Saturation effects limit the application of vegetation indices (VIs) in dense vegetation areas. The possibility to mitigate them by adopting a negative soil adjustment factor X is addressed. Two leaf area index (LAI) data sets are analyzed using the Google Earth Engine (GEE) for validation. The first one is derived from observations of MODerate resolution Imaging Spectroradiometer (MODIS) from 16 April 2013, to 21 October 2020, in the Apiacás area. Its corresponding VIs are calculated from a combination of Sentinel-2 and Landsat-8 surface reflectance products. The second one is a global LAI dataset with VIs calculated from Landsat-5 surface reflectance products. A linear regression model is applied to both datasets to evaluate four VIs that are commonly used to estimate LAI: normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed SAVI (TSAVI), and enhanced vegetation index (EVI). The optimal soil adjustment factor of SAVI for LAI estimation is determined using an exhaustive search. The Dickey-Fuller test indicates that the time series of LAI data are stable with a confidence level of 99%. The linear regression results stress significant saturation effects in all VIs. Finally, the exhaustive searching results show that a negative soil adjustment factor of SAVI can mitigate the SAVIs’ saturation in the Apiacás area (i.e., X = −0.148 for mean LAI = 5.35), and more generally in areas with large LAI values (e.g., X = −0.183 for mean LAI = 6.72). Our study further confirms that the lower boundary of the soil adjustment factor can be negative and that using a negative soil adjustment factor improves the computation of time series of LAI.

The quantity and quality of cropland are the key to ensuring the sustainable development of national agriculture. Remote sensing technology can accurately and timely detect the surface information, and objectively reflect the state and changes of the ground objects. Using high-resolution remote sensing images to accurately extract cropland is the basic task of precision agriculture. The traditional model of cropland semantic segmentation based on the deep learning network is to down-sample high-resolution feature maps to low resolution, and then restore from low-resolution feature maps to high-resolution ideas; that is, obtain low-resolution feature maps through a network, and then recover to high resolution by up-sampling or deconvolution. This will bring about the loss of features, and the segmented image will be more fragmented, without very clear and smooth boundaries. A new methodology for the effective and accurate semantic segmentation cropland of high spatial resolution remote sensing images is presented in this paper. First, a multi-temporal sub-meter cropland sample dataset is automatically constructed based on the prior result data. Then, a fully convolutional neural network combined with contextual feature representation (HRNet-CFR) is improved to complete the extraction of cropland. Finally, the initial semantic segmentation results are optimized by the morphological post-processing approach, and the broken spots are ablated to obtain the internal homogeneous cropland. The proposed method has been validated on the Jilin-1 data and Gaofen Image Dataset (GID) public datasets, and the experimental results demonstrate that it outperforms the state-of-the-art method in cropland extraction accuracy. We selected the comparison of Deeplabv3+ and UPerNet methods in GID. The overall accuracy of our approach is 92.03%, which is 3.4% higher than Deeplabv3+ and 5.12% higher than UperNet.

The assimilation of remote sensing data into mechanistic models of crop growth has become an available method for estimating yield. The objective of this study was to explore an effective assimilation approach for estimating maize grain protein content and yield using a canopy remote sensing data and crop growth model. Based on two years of field experiment data, the remote sensing inversion model using assimilation intermediate variables, namely leaf area index (LAI) and leaf nitrogen accumulation (LNA), was constructed with an R² greater than 0.80 and a low root-mean-square error (RMSE). The different data assimilation approaches showed that when the LAI and LNA variables were used together in the assimilation process (V_LAI+LNA), better accuracy was achieved for LNA estimations than the assimilation process using single variables of LAI or LNA (V_LAI or V_LNA). Similar differences in estimation accuracy were found in the maize yield and grain protein content (GPC) simulations. When the LAI and LNA were both intermediate variables in the assimilation process, the estimation accuracy of the yield and GPC were better than that of the assimilation process with only one variable. In summary, these results indicate that two physiological and biochemical parameters of maize retrieved from hyperspectral data can be combined with the crop growth model through the assimilation method, which provides a feasible method for improving the estimation accuracy of maize LAI, LNA, GPC and yield.

