Search Results (5)

Drought is one of the extreme climatic events that has a severe impact on crop production and food supply. Our main goal is to test the suitability of remote sensing-based indices to detect drought impacts on crop production from a global to regional scale. Moderate resolution imaging spectroradiometer (MODIS) based imagery, spanning from 2001 to 2017 was used for this task. This includes the normalized difference vegetation index (NDVI), land surface temperature (LST), and the evaporative stress index (ESI), which is based on the ratio of actual to potential evapotranspiration. These indices were used as indicators of drought-induced vegetation conditions for three main crops: maize, wheat, and soybean. The start and end of the growing season, as observed at 500 m resolution, were used to exclude the time steps that are outside of the growing season. Based on the three indicators, monthly standardized anomalies were estimated, which were used for both analyses of spatiotemporal patterns of drought and the relationship with yield anomalies. Anomalies in the ESI had higher correlations with maize and wheat yield anomalies than other indices, indicating that prolonged periods of low ESI during the growing season are highly correlated with reduced crop yields. All indices could identify past drought events, such as the drought in the USA in 2012, Eastern Africa in 2016–2017, and South Africa in 2015–2016. The results of this study highlight the potential of the use of moderate resolution remote sensing-based indicators combined with phenometrics for drought-induced crop impact monitoring. For several regions, droughts identified using the ESI and LST were more intense than the NDVI-based results. We showed that these indices are relevant for agricultural drought monitoring at both global and regional scales. They can be integrated into drought early warning systems, process-based crop models, as well as can be used for risk assessment and included in advanced decision-support frameworks. Full article

The Normalized Difference Vegetation Index (NDVI) is widely used to monitor vegetation phenology and productivity around the world. Over the last few decades, phenology monitoring at large scales has been possible due to the information and metrics derived from satellite sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) or the Project for On-Board Autonomy–Vegetation (PROBA-V). However, due to their temporal and spatial resolution, adequate ground comparison is lacking. In this paper, we analyze how NDVI products from MODIS (Aqua and Terra) and PROBA-V predict vegetation phenology when compared with near-surface observations. We conduct this comparison at four tropical dry forests (TDFs) in the Americas. We undertake this study by comparing the following: (i) Dissimilarities of the standardized NDVI (NDVI_S) using dynamic time warping, (ii) the differences of daily NDVI_S between seasons and ENSO months using generalized linear models, and (iii) phenometrics derived from NDVI time series. Overall, our results suggest that NDVI_S from satellite observations present DTW distances (dissimilarities) between 2.98 and 46.57 (18.91 ± 12.31) when compared with near-surface observations. Furthermore, NDVI_S comparisons reveal that overall differences between satellite and near-surface observations are close to zero, but this tends to differ between seasons or when El Nino Southern Oscillation (ENSO) is present. Phenometrics comparisons show that metrics derived from satellite observations such as green-up, maturity, and start and end of the wet season strongly correlate with those from near-surface observations. In contrast, phenometrics that describe the day of the highest or lowest NDVI tend to be inconsistent with those from near-surface observations. All findings were observed independently of the NDVI source. Our results suggest that satellite-based NDVI products tend to be inconsistent descriptors of vegetation events on tropical deciduous forests in comparison with near-surface observations. These results reinforce the idea that satellite-based NDVI products should be used and interpreted with great caution and only in ecosystems with well-established knowledge of their vegetation phenology. Full article

Monitoring phenological changes of crops through remote sensing methods is becoming a new perspective in assessing heavy metal contamination in agricultural farmlands. This paper proposes a method that combines the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) to detect heavy metal stress-induced variations in satellite-derived rice phenology. First, we applied the enhanced spatial and temporal adaptive reflectance fusion model to obtain the NDVI and NDWI time series for the NDVI–NDWI phase–space construction. Then, six specific rice phenometrics were derived from the NDVI and the phase–space, respectively. Last, we introduced a relative phenophase index (RPI), which characterizes the relative change of the phenometrics to identify the rice paddies under heavy metal stress. The results indicated that satellite-derived rice phenometrics are generally influenced by human and natural factors (e.g., transplanting date, air temperature, and solar radiation), while the RPI showed weak correlations with all of these variables. In the determination of heavy metal stress, the NDVI- and phase–space-based RPIs of unstressed rice both show significantly (p < 0.001) higher values than those of stressed rice, while the phase–space-based RPI shows more apparent statistical difference between the stressed and unstressed rice compared to the NDVI-based one. Our work proved the capability of the phase–space-based method as well as the RPI in the discrimination of regional heavy metal pollution in rice fields. Full article

