植被病虫害遥感
Theme Keywords： spectral indexremote sensingsoybeanseverity quantificationrice sheath blightremote sensing informationphenologyleaf structureland surface temperature
- The Paper
- Abstract：Soybean is the world’s most important legume crop that serves as a major source of high-protein food, the primary ingredient for livestock feed, and an essential source of edible oil. They play a crucial role in the world’s food production, with a global annual production of approximately 370 million metric tons. China is one of the major soybean-producing countries globally, with an annual production of approximately 17 million metric tons. However, China’s domestic soybean production is insufficient to meet production and living needs, and it is highly reliant on imports, accounting for more than 80% of its soybean consumption. Consequently, China’s food security faces considerable structural challenges. Remote sensing technology is a powerful tool for monitoring soybean cultivation and can provide basic data support for various countries to release signals of changes in agricultural product markets, strengthen the guidance of agricultural product markets, and formulate effective agricultural economic development strategies. The traditional method of estimating soybean planting area through agricultural surveys is usually time consuming, labor intensive, and subject to subjective factors, leading to inaccurate and imprecise results. By contrast, remote sensing technology utilizes satellite, aerial, or drone-based sensors to capture and analyze the electromagnetic radiation reflected or emitted by Earth’s surface, providing a more objective and efficient way of monitoring crop planting areas. While methods based on vegetation index time series and phenology are widely used for crop recognition including soybeans, the focus has been primarily on the impact of the vegetation index time series feature or phenological feature on soybean recognition, and research on the time series curve itself has been limited. Furthermore, analysis and research on the spectral characteristics of the key growth stages for soybeans are lacking, and no standard spectral time series curve for soybeans has been established to summarize their changing patterns. Additionally, mainstream crop recognition methods face difficulties in obtaining samples, especially in large-scale mapping, where the quality and quantity of samples are the main limiting factors.This study proposes a soybean recognition method based on the standard spectral time-series curve on the Google Earth Engine (GEE) cloud platform. By adding the climate weight factor, the standard spectral time-series curve of soybean can be accurately recognized.By using the features of the standard spectral time-series curve combined with the random forest classifier, a soybean distribution map in Heilongjiang Province in 2020 is extracted. The classification confusion matrix shows that the overall accuracy of soybean recognition is 86.95%, the user accuracy is 90.91%, the producer accuracy is 86.14%, and the F1-score is 0.8846. Compared with statistical area data, the area accuracy reaches 95.94%.The study focuses on analyzing the difference between the spectral time-series curves of soybean and corn and the impact of meteorological factors on the curves and establishes a method for mapping the standard spectral time-series curve information to samples, thereby solving the problem of insufficient samples for mapping a large area of soybean. Furthermore, this study designs experiments to verify the robustness of this soybean recognition method based on the standard spectral time-series curve in terms of time scale and disaster situations. These results provide scientific evidence and technical support for the monitoring of soybean growth, disaster evaluation, and the formulation of international agricultural product trade strategies.Keywords：remote sensing;soybean;Temporal spectrum;Standard curve;phenology;Meteorology1815|5600|2
<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-11-27 Monitoring chilling damage of corn in the Northeast China based on MODIS LST and meteorological data
Abstract：Corn has become one of the most important crops in China at present. The study area in this work, which includes Jilin Province, Liaoning Province, Heilongjiang Province, and the four eastern cities of Inner Mongolia Autonomous Region (Hulun Buir, Tongliao, Chifeng, and Xing’an League), is the most important corn production area. The area is also located at the northern limit of corn planting area. Notably, the temporal and spatial distribution of chilling damage is highly important to increase yield and quality. This study aims to integrate MODIS and meteorological data for monitoring corn chilling damage in Northeast China. The algorithm was computed in two steps. In the first step, the remote sensing estimation model of air temperature were established. In the second step, the sterile-type chilling damage and delayed-type chilling damage on corn were monitored based on