Soil nitrogen (N) content plays a vital role in agriculture and biogeochemical processes, ranging from the N fertilization management for intensive agricultural production to the patterns of N cycling in agroecological systems. While proximal sensing in laboratory settings can achieve ideal soil N estimation accuracy, the estimation and mapping by using remote sensing methods in a large spatial scale diplays low ability. A new hyperspectral imager with 166 spectral channels, the ZY1-02D, makes possible the detection of subtle but important spectral features of soil. This study aimed at exploring the capability of the ZY1-02D to estimate and map the topsoil N content of the black soil-covered farmlands in northeast China. To this aim, 646 soil samples from study sites were collected, processed, spectrally and geochemically measured for the soil N sensitive bands detection and partial least squares regression (PLSR) calibration and validation. The sensitive bands detection results showed an appealing regularity of the variability and stable tendency of the soil N sensitive spectral bands with the change of the sample size. Based on this, we compared the estimation capacity of the models developed with the full wavelength spectra and the models developed with the sensitive bands. The estimation based on ZY1-02D full wavelength spectral reflectance were robust, with R² of 0.64 in validation. Further, the results of model developed with the sensitive bands showed better validation accuracy with R² of 0.66 and were applied to create a map of topsoil N content of farmlands in the northeast China black soil area. The results demonstrated that sensitive bands modelling could enhance the accuracy of the estimation and simplify model, and what is more, showed the ideal capability of ZY1-02D for soil N content estimation at the regional scale.

Accurate and near-real-time flood monitoring is crucial for effective post-disaster relief efforts. Although extensive research has been conducted on flood classification, efficiently and automatically processing multi-source imagery to generate reliable flood inundation maps remains challenging. In this study, a new automatic flood monitoring method, utilizing optical and Synthetic Aperture Radar (SAR) imagery, was developed based on the Google Earth Engine (GEE) cloud platform. The Normalized Difference Flood Vegetation Index (NDFVI) was innovatively combined with the Edge Otsu segmentation method, utilizing SAR imagery, to enhance the initial accuracy of flood area mapping. To more effectively distinguish flood areas from non-seasonal water bodies, such as lakes, rivers, and reservoirs, pre-flood Landsat-8 imagery was analyzed. Non-seasonal water bodies were classified using multi-index methods and water body probability distributions, thereby further enhancing the accuracy of flood mapping. The method was applied to the catastrophic floods in Poyang Lake, Jiangxi Province, in 2020, and East Dongting Lake, Hunan Province, China, in 2024. The results demonstrated classification accuracies of 92.6% and 97.2% for flood inundation mapping during the Poyang Lake and East Dongting Lake events, respectively. This method offers efficient and precise information support to decision-makers and emergency responders, thereby fully demonstrating its substantial potential for practical applications.

The method of proximal VNIR-SWIR (with a spectral region of 400–2500 nm) spectroscopy in a laboratory setting has been widely employed in soil property estimations. Increasing attention has been focused recently on establishing an agreed-upon protocol for soil spectral measurement, fueled by the recognition that studies carried out under different laboratory settings have made future data sharing and model comparisons difficult. This study aimed to explore the key factors in a lab-based spectral measurement procedure to provide recommendations for enhancing the spectra quality and promoting the development of the spectral measurement protocol. To this aim, with the support of the standard spectral laboratory at Jilin University, China, we designed and performed control experiments on four key factors—the light interference in the measurement course, soil temperature, soil moisture, and soil particle size—to quantify the variation in the spectra quality by the subsequent estimation accuracies of different estimation models developed with different spectra obtained from control groups. The results showed that (1) the soil–probe contact measurement derived the optimum spectra quality and estimation accuracy; however, close-non-contact measurement also achieved acceptable results; (2) sieving the soil sample into particle sizes below 1 mm and drying before spectral measurement effectively enhanced spectra quality and estimation accuracy; (3) the variation in soil temperature did not have a distinct influence on spectra quality, and the estimation accuracies of models developed based on soil samples at 20–50 °C were all acceptable. Moreover, a 30-min warm-up of the spectrometer and contact probe was found to be effective. We carried out a complete and detailed control experiment process, the results of which offer a guide for optimizing the process of laboratory-based soil proximal spectral measurement to enhance spectra quality and corresponding estimation accuracy. Furthermore, we present theoretical support for the development of the spectral measurement protocol. We also present optional guidance with relatively lower accuracy but effective results, which are save time and are low cost for future spectral measurement projects.