The worst forest fire in South Korea occurred in April 2000 on the eastern coast. Forest recovery works were conducted until 2005, and the forest has been monitored since the fire. Remote sensing techniques have been used to detect the burned areas and to evaluate the recovery-time point of the post-fire processes during the past 18 years. We used three indices, Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), and Gross Primary Production (GPP), to temporally monitor a burned area in terms of its moisture condition, vegetation biomass, and photosynthetic activity, respectively. The change of those three indices by forest recovery processes was relatively analyzed using an unburned reference area. The selected unburned area had similar characteristics to the burned area prior to the forest fire. The temporal patterns of NBR and NDVI, not only showed the forest recovery process as a result of forest management, but also statistically distinguished the recovery periods at the regions of low, moderate, and high fire severity. The NBR_2.1 for all areas, calculated using 2.1 μm wavelengths, reached the unburned state in 2008. The NDVI for areas with low and moderate fire severity levels became significantly equal to the unburned state in 2009 (p > 0.05), but areas with high severity levels did not reach the unburned state until 2017. This indicated that the surface and vegetation moisture conditions recovered to the unburned state about 8 years after the fire event, while vegetation biomass and health required a longer time to recover, particularly for high severity regions. In the case of GPP, it rapidly recovered after about 3 years. Then, the steady increase in GPP surpassed the GPP of the reference area in 2015 because of the rapid growth and high photosynthetic activity of young forests. Therefore, the concluding scientific message is that, because the recovery-time point for each component of the forest ecosystem is different, using only one satellite-based indicator will not be suitable to understand the post-fire recovery process. NBR, NDVI, and GPP can be combined. Further studies will require more approaches using various terms of indices.

Spectral reflectance-based vegetation indices have sensitive characteristics to crop growth and health conditions. The performance of each vegetation index to a certain condition is different and needs to be interpreted, correspondingly. This study aimed to assess the most suitable vegetation index to identify the crop response against elevated air temperatures, heat stress, and herbicide damage. The spectral reflectance, yield components, and growth parameters such as plant height, leaf area index (LAI), and above-ground dry matter of paddy rice, which was cultivated in a temperature gradient field chamber to simulate global warming conditions, were observed from 2016 to 2018. The relationships between the vegetation indices and the crop parameters were assessed considering stress conditions. The normalized difference vegetation index (NDVI) represented the changes in plant height (R-square = 0.93) and the LAI (R-square = 0.901) before the heading stage. Furthermore, the NDVI and the cumulative growing degree days had a Sigmoid curve and an R-square value of 0.937 under the normal growth case, but it decreased significantly in the herbicide damage case. This characteristic was useful for detecting the damaged crop growth condition. Additionally, to estimate the grain yield of paddy rice, the medium resolution imaging spectrometer (MERIS) terrestrial chlorophyll index was better: R-square = 0.912; root mean square error = 95.69 g/m². Photochemical reflectance index was sensitive to physiological stress caused by the heatwave, and it decreased in response to extremely high air temperatures. These results will contribute towards determining vegetation indices under stress conditions and how to effectively utilize them.

Remote sensing techniques using visible and near-infrared wavelengths are useful for monitoring terrestrial vegetation. The normalized difference vegetation index (NDVI) is a widely used proxy of vegetation conditions, and it has been measured at various footprint sizes using satellite, unmanned aerial vehicle (UAV), and ground-installed sensors. The goal of this study was to analyze the spatial characteristics of NDVI data by comparing the values obtained at different footprint sizes. In particular, the NDVI was evaluated in garlic and onion fields that featured ridges and furrows. The evaluation was performed using data from a leaf spectrometer, field spectrometers, ground-installed spectral reflectance sensors, a multispectral camera onboard a UAV, and Sentinel-2 satellites. The correlation coefficients between NDVIs evaluated from the various sensors (excluding the satellite-mounted sensors) ranged from 0.628 to 0.944. The UAV-based NDVI (NDVI_UAV) exhibited the lowest root mean square error (RMSE = 0.088) when compared with field spectrometer data. On the other hand, the satellite-based NDVI data (NDVI_Sentinel-2) were poorly correlated with those obtained from the other sensors as a result of the footprint mismatch. However, by upscaling the NDVI_UAV data to the pixel size of Sentinel-2, the comparison was improved, and the following statistics were obtained: correlation coefficient: 0.504–0.785; absolute bias: 0.048–0.078; RMSE: 0.063–0.094. According to the aforementioned results, ground-based NDVI data can be used to validate NDVI_UAV data without further processing and NDVI_UAV data can be used to validate NDVI_Sentinel-2 data after upscaling to the Sentinel-2 pixel size. Overall, the results presented in this study may be helpful to understand and integrate NDVI data at different spatial scales.