the full coverage daily mean air temperature and the corn chilling damage indicator. Satellite data, including LST, EVI, and quality control data derived from TERRA/AQUA -MODIS, and ground-based data, including daily mean air temperature and phenological data observed by 234 meteorological stations, from 2003 to 2015 were collected for data analysis, image processing, and mapping.The remote sensing estimation model of air temperature was established by multi-variated linear regression using the MODIS LST, EVI, and solar declination of cloud-free pixels as independent variables, and daily mean air temperature observed by meteorological stations was used as a dependent variable. The meteorological stations were divided into two parts according to the coordinates. Daily mean air temperature measured by two thirds of station (156) from 2003 to 2013 was used to establish the daily average temperature estimation model, and the remaining data including the observations of 78 meteorological stations from 2003 to 2013 and the observation data of all stations in 2014 and 2015 were used to validate the model. The MODIS EVI production is the composited production on the 16th day. The S-G filter with max was used to achieve daily EVI. The air temperature of cloud-free pixels was calculated with estimated models using TERRA and AQUA daytime and nighttime data, respectively. Then, the TERRA and AQUA daytime and nighttime derived daily mean air temperature data were merged based on the R2 and RMSE to increase spatial cloud-free data. The validation results show that the models using TERRA or AQUA night LST data as predictors outperform those using daytime LST as predictors. A framework was proposed for the air temperature data fusion. The data fusion framework is based on the fact that the MODIS TERRA and AQUA can provide daytime and nighttime LST and that the merge of these data can increase spatial coverage. The daily mean air temperature dataset covering the whole study area with a spatial resolution of 1 km from 2003 to 2015 was completed based on the retrieval models and the data fusion framework.This study provided a remote sensing monitoring method of sterile-type chilling damage and delayed-type chilling damage on corn. Low daily mean air temperature and its last days are the corn sterile-type chilling damage indicator. Corn sterile-type chilling damage was identified by integrating the corn chilling damage indicator and the daily mean air temperature dataset. The results showed corn sterile-type chilling damage in 2003, 2006, and 2012. These findings are consistent with the meteorological observation. This research can be used to monitor the process of corn sterile-type chilling damage, take abatement measures to mitigate corn sterile-type chilling damage, and reduce disaster losses. On the basis of the full coverage daily mean air temperature dataset, the accumulated temperature of ≥10 ℃ from 2003 to 2015 was calculated. The indicators of delayed-type chilling damage on corn also revealed that the study area suffered from widespread delayed-type chilling damage in 2003, 2005, 2006, 2009, and 2011. Compared with the observation of meteorological stations, the results of this research match the actual situation.Keywords：remote sensing;MODIS;land surface temperature;data fusion;chilling damage;corn1342|3570|3<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-11-27- Abstract：Remote sensing monitoring of crop diseases plays a crucial role in food security in terms of the precision management of chemical fungicides and the efficient assessment of crop losses. Spectroscopic detection of disease infection has been investigated for numerous crop diseases individually. However, it remains unclear how biochemical and spectral variations differ in response to divergent diseases given the distinct symptoms caused by different pathogens. This study aimed to determine the pathological mechanism and specificity of the spectral responses of two types of fungal diseases by comparing their specific spectral signatures and disease monitoring performance. The biotrophic Wheat Powdery Mildew (WPM) and the semi-biotrophic Rice Leaf Blast (RLB) diseases were used as examples for the comparison. With the reflectance measurements of infected leaves and radiative transfer modeling, a comparative analysis for these two diseases was conducted in terms of spectral responses, leaf biochemical and structural parameters. Additionally, we assessed the specificity of various disease-related spectral features, which were proposed in previous studies for the monitoring of WPM or RLB, by accuracy comparison in the detection of diseased leaves and the estimation of Leaf Lesion Proportion (LLP). The results showed significant differences in the intensity of spectral responses to the two diseases despite the similarity observed in the general trend in spectral variations. In addition, distinct variations appeared in the spectral shape at the green peak and near-infrared plateau between WPM and RLB. Moreover, the pigment variations