Leaf area index (LAI) is a key parameter in plant growth monitoring. For several decades, vegetation indices-based empirical method has been widely-accepted in LAI retrieval. A growing number of spectral indices have been proposed to tailor LAI estimations, however, saturation effect has long been an obstacle. In this paper, we classify the selected 14 vegetation indices into five groups according to their characteristics. In this study, we proposed a new index for LAI retrieval-transformed triangular vegetation index (TTVI), which replaces NIR and red bands of triangular vegetation index (TVI) into NIR and red-edge bands. All fifteen indices were calculated and analyzed with both hyperspectral and multispectral data. Best-fit models and k-fold cross-validation were conducted. The results showed that TTVI performed the best predictive power of LAI for both hyperspectral and multispectral data, and mitigated the saturation effect. The R² and RMSE values were 0.60, 1.12; 0.59, 1.15, respectively. Besides, TTVI showed high estimation accuracy for sparse (LAI < 4) and dense canopies (LAI > 4). Our study provided the value of the Red-edge bands of the Sentinel-2 satellite sensors in crop LAI retrieval, and demonstrated that the new index TTVI is applicable to inverse LAI for both low-to-moderate and moderate-to-high vegetation cover.

In recent decades, rice disease co-epidemics have caused tremendous damage to crop production in both China and Southeast Asia. A variety of remote sensing based approaches have been developed and applied to map diseases distribution using coarse- to moderate-resolution imagery. However, the detection and discrimination of various disease species infecting rice were seldom assessed using high spatial resolution data. The aims of this study were (1) to develop a set of normalized two-stage vegetation indices (VIs) for characterizing the progressive development of different diseases with rice; (2) to explore the performance of combined normalized two-stage VIs in partial least square discriminant analysis (PLS-DA); and (3) to map and evaluate the damage caused by rice diseases at fine spatial scales, for the first time using bi-temporal, high spatial resolution imagery from PlanetScope datasets at a 3 m spatial resolution. Our findings suggest that the primary biophysical parameters caused by different disease (e.g., changes in leaf area, pigment contents, or canopy morphology) can be captured using combined normalized two-stage VIs. PLS-DA was able to classify rice diseases at a sub-field scale, with an overall accuracy of 75.62% and a Kappa value of 0.47. The approach was successfully applied during a typical co-epidemic outbreak of rice dwarf (Rice dwarf virus, RDV), rice blast (Magnaporthe oryzae), and glume blight (Phyllosticta glumarum) in Guangxi Province, China. Furthermore, our approach highlighted the feasibility of the method in capturing heterogeneous disease patterns at fine spatial scales over the large spatial extents.

Lakes are essential components of the terrestrial water cycle. Their size and quantity reflect natural climate change and anthropogenic activities in time. Lakes on the Inner-Mongolian Plateau (IMP) have experienced significant changes in recent decades, but the current situation remains elusive. In this study, we conducted multi-decadal intensive monitoring of lake area and performed comprehensive variation analysis on the IMP. The study involved pre-processing, lake area extraction, post-processing, and lake area analysis procedures using multi-source satellite images. The results reveal the detailed variation in the lake from various aspects. The temporal analysis indicates that the lake area has undergone two distinct periods of decline followed by subsequent increase, and the variation pattern from January to December was diverse. The spatial analysis suggests that the lake area expanded slightly in the western and southern parts, while the area shrank in the other two. Variations in lakes of different sizes exhibited spatial heterogeneity. To some extent, effective actions have led to lake rehabilitation, but it has not yet reached or surpassed the level of 2000. This study provides a substantial data basis and technological guidance for conducting lacustrine surveys. The results can play important roles in fostering further analyses of the water cycle and the carbon cycle.

Leaf area index (LAI), as one of the most important indicators in vegetation growth monitoring, has been widely used in crop growth monitor and yield estimation. Previous studies focused on LAI retrieval for whole growth stages, this study inversed LAI at five main growth stages using widely-acknowledged vegetation indices and their optimized red-edge transformations. We used hyperspectral data to analyse relationships between LAI and vegetation indices. Partial least square discriminant analysis (PLS-DA) was used in this study to distinguish the best indices for distinct growth stages. The results show that LAI retrieval at various growth stages have distinct results. The best fitted indices for different growth stages are not fixed. The best indices for five stages are OSAVI_re1, SR_re1, SAVI_re2, SAVI_re2 and OSAVI_re1 with R² value of 0.20, 0.50, 0.49, 0.72 and 0.23, respectively. Most vegetation indices show better performance in milky stage than other four stages this may due to the high vegetation cover in this stage because leaves rise to maximum. Indices composed of red-edge exhibited better relationships with LAI with higher R² and lower RMSE. Indices combined reflectance at 740 nm are capable of inversing LAI at stem elongation, anthesis and milk development stages. Indices with 705 nm showed better results in jointing and ripening stages. The results indicate that:1) specific vegetation indices for distinct growth stages lead to better estimation than inversing LAI for whole growth stages; 2) indices take advantage of red-edge showed better potentials in estimating high LAI than traditional visible wavelength.

High-precision quantification of regional precipitation is the foundation of watershed water balance analysis and water resources management. However, current approaches are hindered by methodological diversity and inconsistent results, with limited accuracy, especially in gauge-sparse regions and areas with drastic topographic relief. This study proposes a dual thin-plate spline (TPS) fusion method combining satellite observations and gauge observations to develop a daily precipitation dataset named CGS01. Multi-dimensional comparisons and comprehensive accuracy evaluations are conducted against several mainstream precipitation products including CN05.1, CMFD_V1 and CHM_PRE_V2. From a methodological perspective, this paper analyzes the core difficulties and feasible solutions for regional precipitation estimation, and discusses the error sources of GPM satellite precipitation data, as well as the application potential of the proposed method in watershed water balance assessment and high-precision global precipitation quantification. The results reveal that compared with digital elevation model (DEM) and reanalysis data used as covariates in TPS method, satellite precipitation data exhibits higher reliability when serving as covariates, while corrections for systematic errors and suppression of random errors are still required. A novel systematic error correction method for GPM data is proposed. Adopting only 27 % of the gauge numbers used in previous studies, the newly developed CGS01 presents obviously superior performance to existing datasets. The national average error of annual precipitation is 2.0 %. Validated against observed runoff data, the watershed water balance analysis error is 10.9 %. Furthermore, CGS01 can reasonably reflect the controlling effects of topography and wind direction on precipitation spatial patterns in high mountain and canyon areas without in-situ observations. Compared with CHM_PRE_V2, CGS01 daily precipitation shows improvements of 14.2 %, 2.9 %, 21.3 %, and 8.2 % in the correlation coefficient, Kling-Gupta efficiency, probability of detection, and mean absolute error. The method proposed in this study has three prominent strengths: it requires no preset empirical parameters, delivers highly reproducible results, and imposes low demands on gauge station density. The CGS01 dataset shows that from 2000 to 2024 eight out of China’s ten first level watersheds showed annually wetting trends, with trends ranging from 0.5 to 8.4 mm per year, and in seven of the ten first level watersheds, the contribution ratio of daily extreme precipitation to total precipitation increased at a rate of 0.2 %–0.5 % per year. Free access to the dataset can be found at https://doi.org/10.5281/zenodo.20176004 (Yan et al., 2026).