Satellite-based remote sensing techniques have been widely used to monitor droughts spanning large areas. Various agricultural drought indices have been developed to assess the intensity of agricultural drought and to detect damaged crop areas. However, to better understand the responses of agricultural drought to meteorological drought, agricultural management practices should be taken into consideration. This study aims to evaluate the responses to drought under different forms of agricultural management for the extreme drought that occurred on the Korean Peninsula in 2014 and 2015. The 3-month standardized precipitation index (SPI3) and the 3-month vegetation health index (VHI3) were selected as a meteorological drought index and an agricultural drought index, respectively. VHI3, which comprises the 3-month temperature condition index (TCI3) and the 3-month vegetation condition index (VCI3), differed significantly in the study area during the extreme drought. VCI3 had a different response to the lack of precipitation in South and North Korea because it was affected by irrigation. However, the time series of TCI3 were similar in South and North Korea. These results meant that each drought index has different characteristics and should be utilized with caution. Our results are expected to help comprehend the responses of the agricultural drought index on meteorological drought depending on agricultural management.

Rice is one of the major food crops, particularly in Asia. However, it is vulnerable to high temperature and has high yield fluctuations. Monitoring crop growth and physiological responses to high temperatures can help us better understand the agricultural impacts of global warming. The aim of this study is to monitor growth, development, and physiological responses to high temperature conditions on paddy rice and to assess their combined effects on yield. In this study, changes to growth, maturity, and senescence in paddy rice throughout the growing season were identified under elevated air temperature conditions created by a temperature gradient field chamber (TGFC). That facility provides a gradient from the ambient air temperature (AT) to 3 °C above AT (AT + 3 °C). To represent crop physiology and productivity, we measured the plant height, chlorophyll, normalized difference vegetation index (NDVI), and maximum photosynthetic rate (A_max) to assess growth and physiological processes, and heat stress effects on four yield measurements were assessed using the heating degree day index. Rice height increased more rapidly in the AT + 3 °C treatment from the early growth stage to heading, while SPAD and NDVI decreased more rapidly at AT after heading. The A_max of AT and AT + 3 °C was not significantly different in the tillering stage. However, it was higher at AT in the booting stage but higher at AT + 3 °C in the grain filling stage. These results indicate that paddy rice was not affected by heat stress at the tillering stage, but a cumulative effect emerged by the booting stage. Further, photosynthetic capacity was maintained much later into the grain filling stage at AT + 3 °C. These results will be useful for understanding the growth and physiological responses of paddy rice to global warming.

Extremely high air temperature at the heading stage of paddy rice causes a yield reduction due to the increasing spikelet sterility. Quantifying the damage to crops caused by high temperatures can lead to more accurate estimates of crop yields. The remote sensing technique evaluates crop conditions indirectly but provides information related to crop physiology, growth, and yield. In this study, we aim to assess the crop damage caused by heat stress in paddy rice examined under elevated air temperatures in a temperature gradient field chamber from 2016 to 2019, using remote-sensed vegetation indices. A leaf-spectrometer, field-spectrometers, and a multi-spectral camera were used to monitor the conditions of paddy rice. Although, in the leaf- and canopy-scales, the values of normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI) decreased after the heading of rice under normal conditions, the decreasing sensitivity of NDVI and PRI was different depending on the degree of physiological heat stress by high temperature conditions. The NDVI after the heading under extremely high air temperature was not dropped and remained the value before heading. The PRI decreased at all air temperature conditions after the heading; the PRI of the plot exposed to the elevated air temperature was higher than that under ambient air temperature. Further, the relative change in NDVI and PRI after the heading exhibited a strong relationship with the ripening ratio of paddy rice, which is the variable related to crop yield. These remote-sensing results aid in evaluating the crop damage caused by heat stress using vegetation indices.