Air temperature (Ta) is a key input in a wide range of agroclimatic applications. Moderate Resolution Imaging Spectroradiometer (MODIS) Ts (Land Surface Temperature (LST)) products are widely used to estimate daily Ta. However, only daytime LST (Ts-day) or nighttime LST (Ts-night) data have been used to estimate Tmax/Tmin (daily maximum or minimum air temperature), respectively. The relationship between Tmax and Ts-night, and the one between Tmin and Ts-day has not been studied. In this study, both the ability of Ts-night data to estimate Tmax and the ability of Ts-day data to estimate Tmin were tested and studied in the Corn Belt during the growing season (May–September) from 2008 to 2012, using MODIS daily LST products from both Terra and Aqua. The results show that using Ts-night for estimating Tmax could result in a higher accuracy than using Ts-day for a similar estimate. Combining Ts-day and Ts-night, the estimation of Tmax was improved by 0.19–1.85, 0.37–1.12 and 0.26–0.93 °C for crops, deciduous forest and developed areas, respectively, when compared with using only Ts-day or Ts-night data. The main factors influencing the Ta estimation errors spatially and temporally were analyzed and discussed, such as satellite overpassing time, air masses, irrigation, etc.

Soil moisture mapping at a regional scale is commonplace since these data are required in many applications, such as hydrological and agricultural analyses. The use of remotely sensed data for the estimation of deep soil moisture at a regional scale has received far less emphasis. The objective of this study was to map the 500-m, 8-day average and daily soil moisture at different soil depths in Oklahoma from remotely sensed and ground-measured data using the random forest (RF) method, which is one of the machine-learning approaches. In order to investigate the estimation accuracy of the RF method at both a spatial and a temporal scale, two independent soil moisture estimation experiments were conducted using data from 2010 to 2014: a year-to-year experiment (with a root mean square error (RMSE) ranging from 0.038 to 0.050 m³/m³) and a station-to-station experiment (with an RMSE ranging from 0.044 to 0.057 m³/m³). Then, the data requirements, importance factors, and spatial and temporal variations in estimation accuracy were discussed based on the results using the training data selected by iterated random sampling. The highly accurate estimations of both the surface and the deep soil moisture for the study area reveal the potential of RF methods when mapping soil moisture at a regional scale, especially when considering the high heterogeneity of land-cover types and topography in the study area.

Accurate phenology information detection is the basis for other remote-sensing based agriculture applications. So far, there have been a lot of phenology estimation models based on remote-sensing data, but little attention was paid to microscopic mechanism of crops and the environmental factors. The main purpose of this chapter is to apply a new phenology detection model, which combined physical mechanism-based crop models with remote-sensing data to detect the critical phenological stages of corn in Northeast China (Jilin and Liaoning Provinces). Compared to the phenology observations from the agriculture meteorological stations, the corn phenology estimation accuracy in Northeast China using only MODIS data is much lower than that in the US field sites. The main reason might be the small size of single piece of cropland in northeastern China, which led to the mixed MODIS pixels. Accordingly, Landsat and MODIS data fusion methods were applied to get time-series images with Landsat-like spatial resolution and MODIS-like temporal resolution, and quantitative and qualitative validation was conducted to evaluate and verify the accuracy of the data fusion. The results show that data fusion of Landsat and MODIS improved the spatial resolution and decreased the influence of mixed pixels.