An Assessment of Drought Stress in Tea Estates Using Optical and Thermal Remote Sensing

Abstract

Drought is one of the detrimental climatic factors that affects the productivity and quality of tea by limiting the growth and development of the plants. The aim of this research was to determine drought stress in tea estates using a remote sensing technique with the standardized precipitation index (SPI). Landsat 8 OLI/TIRS images were processed to measure the land surface temperature (LST) and soil moisture index (SMI). Maps for the normalized difference moisture index (NDMI), normalized difference vegetation index (NDVI), and leaf area index (LAI), as well as yield maps, were developed from Sentinel-2 satellite images. The drought frequency was calculated from the classification of droughts utilizing the SPI. The results of this study show that the drought frequency for the Sylhet station was 38.46% for near-normal, 35.90% for normal, and 25.64% for moderately dry months. In contrast, the Sreemangal station demonstrated frequencies of 28.21%, 41.02%, and 30.77% for near-normal, normal, and moderately dry months, respectively. The correlation coefficients between the SMI and NDMI were 0.84, 0.77, and 0.79 for the drought periods of 2018–2019, 2019–2020 and 2020–2021, respectively, indicating a strong relationship between soil and plant canopy moisture. The results of yield prediction with respect to drought stress in tea estates demonstrate that 61%, 60%, and 60% of estates in the study area had lower yields than the actual yield during the drought period, which accounted for 7.72%, 11.92%, and 12.52% yield losses in 2018, 2019, and 2020, respectively. This research suggests that satellite remote sensing with the SPI could be a valuable tool for land use planners, policy makers, and scientists to measure drought stress in tea estates.

Spatiotemporal Hybrid Random Forest Model for Tea Yield Prediction Using Satellite-Derived Variables

“…Additionally, this study explored the use of the RS satellite dataset and combined it with advanced machine algorithms to predict tea yield, as studies have previously explored with other crops [2]. In the future, further tea yield forecasting can be carried out incorporating deep-learning approaches [69][70][71] and including more features such as the elevation of the land [72] and vegetation indices, as described in [73]. The present study is important for developing countries such as Bangladesh, with land and climates suitable for producing cash crops such as tea.…”

Assessment of remote sensing-based indices for drought monitoring in the north-western region of Bangladesh

“…This technique was also used in previous studies in the same region [ 43 , 60 ] and the results were consistent with the present study. The validation of other indices has been confirmed in recent studies (LST [ 61 ], NDWI [ 62 ], NDMI [ 63 ] and SAVI [ 64 ]). The validation results of previous studies indicate that these remote sensing data can be used for hydrological approaches such as drought monitoring.…”

Oil Palm Yield Estimation Based on Vegetation and Humidity Indices Generated from Satellite Images and Machine Learning Techniques

“…The RF model was the best qualified for predicting oil palm cultivation of the AVROS species (MAE = 0.2669 t/ha, RMSE = 0.3452 t/ha, NSE = 0.8020, r 2 = 0.8214), while the NN model was the best when the plantation has multiple species (MAE = 0.2605, RMSE = 0.3437, NSE = 0.8131, r 2 = 0.8139). The Normalized Difference Moisture Index (NDMI) is the most relevant variable in the prediction of oil palm cultivation among a total of 12 VIs used, regardless of the type of species under study [37]. The estimation methods of this study can provide information on the identification variables (NDMI) to characterize palm oil yield.…”