Satellite-Based Observations Reveal Effects of Weather Variation on Rice Phenology

Abstract

Obtaining detailed data on the spatio-temporal variation in crop phenology is critical to increasing our understanding of agro-ecosystem function, such as their response to weather variation and climate change. It is challenging to collect such data over large areas through field observations. The use of satellite remote sensing data has made phenology data collection easier, although the quality and the utility of such data to understand agro-ecosystem function have not been widely studied. Here, we evaluated satellite data-based estimates of rice phenological stages in California, USA by comparing them with survey data and with predictions by a temperature-driven phenology model. We then used the satellite data-based estimates to quantify the crop phenological response to changes in weather. We used time-series of MODIS satellite data and PhenoRice, a rule-based rice phenology detection algorithm, to determine annual planting, heading and harvest dates of paddy rice in California between 2002 and 2017. At the state level, our satellite-based estimates of rice phenology were very similar to the official survey data, particularly for planting and harvest dates (RMSE = 3.8–4.0 days). Satellite based observations were also similar to predictions by the DD10 temperature-driven phenology model. We analyzed how the timing of these phenological stages varied with concurrent temperature and precipitation over this 16-year time period. We found that planting was earlier in warm springs (−1.4 days °C−1 for mean temperature between mid-April and mid-May) and later in wet years (5.3 days 100 mm-1 for total precipitation from March to April). Higher mean temperature during the pre-heading period of the growing season advanced heading by 2.9 days °C−1 and shortened duration from planting to heading by 1.9 days °C−1. The entire growing season was reduced by 3.2 days °C−1 because of the increased temperature during the rice season. Our findings confirm that satellite data can be an effective way to estimate variations in rice phenology and can provide critical information that can be used to improve understanding of agricultural responses to weather variation.

Paddy Rice Mapping in Thailand Using Time-Series Sentinel-1 Data and Deep Learning Model

“…The SVM experiments were carried out with LibSVM 3.2.4 [68]. The Gaussian Radial Basis Function (RBF) was chosen as the kernel function, and the penalty parameter C and Gaussian kernel parameter g were selected by hyperparameter optimization strategy in the range of [2][3][4][5][6][7][8]28]. To increase the search efficiency and avoid overfitting, several independent training groups and a validation group were randomly extracted from frame 99-16 for cross-validation, where each dataset contained 20,000 paddy rice and 20,000 non-paddy rice samples, and each sample was characterized with the RGB values and corresponding label.…”

“…As the third most widely cultivated grains (following wheat and maize), the cultivation activities of paddy rice greatly influence not only the global rice marketing and the rice-reliant populations but also the hydrologic cycle and the ecological balance [3][4][5]. The land-use-land-cover (LULC) changes due to fast urban expansions, policy adjustments, and climate changes increase the uncertainty of paddy rice growth [6][7][8]. In addition, natural hazards, such as typhoons, floods, droughts, and pests have direct impacts on the rice yields, which further affect food supplies and greenhouse gas emissions [9][10][11][12].…”

Monitoring Hybrid Rice Phenology at Initial Heading Stage Based on Low-Altitude Remote Sensing Data

“…Rice is a crop that is sensitive to high temperature [79] and water [80]. The continuous high air temperature or additional rain had great influence on the growth process of rice, IHS DAS of rice may be advanced or postponed [81]. Moreover, strong sunlight can burn rice leaves and change the rice canopy structure.…”

Impact of High-Cadence Earth Observation in Maize Crop Phenology Classification

“…Understanding changes in crop phenology and their response to climatic conditions is critical for production-beneficial management practices [2]. While the literature describes several initiatives to address crop phenology via earth observations (remote sensing) [3][4][5][6], or mathematical models supported on weather information [7,8], the metrics reported from those studies are not easily translated into effective agronomic management strategies. For instance, Land Surface Phenology (LSP) determines the change in green vegetation condition by identifying changes in the vegetation seasonal pattern using remote sensing technologies [3], but these changes do not directly reflect the true phenological stage of a distinctive crop [9].…”