Using MODIS LAI Data to Monitor Spatio-Temporal Changes of Winter Wheat Phenology in Response to Climate Warming

Spatio-temporal change of winter wheat yield and its quantitative responses to compound frost-dry events - An example of the Huang-Huai-Hai Plain of China from 2001 to 2020

Extreme climate events such as frost and drought have great influence on wheat growth and yield. Understanding the effects of frost, drought and compound frost-dry events on wheat growth and yield is of great significance for ensuring national food security. In this study, wheat yield prediction model (SCYMvp) was developed by combining crop growth model (CGM), satellite images and meteorological variables. Wheat yield maps in the Huang-Huai-Hai Plain (HHHP) during 2001-2020 were generated using SCYMvp model. Meanwhile, accumulative frost days (AFD), accumulative dry days (ADD) and accumulative frost-dry days (AFDD) in different growth periods of wheat were calculated, and the effects of frost and drought on wheat yield were quantified by the first difference method and linear mixed model. The results showed that wheat yield increased significantly, while the rising trend was obvious at more than half of the regions. Extreme climate events (ECEs) showed a relatively stable change trend, although the change trend was significant only in a few areas. Compared with frost and drought in the early growth period, ECEs in the middle growth period (spring ECEs) had more negative effects on wheat growth and yield. Wheat yield was negatively correlated with spring ECEs, and yield loss was between 4.6 and 49.8 kg/ha for each 1 d increase of spring ECEs. The effects of spring ECEs on wheat yield were ranked as AFDD > AFD > ADD. The negative effect of ADD on wheat yield in the late growth period was higher than that in the other periods. The negative effects of spring ECEs on yield in southern regions were higher than those in northern regions. Overall, due to the adverse effects of frost and drought on wheat yield in the middle and late growth periods, the mean annual yield loss was 6.4 %, among which spring AFD caused the greatest loss to wheat yield (3.1 %). The results have important guiding significance for formulating climate adaptation management strategies.

Validation of leaf area index measurement system based on wireless sensor network

Accurate measurement of leaf area index (LAI) is important for agricultural analysis such as the estimation of crop yield, which makes its measurement work important. There are mainly two ways to obtain LAI: ground station measurement and remote sensing satellite monitoring. Recently, reliable progress has been made in long-term automatic LAI observation using wireless sensor network (WSN) technology under certain conditions. We developed and designed an LAI measurement system (LAIS) based on a wireless sensor network to select and improve the appropriate algorithm according to the image collected by the sensor, to get a more realistic leaf area index. The corn LAI was continuously observed from May 30 to July 16, 2015. Research on hardware has been published, this paper focuses on improved system algorithm and data verification. By improving the finite length average algorithm, the data validation results are as follows: (1) The slope of the fitting line between LAIS measurement data and the real value is 0.944, and the root means square error (RMSE) is 0.264 (absolute error ~ 0–0.6), which has high consistency with the real value. (2) The measurement error of LAIS is less than LAI2000, although the result of our measurement method will be higher than the actual value, it is due to the influence of weeds on the ground. (3) LAIS data can be used to support the retrieval of remote sensing products. We find a suitable application situation of our LAIS system data, and get our application value as ground monitoring data by the verification with remote sensing product data, which supports its application and promotion in similar research in the future.

Drought Monitoring over Yellow River Basin from 2003–2019 Using Reconstructed MODIS Land Surface Temperature in Google Earth Engine

Drought is one of the most complex and least-understood environmental disasters that can trigger environmental, societal, and economic problems. To accurately assess the drought conditions in the Yellow River Basin, this study reconstructed the Land Surface Temperature (LST) using the Annual Temperature Cycle (ATC) model and the Normalized Difference Vegetation Index (NDVI). The Temperature Condition Index (TCI), Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Temperature-Vegetation Drought Index (TVDI), which are four typical remote sensing drought indices, were calculated. Then, the air temperature, precipitation, and soil moisture data were used to evaluate the applicability of each drought index to different land types. Finally, this study characterized the spatial and temporal patterns of drought in the Yellow River Basin from 2003 to 2019. The results show that: (1) Using the LST reconstructed by the ATC model to calculate the drought index can effectively improve the accuracy of drought monitoring. In most areas, the reconstructed TCI, VHI, and TVDI are more reliable for monitoring drought conditions than the unreconstructed VCI. (2) The four drought indices (TCI, VCI, VH, TVDI) represent the same temporal and spatial patterns throughout the study area. However, in some small areas, the temporal and spatial patterns represented by different drought indices are different. (3) In the Yellow River Basin, the drought level is highest in the northwest and lowest in the southwest and southeast. The dry conditions in the Yellow River Basin were stable from 2003 to 2019. The results in this paper provide a basis for better understanding and evaluating the drought conditions in the Yellow River Basin and can guide water resources management, agricultural production, and ecological protection of this area.