in response to two infections were generally similar, whereas the response was more pronounced for RLB. Notably, the leaf water content and structural parameter displayed significant changes only in relation to the severity of RLB. In disease detection, the spectral features developed for WPM or RLB generated higher accuracy in detection of the target disease than the other disease. Wavelet features of WF3820 and WF5866 displayed the highest accuracy and specificity for WPM and RLB, respectively. Regarding the severity quantification, most spectral features exhibited higher sensitivity to the LLP of RLB than to that of WPM. Specifically, a variat of rice blast index (RIBIred) and the Photochemical Reflectance Index (PRI) demonstrated the highest accuracy and specificity in the LLP estimation of WPM and RLB, respectively. Among the WPM- or RLB-related spectral features, RIBIred showed the optimal monitoring performance and specificity in both disease detection and severity estimation (Overall accuracy=0.74, R2=0.58). Our findings provide solid evidence and new insights into disease-specific spectroscopic monitoring by associating spectral responses with pathogenesis of two types of fungal diseases. This study offers significant contributions to the understanding of disease monitoring mechanisms and the identification of multiple diseases with hyperspectral remote sensing.Keywords：remote sensing;Disease detection;severity quantification;PROSPECT;biochemical parameters;leaf structure;spectral index;continuous wavelet transform1203|1179|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-11-27
- Abstract：Crop diseases have a severe impact on food security, and the excessive use of pesticides in crop disease prevention and control is a common issue. The evaluation of disease habitat suitability can provide important information for disease forecasting and control. The occurrence of crop diseases is closely associated with factors, such as the growth status of host and environmental conditions, while disease habitat conditions vary considerably due to cultivation practices and microclimate variations in the field. At present, disease habitat monitoring and evaluation are generally coarse, mainly relying on meteorological information and lacking detailed descriptions of spatially heterogeneous factors, such as crop growth status and environmental conditions among fields. In this study, Rice Sheath Blight (RSB), a major disease widespread in rice cultivation, was selected as the research object; the disease surveys were conducted at a county level in 2018 and 2019. Multisource remote sensing data, including optical, microwave, and thermal infrared images, were used for monitoring the key disease habitat factors. Multitemporal Sentinel-2 optical images were utilized to extract the planting area of the host crop, which solved the problem of confusing the host with other vegetation in single phase images; the growth status of host was indicated by the tasseled cap products of Sentinel-2 optical images; the status of water layer in rice field was extracted by combining Sentinel-1 microwave images and Sentinel-2 optical images, the optical image of rice region was segmented by object-oriented analysis method to obtain the rice plot boundary to eliminate the noise of microwave image; and the MODIS land surface temperature products were utilized to reflect the evapotranspiration and respiration status of rice plants. On the basis of these remote sensing habitat features of the RSB and a spatial gridding analysis, the habitat suitability evaluation model was established using the partial least squares regression method.Validation results against the disease survey data showed that the remote sensing information can effectively characterize the disease habitat features. The R2 of the habitat suitability evaluation model was 0.60—0.65, and the RMSE was 0.72 and 0.56, respectively, and the output of the model was consistent with the actual spatial pattern of the disease. In addition, the hot and cold spots of the disease habitat suitability map were highly consistent with the actual pattern of disease occurrence in the region. Moreover, the rate of habitat suitability under each disease grade was analyzed, and the results further confirmed the rationality of the evaluation. Therefore, this study demonstrates the feasibility of utilizing multisource remote sensing data in evaluating the disease habitat suitability. The disease habitat evaluation map can be integrated into some disease epidemic models to develop spatiotemporal dynamic disease forecasting models at a regional scale, and multisource data, such as meteorological data, remote sensing data, and ground sensor networks, can be incorporated to establish a more comprehensive habitat suitability evaluation model, which is expected to be beneficial for large-scale disease control.Keywords：rice sheath blight;habitat;remote sensing information;spatial gridding;evaluation model1183|2645|1<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-11-27
