spatially heterogeneous; the coupling mechanism is not fully understood; and the driving forces and regulation strategies remain uncertain. Therefore, in this study, the WLFC nexus centered on low-carbon and high-quality agricultural development was systematically reviewed. The main contributions are as follows: (1) A framework of the regional agricultural WLFC nexus was proposed based on bibliographic analysis. (2) The main internal and external factors influencing the WLFC nexus in agriculture were identified by reevaluating meta-analysis review studies. The results showed that changes in the amount and type of irrigation water, the amount and planting activities of agricultural land, and climate change (temperature, precipitation, and CO₂ concentration) affected food (rice, wheat, and maize) yields and carbon emissions to varying degrees. Moreover, population, technological innovation, trade, and polices were important external factors impacting food production and carbon emissions. (3) The common methods and tools for assessing, simulating, and optimizing the WLFC nexus in agriculture were summarized from the perspectives of its status, physical links, and embodied links. Integrated indices, complex system thinking, and process-based and data-driven methods were applied in the studies of the WLFC nexus. (4) Strategies and programs for collaborative WLFC management in agriculture within 10 global river basins were compiled. These findings could help us better understand the WLFC nexus in agriculture and identify the optimal cooperative management scheme, thereby realizing low-carbon and high-quality agricultural development.

To more precisely monitor drought, a new remote sensing-based drought index, the Vapor Pressure Deficit–Soil Moisture–Sun-Induced Chlorophyll fluorescence Dryness Index (VMFDI), with a spatial resolution of 1 km based on vapor pressure deficit (VPD), soil moisture (SM), and sun-induced chlorophyll fluorescence (SIF) data was constructed via a three-dimensional spatial distance model, and it was used to monitor dryness in the Yellow River Basin during 2003–2020. The spatiotemporal variations in and main factors of the VMFDI and agroecosystem responses were analyzed via the Theil–Sen median and Mann–Kendall tests and Liang–Kleeman information flow. The results revealed the following: (1) The VMFDI effectively monitors regional drought and is more sensitive than other indices like the standardized precipitation evapotranspiration index (SPEI) and GRACE drought severity index and single variables. (2) VMFDI values fluctuated seasonally in the Yellow River Basin, peaking in August and reaching their lowest in March. The basin becomes drier in winter but wetter in spring, summer, and autumn, with the middle and lower reaches, particularly Shaanxi and Gansu, being drought-prone. The VMFDI values in the agroecosystem were lower. (3) SM and VPD dominated drought at the watershed and agroecosystem scales, respectively. Key agroecosystem indicators, including greenness (NDVI), gross primary productivity (GPP), water use efficiency (WUE), and leaf area index (LAI), were negatively correlated with drought (p < 0.05). When VPD exceeded a threshold range of 7.11–7.17 ha, the relationships between these indicators and VPD shifted from positive to negative. The specific VPD thresholds in maize and wheat systems were 8.03–8.57 ha and 7.15 ha, respectively. Suggestions for drought risk management were also provided. This study provides a new method and high-resolution data for accurately monitoring drought, which can aid in mitigating agricultural drought risks and promoting high-quality agricultural development.

Studies have investigated heavy metal (HM) contamination in the Bohai Sea, but primarily in seawater and associated sediments, or in single rivers. For the first time, 31 major rivers discharging into the Bohai Sea were analyzed, along with 27 uniformly distributed coastal seawater samples and selected invertebrates. The elements measured were As, Cd, Cr, Cu, Ni, Pb, V, and Zn. We calculated the ‘geo-accumulation index’, the ‘metal enrichment factor’, and the ‘contamination factor’, coupled with the ‘pollution load index’, and our findings suggested low-grade HM pollution, although two conspicuous associations of elements were found to stand out in particular: One is a combination of As, Cu, Cr, and V in seawater samples that may indicate pollution from intensive ship traffic. The other shows a significant pattern of Cr, Pb, and Zn in water samples from rivers discharging between Yantai and Weihai on the Shandong Peninsula at the south edge of the Bohai Sea. This is primarily a farming area, with a moderate share of industrial enterprises. Investigations including fertilizers and pesticides point to agricultural practices and textile printing/chrome tanneries as the causes of contamination. Overall, a significant decline was found in the HM load in the rivers, apart from those discharging into the Yellow Sea section.

While satellite-based remote sensing offers a promising avenue for large-scale LCC estimations, the accuracy of evaluations is often decreased by mixed pixels, attributable to distinct farming practices and diverse soil conditions. To overcome these challenges and to account for maize intercropping with soybeans at different growth stages combined with varying soil backgrounds, a hyperspectral database for maize was set up using a random linear mixed model applied to hyperspectral data recorded by an unmanned aerial vehicle (UAV). Four methods, namely, Euclidean distance, Minkowski distance, Manhattan distance, and Cosine similarity, were used to compare vegetation spectra from Sentinel-2A with the newly constructed database. In a next step, widely used vegetation indices such as NDVI, NAOC, and CAI were tested to find the optimum method for LCC retrieval, validated by field measurements. The results show that the NAOC had the strongest correlation with ground sampling information (R² = 0.83, RMSE = 0.94 μg/cm², and MAE = 0.67 μg/cm²). Additional field measurements sampled at other farming areas were applied to validate the method’s transferability and generalization. Here too, validation results showed a highly precise LCC estimation (R² = 0.93, RMSE = 1.10 μg/cm², and MAE = 1.09 μg/cm²), demonstrating that integrating UAV hyperspectral data with a random linear mixed model significantly improves satellite-based LCC retrievals.

Pine wilt disease (PWD), caused by Bursaphelenchus xylophilus Steiner & Buhrer (pine wood nematodes, PWN), impacts forest carbon sequestration and climate change. However, satellite-based PWD monitoring is challenging due to the limited spatial resolution of Sentinel’s MSI sensor, which reduces its sensitivity to subtle biochemical alterations in foliage. We have, therefore, developed a slope product index (SPI) for effective detection of PWD using single-date satellite imagery based on spectral gradients in the visible and near-infrared (VNIR) range. The SPI was compared against 15 widely used vegetation indices and demonstrated superior robustness across diverse test sites. Results show that the SPI is more sensitive to changes in chlorophyll content in the PWD detection, even under potentially confounding conditions such as drought. When integrated into Random Forest (RF) and Back-Propagation Neural Network (BPNN) models, SPI significantly improved classification accuracy, with the multivariate RF model achieving the highest performance and univariate with SPI in BPNN. The generalizability of SPI was validated across test sites in distinct climate zones, including Zhejiang (accuracyZ_Mean = 88.14%) and Shandong (accuracyS_Mean = 78.45%) provinces in China, as well as Portugal. Notably, SPI derived from Sentinel-2 imagery in October enables more accurate and timely PWD detection while reducing field investigation complexity and cost.