Quantification of Changes in Rice Production for 2003–2019 with MODIS LAI Data in Pursat Province, Cambodia

Rice is not merely a staple food but an important source of income in Cambodia. Rapid socioeconomic development in the country affects farmers’ management practices, and rice production has increased almost three-fold over two decades. However, detailed information about the recent changes in rice production is quite limited and mainly obtained from interviews and statistical data. Here, we analyzed MODIS LAI data (MCD152H) from 2003 to 2019 to quantify rice production changes in Pursat Province, one of the great rice-producing areas in Cambodia. Although the LAI showed large variations, the data clearly indicate that a major shift occurred in approximately 2010 after applying smoothing methods (i.e., hierarchical clustering and the moving average). This finding is consistent with the results of the interviews with the farmers, which indicate that earlier-maturing cultivars had been adopted. Geographical variations in the LAI pattern were illustrated at points analyzed along a transverse line from the mountainside to the lakeside. Furthermore, areas of dry season cropping were detected by the difference in monthly averaged MODIS LAI data between January and April, which was defined as the dry season rice index (DSRI) in this study. Consequently, three different types of dry season cropping areas were recognized by nonhierarchical clustering of the annual LAI transition. One of the cropping types involved an irrigation-water-receiving area supported by canal construction. The analysis of the peak LAI in the wet and dry seasons suggested that the increase in rice production was different among cropping types and that the stagnation of the improvements and the limitation of water resources are anticipated. This study provides valuable information about differences and changes in rice cropping to construct sustainable and further-improved rice production strategies.

Satellite-based application in drought disaster assessment using terra MOD13Q1 data across free state province, South Africa

Terra MOD13Q1 satellite data were used to assess drought disaster events and its spatiotemporal patterns over the Free State Province, South Africa between 2001 and 2019. Moderate Resolution Imaging Spectroradiometer (MODIS) products and climate data downloaded from Application for Extracting and Exploring Analysis Ready Samples" (AppEEARS) and NASA: Prediction of Worldwide Energy Resource databases, respectively were used in the study area. After acquiring MODIS data with the area of interest extracted using field sample and the layers of interest: using Enhanced Vegetation Index (EVI) and pixel reliability and defining the output as GeoTIFF with geographic projection, R programming was used for the analysis. This study also evaluated Standardized Precipitation Index (SPI) to further identify meteorological drought, computed at various time scales utilising information acquired from the Global Drought Observatory database. The results show and identify the years that are water-stressed in the study area, which indicated that low vegetation abundance and high temperature in the Free State Province occurred in 2000, 2008, and 2009. The result also shows that the summer season over large parts of the study region is characterized by moderate to extreme drought while winter seasons have light drought conditions during the same time. Seasonal and inter-annual comparisons of drought events outlined in the Vegetation Condition Index (VCI) provided a useful tool for analysing the temporal and spatial evolution of regional drought and for estimating potential environmental impacts. Drought events occurrence in the study area is less frequent and milder in winter months while summer droughts of different years were more severe. The results of this study can also be used as a tool for monitoring droughts and support for decision making in the evaluation and management of regional drought, for disaster preparedness.

A Method for Monitoring and Forecasting the Heading and Flowering Dates of Winter Wheat Combining Satellite-Derived Green-up Dates and Accumulated Temperature

Heading and flowering are two key phenological stages in the growth process of winter wheat. It is of great significance for agricultural management and scientific research to accurately monitor and forecast the heading and flowering dates of winter wheat. However, the monitoring accuracy of existing methods based on remote sensing needs to be improved, and these methods cannot realize forecasting in advance. This study proposed an accumulated temperature method (ATM) for monitoring and forecasting the heading and flowering dates of winter wheat from the perspective of thermal requirements for crop growth. The ATM method consists of three key procedures: (1) extracting the green-up date of winter wheat as the starting point of temperature accumulation with the dynamic threshold method from remotely sensed vegetation index (VI) time-series data, (2) calculating the accumulated temperature and determining the thermal requirements from the green-up date to the heading date or the flowering date based on phenology observation samples, and (3) combining the satellite-derived green-up date, daily temperature data, and thermal requirements to monitor and forecast the heading date and flowering date of winter wheat. When applying the ATM method to winter wheat in the North China Plain during 2017–2019, the root mean square error (RMSE) for the estimated heading date was between 4.76 and 6.13 d and the RMSE for the estimated flowering date was between 5.30 and 6.41 d. By contrast, the RMSE for the heading and flowering dates estimated by the widely used maximum vegetation index method was approximately 10 d. Furthermore, the forecasting accuracy of the ATM method was also high, and the RMSE was approximately 6 d. In summary, the proposed ATM method can be used to accurately monitor and forecast the heading and flowering dates of winter wheat in large spatial scales and it performs better than the existing maximum vegetation index method.

Estimating Wheat Grain Yield Using Sentinel-2 Imagery and Exploring Topographic Features and Rainfall Effects on Wheat Performance in Navarre, Spain

Reliable methods for estimating wheat grain yield before harvest could help improve farm management and, if applied on a regional level, also help identify spatial factors that influence yield. Regional grain yield can be estimated using conventional methods, but the typical process is complex and labor-intensive. Here we describe the development of a streamlined approach using publicly accessible agricultural data, field-level yield, and remote sensing data from Sentinel-2 satellite to estimate regional wheat grain yield. We validated our method on wheat croplands in Navarre in northern Spain, which features heterogeneous topography and rainfall. First, this study developed stepwise multilinear equations to estimate grain yield based on various vegetation indices, which were measured at various phenological stages in order to determine the optimal timings. Second, the most suitable model was used to estimate grain yield in wheat parcels mapped from Sentinel-2 satellite images. We used a supervised pixel-based random forest classification and the estimates were compared to government-published post-harvest yield statistics. When tested, the model achieved an R2 of 0.83 in predicting grain yield at field level. The wheat parcels were mapped with an accuracy close to 86% for both overall accuracy and compared to official statistics. Third, the validated model was used to explore potential relationships of the mapped per-parcel grain yield estimation with topographic features and rainfall by using geographically weighted regressions. Topographic features and rainfall together accounted for an average for 11 to 20% of the observed spatial variation in grain yield in Navarre. These results highlight the ability of our method for estimating wheat grain yield before harvest and determining spatial factors that influence yield at the regional scale.