Planting and harvesting times drive cropland phenology. There are few datasets that derive explicit phenological metrics, and these datasets use the visible to near infrared (VNIR) spectrum. Many different methods have been used to derive phenometrics such as Start of Season (SOS) and End of Season (EOS), leading to differing results. This discrepancy is partly due to spatial and temporal compositing of the VNIR satellite data to minimize data gaps resulting from cloud cover, atmospheric aerosols, and solar illumination constraints. Phenometrics derived from the downward Convex Quadratic model (CxQ) include Peak Height (PH) and Thermal Time to Peak (TTP), which are more consistent than SOS and EOS because they are minimally affected by snow and frost and other non-vegetation related issues. Here, we have determined PH using the vegetation optical depth (VOD) in three microwave frequencies (6.925, 10.65 and 18.7 GHz) and accumulated growing degree-days derived from AMSR-E (Advanced Microwave Scanning Radiometer on EOS) data at a spatial resolution of 25 km. We focus on 50 AMSR-E cropland pixels in the major grain production areas of Northern Eurasia (Ukraine, southwestern Russia, and northern Kazakhstan) for 2003–2010. We compared the land surface phenologies of AMSR-E VOD and MODIS NDVI data. VOD time series tracked cropland seasonal dynamics similar to that recorded by the NDVI. The coefficients of determination for the CxQ model fit of the NDVI data were high for all sites (0.78 < R² < 0.99). The 10.65 GHz VOD (VOD_1065GHz) achieved the best linear regression fit (R² = 0.84) with lowest standard error (SEE = 0.128); it is therefore recommended for microwave VOD studies of cropland land surface phenology. Based on an Analysis of Covariance (ANCOVA) analysis, the slopes from the linear regression fit were not significantly different by microwave frequency, whereas the intercepts were significantly different, given the different magnitudes of the VODs. PHs for NDVI and VOD were highly correlated. Despite their strong correspondence, there was generally a lag of AMSR-E PH VOD_10.65GHz by about two weeks compared to MODIS peak greenness. To evaluate the utility of the PH determination based on maximum value, we correlated the CxQ derived and maximum value determined PHs of NDVI and found that they were highly correlated with R² of 0.87, but with a one-week bias. Considering the one-week bias between the two methods, we find that PH of VOD_10.65GHz lags PH of NDVI by three weeks. We conclude, therefore, that maximum-value based PH of VOD can be a complementary phenometric for the CxQ model derived PH NDVI, especially in cloud and aerosol obscured regions of the world. Full article

The increasing spread and abundance of an invasive perennial grass, buffelgrass (Pennisetum ciliare), represents a critical threat to the native vegetation communities of the Sonoran desert in southern Arizona, USA, where buffelgrass eradication is a high priority for resource managers. Herbicidal treatment of buffelgrass is most effective when the vegetation is actively growing, but the remoteness of infestations and the erratic timing and length of the species’ growth periods confound effective treatment. The goal of our research is to promote buffelgrass management by using remote sensing data to detect where the invasive plants are located and when they are photosynthetically active. We integrated citizen scientist observations of buffelgrass phenology in the Tucson, Arizona area with PRISM precipitation data, eight-day composites of 250-m Moderate-resolution Imaging Spectroradiometer (MODIS) satellite imagery, and aerially-mapped polygons of buffelgrass presence to understand dynamics and relationships between precipitation and the timing and amount of buffelgrass greenness from 2011 to 2013. Our results show that buffelgrass responds quickly to antecedent rainfall: in pixels containing buffelgrass, higher correlations (R² > 0.5) typically occur after two cumulative eight-day periods of rain, whereas in pixels dominated by native vegetation, four prior 8-day periods are required to reach that threshold. Using the new suite of phenometrics introduced here—Climate Landscape Response metrics—we accurately predicted the location of 49% to 55% of buffelgrass patches in Saguaro National Park. These metrics and the suggested guidelines for their use can be employed by resource managers to treat buffelgrass during optimal time periods. Full article